# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

# Code generated by the Google Gen AI SDK generator DO NOT EDIT.

import json
import logging
from typing import Any, AsyncIterator, Awaitable, Iterator, Optional, Union
from urllib.parse import urlencode

from . import _api_module
from . import _base_transformers as base_t
from . import _common
from . import _extra_utils
from . import _mcp_utils
from . import _transformers as t
from . import errors
from . import types
from ._api_client import BaseApiClient
from ._common import get_value_by_path as getv
from ._common import set_value_by_path as setv
from .pagers import AsyncPager, Pager

logger = logging.getLogger('google_genai.models')


def _PersonGeneration_to_mldev_enum_validate(enum_value: Any) -> None:
  if enum_value in set(['ALLOW_ALL']):
    raise ValueError(f'{enum_value} enum value is not supported in Gemini API.')


def _SafetyFilterLevel_to_mldev_enum_validate(enum_value: Any) -> None:
  if enum_value in set(['BLOCK_NONE']):
    raise ValueError(f'{enum_value} enum value is not supported in Gemini API.')


def _VideoGenerationReferenceType_to_mldev_enum_validate(
    enum_value: Any,
) -> None:
  if enum_value in set(['STYLE']):
    raise ValueError(f'{enum_value} enum value is not supported in Gemini API.')


def _AuthConfig_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['api_key']) is not None:
    setv(to_object, ['apiKey'], getv(from_object, ['api_key']))

  if getv(from_object, ['api_key_config']) is not None:
    raise ValueError('api_key_config parameter is not supported in Gemini API.')

  if getv(from_object, ['auth_type']) is not None:
    raise ValueError('auth_type parameter is not supported in Gemini API.')

  if getv(from_object, ['google_service_account_config']) is not None:
    raise ValueError(
        'google_service_account_config parameter is not supported in Gemini'
        ' API.'
    )

  if getv(from_object, ['http_basic_auth_config']) is not None:
    raise ValueError(
        'http_basic_auth_config parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['oauth_config']) is not None:
    raise ValueError('oauth_config parameter is not supported in Gemini API.')

  if getv(from_object, ['oidc_config']) is not None:
    raise ValueError('oidc_config parameter is not supported in Gemini API.')

  return to_object


def _Blob_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['data']) is not None:
    setv(to_object, ['data'], getv(from_object, ['data']))

  if getv(from_object, ['display_name']) is not None:
    raise ValueError('display_name parameter is not supported in Gemini API.')

  if getv(from_object, ['mime_type']) is not None:
    setv(to_object, ['mimeType'], getv(from_object, ['mime_type']))

  return to_object


def _Candidate_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['content']) is not None:
    setv(to_object, ['content'], getv(from_object, ['content']))

  if getv(from_object, ['citationMetadata']) is not None:
    setv(
        to_object,
        ['citation_metadata'],
        _CitationMetadata_from_mldev(
            getv(from_object, ['citationMetadata']), to_object, root_object
        ),
    )

  if getv(from_object, ['tokenCount']) is not None:
    setv(to_object, ['token_count'], getv(from_object, ['tokenCount']))

  if getv(from_object, ['finishReason']) is not None:
    setv(to_object, ['finish_reason'], getv(from_object, ['finishReason']))

  if getv(from_object, ['groundingMetadata']) is not None:
    setv(
        to_object,
        ['grounding_metadata'],
        getv(from_object, ['groundingMetadata']),
    )

  if getv(from_object, ['avgLogprobs']) is not None:
    setv(to_object, ['avg_logprobs'], getv(from_object, ['avgLogprobs']))

  if getv(from_object, ['index']) is not None:
    setv(to_object, ['index'], getv(from_object, ['index']))

  if getv(from_object, ['logprobsResult']) is not None:
    setv(to_object, ['logprobs_result'], getv(from_object, ['logprobsResult']))

  if getv(from_object, ['safetyRatings']) is not None:
    setv(
        to_object,
        ['safety_ratings'],
        [item for item in getv(from_object, ['safetyRatings'])],
    )

  if getv(from_object, ['urlContextMetadata']) is not None:
    setv(
        to_object,
        ['url_context_metadata'],
        getv(from_object, ['urlContextMetadata']),
    )

  return to_object


def _CitationMetadata_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['citationSources']) is not None:
    setv(
        to_object,
        ['citations'],
        [item for item in getv(from_object, ['citationSources'])],
    )

  return to_object


def _ComputeTokensParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['contents']) is not None:
    setv(
        to_object,
        ['contents'],
        [
            _Content_to_vertex(item, to_object, root_object)
            for item in t.t_contents(getv(from_object, ['contents']))
        ],
    )

  return to_object


def _ComputeTokensResponse_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['tokensInfo']) is not None:
    setv(
        to_object,
        ['tokens_info'],
        [item for item in getv(from_object, ['tokensInfo'])],
    )

  return to_object


def _ContentEmbeddingStatistics_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['truncated']) is not None:
    setv(to_object, ['truncated'], getv(from_object, ['truncated']))

  if getv(from_object, ['token_count']) is not None:
    setv(to_object, ['token_count'], getv(from_object, ['token_count']))

  return to_object


def _ContentEmbedding_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['values']) is not None:
    setv(to_object, ['values'], getv(from_object, ['values']))

  if getv(from_object, ['statistics']) is not None:
    setv(
        to_object,
        ['statistics'],
        _ContentEmbeddingStatistics_from_vertex(
            getv(from_object, ['statistics']), to_object, root_object
        ),
    )

  return to_object


def _Content_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['parts']) is not None:
    setv(
        to_object,
        ['parts'],
        [
            _Part_to_mldev(item, to_object, root_object)
            for item in getv(from_object, ['parts'])
        ],
    )

  if getv(from_object, ['role']) is not None:
    setv(to_object, ['role'], getv(from_object, ['role']))

  return to_object


def _Content_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['parts']) is not None:
    setv(
        to_object,
        ['parts'],
        [
            _Part_to_vertex(item, to_object, root_object)
            for item in getv(from_object, ['parts'])
        ],
    )

  if getv(from_object, ['role']) is not None:
    setv(to_object, ['role'], getv(from_object, ['role']))

  return to_object


def _ControlReferenceConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['control_type']) is not None:
    setv(to_object, ['controlType'], getv(from_object, ['control_type']))

  if getv(from_object, ['enable_control_image_computation']) is not None:
    setv(
        to_object,
        ['computeControl'],
        getv(from_object, ['enable_control_image_computation']),
    )

  return to_object


def _CountTokensConfig_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['system_instruction']) is not None:
    raise ValueError(
        'system_instruction parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['tools']) is not None:
    raise ValueError('tools parameter is not supported in Gemini API.')

  if getv(from_object, ['generation_config']) is not None:
    raise ValueError(
        'generation_config parameter is not supported in Gemini API.'
    )

  return to_object


def _CountTokensConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['system_instruction']) is not None:
    setv(
        parent_object,
        ['systemInstruction'],
        _Content_to_vertex(
            t.t_content(getv(from_object, ['system_instruction'])),
            to_object,
            root_object,
        ),
    )

  if getv(from_object, ['tools']) is not None:
    setv(
        parent_object,
        ['tools'],
        [
            _Tool_to_vertex(item, to_object, root_object)
            for item in getv(from_object, ['tools'])
        ],
    )

  if getv(from_object, ['generation_config']) is not None:
    setv(
        parent_object,
        ['generationConfig'],
        _GenerationConfig_to_vertex(
            getv(from_object, ['generation_config']), to_object, root_object
        ),
    )

  return to_object


def _CountTokensParameters_to_mldev(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['contents']) is not None:
    setv(
        to_object,
        ['contents'],
        [
            _Content_to_mldev(item, to_object, root_object)
            for item in t.t_contents(getv(from_object, ['contents']))
        ],
    )

  if getv(from_object, ['config']) is not None:
    _CountTokensConfig_to_mldev(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _CountTokensParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['contents']) is not None:
    setv(
        to_object,
        ['contents'],
        [
            _Content_to_vertex(item, to_object, root_object)
            for item in t.t_contents(getv(from_object, ['contents']))
        ],
    )

  if getv(from_object, ['config']) is not None:
    _CountTokensConfig_to_vertex(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _CountTokensResponse_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['totalTokens']) is not None:
    setv(to_object, ['total_tokens'], getv(from_object, ['totalTokens']))

  if getv(from_object, ['cachedContentTokenCount']) is not None:
    setv(
        to_object,
        ['cached_content_token_count'],
        getv(from_object, ['cachedContentTokenCount']),
    )

  return to_object


def _CountTokensResponse_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['totalTokens']) is not None:
    setv(to_object, ['total_tokens'], getv(from_object, ['totalTokens']))

  return to_object


def _DeleteModelParameters_to_mldev(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'name'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  return to_object


def _DeleteModelParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'name'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  return to_object


def _DeleteModelResponse_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  return to_object


def _DeleteModelResponse_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  return to_object


def _EditImageConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['output_gcs_uri']) is not None:
    setv(
        parent_object,
        ['parameters', 'storageUri'],
        getv(from_object, ['output_gcs_uri']),
    )

  if getv(from_object, ['negative_prompt']) is not None:
    setv(
        parent_object,
        ['parameters', 'negativePrompt'],
        getv(from_object, ['negative_prompt']),
    )

  if getv(from_object, ['number_of_images']) is not None:
    setv(
        parent_object,
        ['parameters', 'sampleCount'],
        getv(from_object, ['number_of_images']),
    )

  if getv(from_object, ['aspect_ratio']) is not None:
    setv(
        parent_object,
        ['parameters', 'aspectRatio'],
        getv(from_object, ['aspect_ratio']),
    )

  if getv(from_object, ['guidance_scale']) is not None:
    setv(
        parent_object,
        ['parameters', 'guidanceScale'],
        getv(from_object, ['guidance_scale']),
    )

  if getv(from_object, ['seed']) is not None:
    setv(parent_object, ['parameters', 'seed'], getv(from_object, ['seed']))

  if getv(from_object, ['safety_filter_level']) is not None:
    setv(
        parent_object,
        ['parameters', 'safetySetting'],
        getv(from_object, ['safety_filter_level']),
    )

  if getv(from_object, ['person_generation']) is not None:
    setv(
        parent_object,
        ['parameters', 'personGeneration'],
        getv(from_object, ['person_generation']),
    )

  if getv(from_object, ['include_safety_attributes']) is not None:
    setv(
        parent_object,
        ['parameters', 'includeSafetyAttributes'],
        getv(from_object, ['include_safety_attributes']),
    )

  if getv(from_object, ['include_rai_reason']) is not None:
    setv(
        parent_object,
        ['parameters', 'includeRaiReason'],
        getv(from_object, ['include_rai_reason']),
    )

  if getv(from_object, ['language']) is not None:
    setv(
        parent_object,
        ['parameters', 'language'],
        getv(from_object, ['language']),
    )

  if getv(from_object, ['output_mime_type']) is not None:
    setv(
        parent_object,
        ['parameters', 'outputOptions', 'mimeType'],
        getv(from_object, ['output_mime_type']),
    )

  if getv(from_object, ['output_compression_quality']) is not None:
    setv(
        parent_object,
        ['parameters', 'outputOptions', 'compressionQuality'],
        getv(from_object, ['output_compression_quality']),
    )

  if getv(from_object, ['add_watermark']) is not None:
    setv(
        parent_object,
        ['parameters', 'addWatermark'],
        getv(from_object, ['add_watermark']),
    )

  if getv(from_object, ['labels']) is not None:
    setv(parent_object, ['labels'], getv(from_object, ['labels']))

  if getv(from_object, ['edit_mode']) is not None:
    setv(
        parent_object,
        ['parameters', 'editMode'],
        getv(from_object, ['edit_mode']),
    )

  if getv(from_object, ['base_steps']) is not None:
    setv(
        parent_object,
        ['parameters', 'editConfig', 'baseSteps'],
        getv(from_object, ['base_steps']),
    )

  return to_object


def _EditImageParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['prompt']) is not None:
    setv(to_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt']))

  if getv(from_object, ['reference_images']) is not None:
    setv(
        to_object,
        ['instances[0]', 'referenceImages'],
        [
            _ReferenceImageAPI_to_vertex(item, to_object, root_object)
            for item in getv(from_object, ['reference_images'])
        ],
    )

  if getv(from_object, ['config']) is not None:
    _EditImageConfig_to_vertex(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _EditImageResponse_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['predictions']) is not None:
    setv(
        to_object,
        ['generated_images'],
        [
            _GeneratedImage_from_vertex(item, to_object, root_object)
            for item in getv(from_object, ['predictions'])
        ],
    )

  return to_object


def _EmbedContentConfig_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['task_type']) is not None:
    setv(
        parent_object,
        ['requests[]', 'taskType'],
        getv(from_object, ['task_type']),
    )

  if getv(from_object, ['title']) is not None:
    setv(parent_object, ['requests[]', 'title'], getv(from_object, ['title']))

  if getv(from_object, ['output_dimensionality']) is not None:
    setv(
        parent_object,
        ['requests[]', 'outputDimensionality'],
        getv(from_object, ['output_dimensionality']),
    )

  if getv(from_object, ['mime_type']) is not None:
    raise ValueError('mime_type parameter is not supported in Gemini API.')

  if getv(from_object, ['auto_truncate']) is not None:
    raise ValueError('auto_truncate parameter is not supported in Gemini API.')

  if getv(from_object, ['document_ocr']) is not None:
    raise ValueError('document_ocr parameter is not supported in Gemini API.')

  if getv(from_object, ['audio_track_extraction']) is not None:
    raise ValueError(
        'audio_track_extraction parameter is not supported in Gemini API.'
    )

  return to_object


def _EmbedContentConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  discriminator = getv(root_object, ['embedding_api_type'])
  if discriminator is None:
    discriminator = 'PREDICT'
  if discriminator == 'PREDICT':
    if getv(from_object, ['task_type']) is not None:
      setv(
          parent_object,
          ['instances[]', 'task_type'],
          getv(from_object, ['task_type']),
      )
  elif discriminator == 'EMBED_CONTENT':
    if getv(from_object, ['task_type']) is not None:
      setv(
          parent_object,
          ['embedContentConfig', 'taskType'],
          getv(from_object, ['task_type']),
      )

  discriminator = getv(root_object, ['embedding_api_type'])
  if discriminator is None:
    discriminator = 'PREDICT'
  if discriminator == 'PREDICT':
    if getv(from_object, ['title']) is not None:
      setv(
          parent_object, ['instances[]', 'title'], getv(from_object, ['title'])
      )
  elif discriminator == 'EMBED_CONTENT':
    if getv(from_object, ['title']) is not None:
      setv(
          parent_object,
          ['embedContentConfig', 'title'],
          getv(from_object, ['title']),
      )

  discriminator = getv(root_object, ['embedding_api_type'])
  if discriminator is None:
    discriminator = 'PREDICT'
  if discriminator == 'PREDICT':
    if getv(from_object, ['output_dimensionality']) is not None:
      setv(
          parent_object,
          ['parameters', 'outputDimensionality'],
          getv(from_object, ['output_dimensionality']),
      )
  elif discriminator == 'EMBED_CONTENT':
    if getv(from_object, ['output_dimensionality']) is not None:
      setv(
          parent_object,
          ['embedContentConfig', 'outputDimensionality'],
          getv(from_object, ['output_dimensionality']),
      )

  discriminator = getv(root_object, ['embedding_api_type'])
  if discriminator is None:
    discriminator = 'PREDICT'
  if discriminator == 'PREDICT':
    if getv(from_object, ['mime_type']) is not None:
      setv(
          parent_object,
          ['instances[]', 'mimeType'],
          getv(from_object, ['mime_type']),
      )

  discriminator = getv(root_object, ['embedding_api_type'])
  if discriminator is None:
    discriminator = 'PREDICT'
  if discriminator == 'PREDICT':
    if getv(from_object, ['auto_truncate']) is not None:
      setv(
          parent_object,
          ['parameters', 'autoTruncate'],
          getv(from_object, ['auto_truncate']),
      )
  elif discriminator == 'EMBED_CONTENT':
    if getv(from_object, ['auto_truncate']) is not None:
      setv(
          parent_object,
          ['embedContentConfig', 'autoTruncate'],
          getv(from_object, ['auto_truncate']),
      )

  discriminator = getv(root_object, ['embedding_api_type'])
  if discriminator is None:
    discriminator = 'PREDICT'
  if discriminator == 'EMBED_CONTENT':
    if getv(from_object, ['document_ocr']) is not None:
      setv(
          parent_object,
          ['embedContentConfig', 'documentOcr'],
          getv(from_object, ['document_ocr']),
      )

  discriminator = getv(root_object, ['embedding_api_type'])
  if discriminator is None:
    discriminator = 'PREDICT'
  if discriminator == 'EMBED_CONTENT':
    if getv(from_object, ['audio_track_extraction']) is not None:
      setv(
          parent_object,
          ['embedContentConfig', 'audioTrackExtraction'],
          getv(from_object, ['audio_track_extraction']),
      )

  return to_object


def _EmbedContentParametersPrivate_to_mldev(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['contents']) is not None:
    setv(
        to_object,
        ['requests[]', 'content'],
        [
            item
            for item in t.t_contents_for_embed(
                api_client, getv(from_object, ['contents'])
            )
        ],
    )

  if getv(from_object, ['content']) is not None:
    _Content_to_mldev(
        t.t_content(getv(from_object, ['content'])), to_object, root_object
    )

  if getv(from_object, ['config']) is not None:
    _EmbedContentConfig_to_mldev(
        getv(from_object, ['config']), to_object, root_object
    )

  setv(
      to_object,
      ['requests[]', 'model'],
      t.t_model(api_client, getv(from_object, ['model'])),
  )
  return to_object


def _EmbedContentParametersPrivate_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  discriminator = getv(root_object, ['embedding_api_type'])
  if discriminator is None:
    discriminator = 'PREDICT'
  if discriminator == 'PREDICT':
    if getv(from_object, ['contents']) is not None:
      setv(
          to_object,
          ['instances[]', 'content'],
          [
              item
              for item in t.t_contents_for_embed(
                  api_client, getv(from_object, ['contents'])
              )
          ],
      )

  discriminator = getv(root_object, ['embedding_api_type'])
  if discriminator is None:
    discriminator = 'PREDICT'
  if discriminator == 'EMBED_CONTENT':
    if getv(from_object, ['content']) is not None:
      setv(
          to_object,
          ['content'],
          _Content_to_vertex(
              t.t_content(getv(from_object, ['content'])),
              to_object,
              root_object,
          ),
      )

  if getv(from_object, ['config']) is not None:
    _EmbedContentConfig_to_vertex(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _EmbedContentResponse_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['embeddings']) is not None:
    setv(
        to_object,
        ['embeddings'],
        [item for item in getv(from_object, ['embeddings'])],
    )

  if getv(from_object, ['metadata']) is not None:
    setv(to_object, ['metadata'], getv(from_object, ['metadata']))

  return to_object


def _EmbedContentResponse_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['predictions[]', 'embeddings']) is not None:
    setv(
        to_object,
        ['embeddings'],
        [
            _ContentEmbedding_from_vertex(item, to_object, root_object)
            for item in getv(from_object, ['predictions[]', 'embeddings'])
        ],
    )

  if getv(from_object, ['metadata']) is not None:
    setv(to_object, ['metadata'], getv(from_object, ['metadata']))

  if getv(root_object, ['embedding_api_type']) == 'EMBED_CONTENT':
    embedding = getv(from_object, ['embedding'])
    usage_metadata = getv(from_object, ['usageMetadata'])
    truncated = getv(from_object, ['truncated'])
    if embedding:
      stats = {}
      if usage_metadata and usage_metadata.get('promptTokenCount'):
        stats['token_count'] = usage_metadata['promptTokenCount']
      if truncated:
        stats['truncated'] = truncated
      embedding['statistics'] = stats
      setv(to_object, ['embeddings'], [embedding])

  return to_object


def _Endpoint_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['endpoint']) is not None:
    setv(to_object, ['name'], getv(from_object, ['endpoint']))

  if getv(from_object, ['deployedModelId']) is not None:
    setv(
        to_object, ['deployed_model_id'], getv(from_object, ['deployedModelId'])
    )

  return to_object


def _FileData_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['display_name']) is not None:
    raise ValueError('display_name parameter is not supported in Gemini API.')

  if getv(from_object, ['file_uri']) is not None:
    setv(to_object, ['fileUri'], getv(from_object, ['file_uri']))

  if getv(from_object, ['mime_type']) is not None:
    setv(to_object, ['mimeType'], getv(from_object, ['mime_type']))

  return to_object


def _FunctionCall_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['id']) is not None:
    setv(to_object, ['id'], getv(from_object, ['id']))

  if getv(from_object, ['args']) is not None:
    setv(to_object, ['args'], getv(from_object, ['args']))

  if getv(from_object, ['name']) is not None:
    setv(to_object, ['name'], getv(from_object, ['name']))

  if getv(from_object, ['partial_args']) is not None:
    raise ValueError('partial_args parameter is not supported in Gemini API.')

  if getv(from_object, ['will_continue']) is not None:
    raise ValueError('will_continue parameter is not supported in Gemini API.')

  return to_object


def _FunctionCallingConfig_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['allowed_function_names']) is not None:
    setv(
        to_object,
        ['allowedFunctionNames'],
        getv(from_object, ['allowed_function_names']),
    )

  if getv(from_object, ['mode']) is not None:
    setv(to_object, ['mode'], getv(from_object, ['mode']))

  if getv(from_object, ['stream_function_call_arguments']) is not None:
    raise ValueError(
        'stream_function_call_arguments parameter is not supported in Gemini'
        ' API.'
    )

  return to_object


def _FunctionDeclaration_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['description']) is not None:
    setv(to_object, ['description'], getv(from_object, ['description']))

  if getv(from_object, ['name']) is not None:
    setv(to_object, ['name'], getv(from_object, ['name']))

  if getv(from_object, ['parameters']) is not None:
    setv(to_object, ['parameters'], getv(from_object, ['parameters']))

  if getv(from_object, ['parameters_json_schema']) is not None:
    setv(
        to_object,
        ['parametersJsonSchema'],
        getv(from_object, ['parameters_json_schema']),
    )

  if getv(from_object, ['response']) is not None:
    setv(to_object, ['response'], getv(from_object, ['response']))

  if getv(from_object, ['response_json_schema']) is not None:
    setv(
        to_object,
        ['responseJsonSchema'],
        getv(from_object, ['response_json_schema']),
    )

  if getv(from_object, ['behavior']) is not None:
    raise ValueError('behavior parameter is not supported in Vertex AI.')

  return to_object


def _GenerateContentConfig_to_mldev(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['system_instruction']) is not None:
    setv(
        parent_object,
        ['systemInstruction'],
        _Content_to_mldev(
            t.t_content(getv(from_object, ['system_instruction'])),
            to_object,
            root_object,
        ),
    )

  if getv(from_object, ['temperature']) is not None:
    setv(to_object, ['temperature'], getv(from_object, ['temperature']))

  if getv(from_object, ['top_p']) is not None:
    setv(to_object, ['topP'], getv(from_object, ['top_p']))

  if getv(from_object, ['top_k']) is not None:
    setv(to_object, ['topK'], getv(from_object, ['top_k']))

  if getv(from_object, ['candidate_count']) is not None:
    setv(to_object, ['candidateCount'], getv(from_object, ['candidate_count']))

  if getv(from_object, ['max_output_tokens']) is not None:
    setv(
        to_object, ['maxOutputTokens'], getv(from_object, ['max_output_tokens'])
    )

  if getv(from_object, ['stop_sequences']) is not None:
    setv(to_object, ['stopSequences'], getv(from_object, ['stop_sequences']))

  if getv(from_object, ['response_logprobs']) is not None:
    setv(
        to_object,
        ['responseLogprobs'],
        getv(from_object, ['response_logprobs']),
    )

  if getv(from_object, ['logprobs']) is not None:
    setv(to_object, ['logprobs'], getv(from_object, ['logprobs']))

  if getv(from_object, ['presence_penalty']) is not None:
    setv(
        to_object, ['presencePenalty'], getv(from_object, ['presence_penalty'])
    )

  if getv(from_object, ['frequency_penalty']) is not None:
    setv(
        to_object,
        ['frequencyPenalty'],
        getv(from_object, ['frequency_penalty']),
    )

  if getv(from_object, ['seed']) is not None:
    setv(to_object, ['seed'], getv(from_object, ['seed']))

  if getv(from_object, ['response_mime_type']) is not None:
    setv(
        to_object,
        ['responseMimeType'],
        getv(from_object, ['response_mime_type']),
    )

  if getv(from_object, ['response_schema']) is not None:
    setv(
        to_object,
        ['responseSchema'],
        t.t_schema(api_client, getv(from_object, ['response_schema'])),
    )

  if getv(from_object, ['response_json_schema']) is not None:
    setv(
        to_object,
        ['responseJsonSchema'],
        getv(from_object, ['response_json_schema']),
    )

  if getv(from_object, ['routing_config']) is not None:
    raise ValueError('routing_config parameter is not supported in Gemini API.')

  if getv(from_object, ['model_selection_config']) is not None:
    raise ValueError(
        'model_selection_config parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['safety_settings']) is not None:
    setv(
        parent_object,
        ['safetySettings'],
        [
            _SafetySetting_to_mldev(item, to_object, root_object)
            for item in getv(from_object, ['safety_settings'])
        ],
    )

  if getv(from_object, ['tools']) is not None:
    setv(
        parent_object,
        ['tools'],
        [
            _Tool_to_mldev(t.t_tool(api_client, item), to_object, root_object)
            for item in t.t_tools(api_client, getv(from_object, ['tools']))
        ],
    )

  if getv(from_object, ['tool_config']) is not None:
    setv(
        parent_object,
        ['toolConfig'],
        _ToolConfig_to_mldev(
            getv(from_object, ['tool_config']), to_object, root_object
        ),
    )

  if getv(from_object, ['labels']) is not None:
    raise ValueError('labels parameter is not supported in Gemini API.')

  if getv(from_object, ['cached_content']) is not None:
    setv(
        parent_object,
        ['cachedContent'],
        t.t_cached_content_name(
            api_client, getv(from_object, ['cached_content'])
        ),
    )

  if getv(from_object, ['response_modalities']) is not None:
    setv(
        to_object,
        ['responseModalities'],
        getv(from_object, ['response_modalities']),
    )

  if getv(from_object, ['media_resolution']) is not None:
    setv(
        to_object, ['mediaResolution'], getv(from_object, ['media_resolution'])
    )

  if getv(from_object, ['speech_config']) is not None:
    setv(
        to_object,
        ['speechConfig'],
        t.t_speech_config(getv(from_object, ['speech_config'])),
    )

  if getv(from_object, ['audio_timestamp']) is not None:
    raise ValueError(
        'audio_timestamp parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['thinking_config']) is not None:
    setv(to_object, ['thinkingConfig'], getv(from_object, ['thinking_config']))

  if getv(from_object, ['image_config']) is not None:
    setv(
        to_object,
        ['imageConfig'],
        _ImageConfig_to_mldev(
            getv(from_object, ['image_config']), to_object, root_object
        ),
    )

  if getv(from_object, ['enable_enhanced_civic_answers']) is not None:
    setv(
        to_object,
        ['enableEnhancedCivicAnswers'],
        getv(from_object, ['enable_enhanced_civic_answers']),
    )

  if getv(from_object, ['model_armor_config']) is not None:
    raise ValueError(
        'model_armor_config parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['service_tier']) is not None:
    setv(parent_object, ['serviceTier'], getv(from_object, ['service_tier']))

  return to_object


def _GenerateContentConfig_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['system_instruction']) is not None:
    setv(
        parent_object,
        ['systemInstruction'],
        _Content_to_vertex(
            t.t_content(getv(from_object, ['system_instruction'])),
            to_object,
            root_object,
        ),
    )

  if getv(from_object, ['temperature']) is not None:
    setv(to_object, ['temperature'], getv(from_object, ['temperature']))

  if getv(from_object, ['top_p']) is not None:
    setv(to_object, ['topP'], getv(from_object, ['top_p']))

  if getv(from_object, ['top_k']) is not None:
    setv(to_object, ['topK'], getv(from_object, ['top_k']))

  if getv(from_object, ['candidate_count']) is not None:
    setv(to_object, ['candidateCount'], getv(from_object, ['candidate_count']))

  if getv(from_object, ['max_output_tokens']) is not None:
    setv(
        to_object, ['maxOutputTokens'], getv(from_object, ['max_output_tokens'])
    )

  if getv(from_object, ['stop_sequences']) is not None:
    setv(to_object, ['stopSequences'], getv(from_object, ['stop_sequences']))

  if getv(from_object, ['response_logprobs']) is not None:
    setv(
        to_object,
        ['responseLogprobs'],
        getv(from_object, ['response_logprobs']),
    )

  if getv(from_object, ['logprobs']) is not None:
    setv(to_object, ['logprobs'], getv(from_object, ['logprobs']))

  if getv(from_object, ['presence_penalty']) is not None:
    setv(
        to_object, ['presencePenalty'], getv(from_object, ['presence_penalty'])
    )

  if getv(from_object, ['frequency_penalty']) is not None:
    setv(
        to_object,
        ['frequencyPenalty'],
        getv(from_object, ['frequency_penalty']),
    )

  if getv(from_object, ['seed']) is not None:
    setv(to_object, ['seed'], getv(from_object, ['seed']))

  if getv(from_object, ['response_mime_type']) is not None:
    setv(
        to_object,
        ['responseMimeType'],
        getv(from_object, ['response_mime_type']),
    )

  if getv(from_object, ['response_schema']) is not None:
    setv(
        to_object,
        ['responseSchema'],
        t.t_schema(api_client, getv(from_object, ['response_schema'])),
    )

  if getv(from_object, ['response_json_schema']) is not None:
    setv(
        to_object,
        ['responseJsonSchema'],
        getv(from_object, ['response_json_schema']),
    )

  if getv(from_object, ['routing_config']) is not None:
    setv(to_object, ['routingConfig'], getv(from_object, ['routing_config']))

  if getv(from_object, ['model_selection_config']) is not None:
    setv(
        to_object,
        ['modelConfig'],
        getv(from_object, ['model_selection_config']),
    )

  if getv(from_object, ['safety_settings']) is not None:
    setv(
        parent_object,
        ['safetySettings'],
        [item for item in getv(from_object, ['safety_settings'])],
    )

  if getv(from_object, ['tools']) is not None:
    setv(
        parent_object,
        ['tools'],
        [
            _Tool_to_vertex(t.t_tool(api_client, item), to_object, root_object)
            for item in t.t_tools(api_client, getv(from_object, ['tools']))
        ],
    )

  if getv(from_object, ['tool_config']) is not None:
    setv(
        parent_object,
        ['toolConfig'],
        _ToolConfig_to_vertex(
            getv(from_object, ['tool_config']), to_object, root_object
        ),
    )

  if getv(from_object, ['labels']) is not None:
    setv(parent_object, ['labels'], getv(from_object, ['labels']))

  if getv(from_object, ['cached_content']) is not None:
    setv(
        parent_object,
        ['cachedContent'],
        t.t_cached_content_name(
            api_client, getv(from_object, ['cached_content'])
        ),
    )

  if getv(from_object, ['response_modalities']) is not None:
    setv(
        to_object,
        ['responseModalities'],
        getv(from_object, ['response_modalities']),
    )

  if getv(from_object, ['media_resolution']) is not None:
    setv(
        to_object, ['mediaResolution'], getv(from_object, ['media_resolution'])
    )

  if getv(from_object, ['speech_config']) is not None:
    setv(
        to_object,
        ['speechConfig'],
        _SpeechConfig_to_vertex(
            t.t_speech_config(getv(from_object, ['speech_config'])),
            to_object,
            root_object,
        ),
    )

  if getv(from_object, ['audio_timestamp']) is not None:
    setv(to_object, ['audioTimestamp'], getv(from_object, ['audio_timestamp']))

  if getv(from_object, ['thinking_config']) is not None:
    setv(to_object, ['thinkingConfig'], getv(from_object, ['thinking_config']))

  if getv(from_object, ['image_config']) is not None:
    setv(
        to_object,
        ['imageConfig'],
        _ImageConfig_to_vertex(
            getv(from_object, ['image_config']), to_object, root_object
        ),
    )

  if getv(from_object, ['enable_enhanced_civic_answers']) is not None:
    raise ValueError(
        'enable_enhanced_civic_answers parameter is not supported in Vertex AI.'
    )

  if getv(from_object, ['model_armor_config']) is not None:
    setv(
        parent_object,
        ['modelArmorConfig'],
        getv(from_object, ['model_armor_config']),
    )

  if getv(from_object, ['service_tier']) is not None:
    setv(parent_object, ['serviceTier'], getv(from_object, ['service_tier']))

  return to_object


def _GenerateContentParameters_to_mldev(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['contents']) is not None:
    setv(
        to_object,
        ['contents'],
        [
            _Content_to_mldev(item, to_object, root_object)
            for item in t.t_contents(getv(from_object, ['contents']))
        ],
    )

  if getv(from_object, ['config']) is not None:
    setv(
        to_object,
        ['generationConfig'],
        _GenerateContentConfig_to_mldev(
            api_client, getv(from_object, ['config']), to_object, root_object
        ),
    )

  return to_object


def _GenerateContentParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['contents']) is not None:
    setv(
        to_object,
        ['contents'],
        [
            _Content_to_vertex(item, to_object, root_object)
            for item in t.t_contents(getv(from_object, ['contents']))
        ],
    )

  if getv(from_object, ['config']) is not None:
    setv(
        to_object,
        ['generationConfig'],
        _GenerateContentConfig_to_vertex(
            api_client, getv(from_object, ['config']), to_object, root_object
        ),
    )

  return to_object


def _GenerateContentResponse_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['candidates']) is not None:
    setv(
        to_object,
        ['candidates'],
        [
            _Candidate_from_mldev(item, to_object, root_object)
            for item in getv(from_object, ['candidates'])
        ],
    )

  if getv(from_object, ['modelVersion']) is not None:
    setv(to_object, ['model_version'], getv(from_object, ['modelVersion']))

  if getv(from_object, ['promptFeedback']) is not None:
    setv(to_object, ['prompt_feedback'], getv(from_object, ['promptFeedback']))

  if getv(from_object, ['responseId']) is not None:
    setv(to_object, ['response_id'], getv(from_object, ['responseId']))

  if getv(from_object, ['usageMetadata']) is not None:
    setv(to_object, ['usage_metadata'], getv(from_object, ['usageMetadata']))

  if getv(from_object, ['modelStatus']) is not None:
    setv(to_object, ['model_status'], getv(from_object, ['modelStatus']))

  return to_object


def _GenerateContentResponse_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['candidates']) is not None:
    setv(
        to_object,
        ['candidates'],
        [item for item in getv(from_object, ['candidates'])],
    )

  if getv(from_object, ['createTime']) is not None:
    setv(to_object, ['create_time'], getv(from_object, ['createTime']))

  if getv(from_object, ['modelVersion']) is not None:
    setv(to_object, ['model_version'], getv(from_object, ['modelVersion']))

  if getv(from_object, ['promptFeedback']) is not None:
    setv(to_object, ['prompt_feedback'], getv(from_object, ['promptFeedback']))

  if getv(from_object, ['responseId']) is not None:
    setv(to_object, ['response_id'], getv(from_object, ['responseId']))

  if getv(from_object, ['usageMetadata']) is not None:
    setv(to_object, ['usage_metadata'], getv(from_object, ['usageMetadata']))

  return to_object


def _GenerateImagesConfig_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['output_gcs_uri']) is not None:
    raise ValueError('output_gcs_uri parameter is not supported in Gemini API.')

  if getv(from_object, ['negative_prompt']) is not None:
    raise ValueError(
        'negative_prompt parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['number_of_images']) is not None:
    setv(
        parent_object,
        ['parameters', 'sampleCount'],
        getv(from_object, ['number_of_images']),
    )

  if getv(from_object, ['aspect_ratio']) is not None:
    setv(
        parent_object,
        ['parameters', 'aspectRatio'],
        getv(from_object, ['aspect_ratio']),
    )

  if getv(from_object, ['guidance_scale']) is not None:
    setv(
        parent_object,
        ['parameters', 'guidanceScale'],
        getv(from_object, ['guidance_scale']),
    )

  if getv(from_object, ['seed']) is not None:
    raise ValueError('seed parameter is not supported in Gemini API.')

  if getv(from_object, ['safety_filter_level']) is not None:
    _SafetyFilterLevel_to_mldev_enum_validate(
        getv(from_object, ['safety_filter_level'])
    )
    setv(
        parent_object,
        ['parameters', 'safetySetting'],
        getv(from_object, ['safety_filter_level']),
    )

  if getv(from_object, ['person_generation']) is not None:
    _PersonGeneration_to_mldev_enum_validate(
        getv(from_object, ['person_generation'])
    )
    setv(
        parent_object,
        ['parameters', 'personGeneration'],
        getv(from_object, ['person_generation']),
    )

  if getv(from_object, ['include_safety_attributes']) is not None:
    setv(
        parent_object,
        ['parameters', 'includeSafetyAttributes'],
        getv(from_object, ['include_safety_attributes']),
    )

  if getv(from_object, ['include_rai_reason']) is not None:
    setv(
        parent_object,
        ['parameters', 'includeRaiReason'],
        getv(from_object, ['include_rai_reason']),
    )

  if getv(from_object, ['language']) is not None:
    setv(
        parent_object,
        ['parameters', 'language'],
        getv(from_object, ['language']),
    )

  if getv(from_object, ['output_mime_type']) is not None:
    setv(
        parent_object,
        ['parameters', 'outputOptions', 'mimeType'],
        getv(from_object, ['output_mime_type']),
    )

  if getv(from_object, ['output_compression_quality']) is not None:
    setv(
        parent_object,
        ['parameters', 'outputOptions', 'compressionQuality'],
        getv(from_object, ['output_compression_quality']),
    )

  if getv(from_object, ['add_watermark']) is not None:
    raise ValueError('add_watermark parameter is not supported in Gemini API.')

  if getv(from_object, ['labels']) is not None:
    raise ValueError('labels parameter is not supported in Gemini API.')

  if getv(from_object, ['image_size']) is not None:
    setv(
        parent_object,
        ['parameters', 'sampleImageSize'],
        getv(from_object, ['image_size']),
    )

  if getv(from_object, ['enhance_prompt']) is not None:
    raise ValueError('enhance_prompt parameter is not supported in Gemini API.')

  return to_object


def _GenerateImagesConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['output_gcs_uri']) is not None:
    setv(
        parent_object,
        ['parameters', 'storageUri'],
        getv(from_object, ['output_gcs_uri']),
    )

  if getv(from_object, ['negative_prompt']) is not None:
    setv(
        parent_object,
        ['parameters', 'negativePrompt'],
        getv(from_object, ['negative_prompt']),
    )

  if getv(from_object, ['number_of_images']) is not None:
    setv(
        parent_object,
        ['parameters', 'sampleCount'],
        getv(from_object, ['number_of_images']),
    )

  if getv(from_object, ['aspect_ratio']) is not None:
    setv(
        parent_object,
        ['parameters', 'aspectRatio'],
        getv(from_object, ['aspect_ratio']),
    )

  if getv(from_object, ['guidance_scale']) is not None:
    setv(
        parent_object,
        ['parameters', 'guidanceScale'],
        getv(from_object, ['guidance_scale']),
    )

  if getv(from_object, ['seed']) is not None:
    setv(parent_object, ['parameters', 'seed'], getv(from_object, ['seed']))

  if getv(from_object, ['safety_filter_level']) is not None:
    setv(
        parent_object,
        ['parameters', 'safetySetting'],
        getv(from_object, ['safety_filter_level']),
    )

  if getv(from_object, ['person_generation']) is not None:
    setv(
        parent_object,
        ['parameters', 'personGeneration'],
        getv(from_object, ['person_generation']),
    )

  if getv(from_object, ['include_safety_attributes']) is not None:
    setv(
        parent_object,
        ['parameters', 'includeSafetyAttributes'],
        getv(from_object, ['include_safety_attributes']),
    )

  if getv(from_object, ['include_rai_reason']) is not None:
    setv(
        parent_object,
        ['parameters', 'includeRaiReason'],
        getv(from_object, ['include_rai_reason']),
    )

  if getv(from_object, ['language']) is not None:
    setv(
        parent_object,
        ['parameters', 'language'],
        getv(from_object, ['language']),
    )

  if getv(from_object, ['output_mime_type']) is not None:
    setv(
        parent_object,
        ['parameters', 'outputOptions', 'mimeType'],
        getv(from_object, ['output_mime_type']),
    )

  if getv(from_object, ['output_compression_quality']) is not None:
    setv(
        parent_object,
        ['parameters', 'outputOptions', 'compressionQuality'],
        getv(from_object, ['output_compression_quality']),
    )

  if getv(from_object, ['add_watermark']) is not None:
    setv(
        parent_object,
        ['parameters', 'addWatermark'],
        getv(from_object, ['add_watermark']),
    )

  if getv(from_object, ['labels']) is not None:
    setv(parent_object, ['labels'], getv(from_object, ['labels']))

  if getv(from_object, ['image_size']) is not None:
    setv(
        parent_object,
        ['parameters', 'sampleImageSize'],
        getv(from_object, ['image_size']),
    )

  if getv(from_object, ['enhance_prompt']) is not None:
    setv(
        parent_object,
        ['parameters', 'enhancePrompt'],
        getv(from_object, ['enhance_prompt']),
    )

  return to_object


def _GenerateImagesParameters_to_mldev(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['prompt']) is not None:
    setv(to_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt']))

  if getv(from_object, ['config']) is not None:
    _GenerateImagesConfig_to_mldev(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _GenerateImagesParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['prompt']) is not None:
    setv(to_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt']))

  if getv(from_object, ['config']) is not None:
    _GenerateImagesConfig_to_vertex(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _GenerateImagesResponse_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['predictions']) is not None:
    setv(
        to_object,
        ['generated_images'],
        [
            _GeneratedImage_from_mldev(item, to_object, root_object)
            for item in getv(from_object, ['predictions'])
        ],
    )

  if getv(from_object, ['positivePromptSafetyAttributes']) is not None:
    setv(
        to_object,
        ['positive_prompt_safety_attributes'],
        _SafetyAttributes_from_mldev(
            getv(from_object, ['positivePromptSafetyAttributes']),
            to_object,
            root_object,
        ),
    )

  return to_object


def _GenerateImagesResponse_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['predictions']) is not None:
    setv(
        to_object,
        ['generated_images'],
        [
            _GeneratedImage_from_vertex(item, to_object, root_object)
            for item in getv(from_object, ['predictions'])
        ],
    )

  if getv(from_object, ['positivePromptSafetyAttributes']) is not None:
    setv(
        to_object,
        ['positive_prompt_safety_attributes'],
        _SafetyAttributes_from_vertex(
            getv(from_object, ['positivePromptSafetyAttributes']),
            to_object,
            root_object,
        ),
    )

  return to_object


def _GenerateVideosConfig_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['number_of_videos']) is not None:
    setv(
        parent_object,
        ['parameters', 'sampleCount'],
        getv(from_object, ['number_of_videos']),
    )

  if getv(from_object, ['output_gcs_uri']) is not None:
    raise ValueError('output_gcs_uri parameter is not supported in Gemini API.')

  if getv(from_object, ['fps']) is not None:
    raise ValueError('fps parameter is not supported in Gemini API.')

  if getv(from_object, ['duration_seconds']) is not None:
    setv(
        parent_object,
        ['parameters', 'durationSeconds'],
        getv(from_object, ['duration_seconds']),
    )

  if getv(from_object, ['seed']) is not None:
    raise ValueError('seed parameter is not supported in Gemini API.')

  if getv(from_object, ['aspect_ratio']) is not None:
    setv(
        parent_object,
        ['parameters', 'aspectRatio'],
        getv(from_object, ['aspect_ratio']),
    )

  if getv(from_object, ['resolution']) is not None:
    setv(
        parent_object,
        ['parameters', 'resolution'],
        getv(from_object, ['resolution']),
    )

  if getv(from_object, ['person_generation']) is not None:
    setv(
        parent_object,
        ['parameters', 'personGeneration'],
        getv(from_object, ['person_generation']),
    )

  if getv(from_object, ['pubsub_topic']) is not None:
    raise ValueError('pubsub_topic parameter is not supported in Gemini API.')

  if getv(from_object, ['negative_prompt']) is not None:
    setv(
        parent_object,
        ['parameters', 'negativePrompt'],
        getv(from_object, ['negative_prompt']),
    )

  if getv(from_object, ['enhance_prompt']) is not None:
    setv(
        parent_object,
        ['parameters', 'enhancePrompt'],
        getv(from_object, ['enhance_prompt']),
    )

  if getv(from_object, ['generate_audio']) is not None:
    raise ValueError('generate_audio parameter is not supported in Gemini API.')

  if getv(from_object, ['last_frame']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'lastFrame'],
        _Image_to_mldev(
            getv(from_object, ['last_frame']), to_object, root_object
        ),
    )

  if getv(from_object, ['reference_images']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'referenceImages'],
        [
            _VideoGenerationReferenceImage_to_mldev(
                item, to_object, root_object
            )
            for item in getv(from_object, ['reference_images'])
        ],
    )

  if getv(from_object, ['mask']) is not None:
    raise ValueError('mask parameter is not supported in Gemini API.')

  if getv(from_object, ['compression_quality']) is not None:
    raise ValueError(
        'compression_quality parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['labels']) is not None:
    raise ValueError('labels parameter is not supported in Gemini API.')

  if getv(from_object, ['webhook_config']) is not None:
    setv(
        parent_object, ['webhookConfig'], getv(from_object, ['webhook_config'])
    )

  return to_object


def _GenerateVideosConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['number_of_videos']) is not None:
    setv(
        parent_object,
        ['parameters', 'sampleCount'],
        getv(from_object, ['number_of_videos']),
    )

  if getv(from_object, ['output_gcs_uri']) is not None:
    setv(
        parent_object,
        ['parameters', 'storageUri'],
        getv(from_object, ['output_gcs_uri']),
    )

  if getv(from_object, ['fps']) is not None:
    setv(parent_object, ['parameters', 'fps'], getv(from_object, ['fps']))

  if getv(from_object, ['duration_seconds']) is not None:
    setv(
        parent_object,
        ['parameters', 'durationSeconds'],
        getv(from_object, ['duration_seconds']),
    )

  if getv(from_object, ['seed']) is not None:
    setv(parent_object, ['parameters', 'seed'], getv(from_object, ['seed']))

  if getv(from_object, ['aspect_ratio']) is not None:
    setv(
        parent_object,
        ['parameters', 'aspectRatio'],
        getv(from_object, ['aspect_ratio']),
    )

  if getv(from_object, ['resolution']) is not None:
    setv(
        parent_object,
        ['parameters', 'resolution'],
        getv(from_object, ['resolution']),
    )

  if getv(from_object, ['person_generation']) is not None:
    setv(
        parent_object,
        ['parameters', 'personGeneration'],
        getv(from_object, ['person_generation']),
    )

  if getv(from_object, ['pubsub_topic']) is not None:
    setv(
        parent_object,
        ['parameters', 'pubsubTopic'],
        getv(from_object, ['pubsub_topic']),
    )

  if getv(from_object, ['negative_prompt']) is not None:
    setv(
        parent_object,
        ['parameters', 'negativePrompt'],
        getv(from_object, ['negative_prompt']),
    )

  if getv(from_object, ['enhance_prompt']) is not None:
    setv(
        parent_object,
        ['parameters', 'enhancePrompt'],
        getv(from_object, ['enhance_prompt']),
    )

  if getv(from_object, ['generate_audio']) is not None:
    setv(
        parent_object,
        ['parameters', 'generateAudio'],
        getv(from_object, ['generate_audio']),
    )

  if getv(from_object, ['last_frame']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'lastFrame'],
        _Image_to_vertex(
            getv(from_object, ['last_frame']), to_object, root_object
        ),
    )

  if getv(from_object, ['reference_images']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'referenceImages'],
        [
            _VideoGenerationReferenceImage_to_vertex(
                item, to_object, root_object
            )
            for item in getv(from_object, ['reference_images'])
        ],
    )

  if getv(from_object, ['mask']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'mask'],
        _VideoGenerationMask_to_vertex(
            getv(from_object, ['mask']), to_object, root_object
        ),
    )

  if getv(from_object, ['compression_quality']) is not None:
    setv(
        parent_object,
        ['parameters', 'compressionQuality'],
        getv(from_object, ['compression_quality']),
    )

  if getv(from_object, ['labels']) is not None:
    setv(parent_object, ['labels'], getv(from_object, ['labels']))

  if getv(from_object, ['webhook_config']) is not None:
    raise ValueError('webhook_config parameter is not supported in Vertex AI.')

  return to_object


def _GenerateVideosOperation_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['name']) is not None:
    setv(to_object, ['name'], getv(from_object, ['name']))

  if getv(from_object, ['metadata']) is not None:
    setv(to_object, ['metadata'], getv(from_object, ['metadata']))

  if getv(from_object, ['done']) is not None:
    setv(to_object, ['done'], getv(from_object, ['done']))

  if getv(from_object, ['error']) is not None:
    setv(to_object, ['error'], getv(from_object, ['error']))

  if getv(from_object, ['response', 'generateVideoResponse']) is not None:
    setv(
        to_object,
        ['response'],
        _GenerateVideosResponse_from_mldev(
            getv(from_object, ['response', 'generateVideoResponse']),
            to_object,
            root_object,
        ),
    )

  if getv(from_object, ['response', 'generateVideoResponse']) is not None:
    setv(
        to_object,
        ['result'],
        _GenerateVideosResponse_from_mldev(
            getv(from_object, ['response', 'generateVideoResponse']),
            to_object,
            root_object,
        ),
    )

  return to_object


def _GenerateVideosOperation_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['name']) is not None:
    setv(to_object, ['name'], getv(from_object, ['name']))

  if getv(from_object, ['metadata']) is not None:
    setv(to_object, ['metadata'], getv(from_object, ['metadata']))

  if getv(from_object, ['done']) is not None:
    setv(to_object, ['done'], getv(from_object, ['done']))

  if getv(from_object, ['error']) is not None:
    setv(to_object, ['error'], getv(from_object, ['error']))

  if getv(from_object, ['response']) is not None:
    setv(
        to_object,
        ['response'],
        _GenerateVideosResponse_from_vertex(
            getv(from_object, ['response']), to_object, root_object
        ),
    )

  if getv(from_object, ['response']) is not None:
    setv(
        to_object,
        ['result'],
        _GenerateVideosResponse_from_vertex(
            getv(from_object, ['response']), to_object, root_object
        ),
    )

  return to_object


def _GenerateVideosParameters_to_mldev(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['prompt']) is not None:
    setv(to_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt']))

  if getv(from_object, ['image']) is not None:
    setv(
        to_object,
        ['instances[0]', 'image'],
        _Image_to_mldev(getv(from_object, ['image']), to_object, root_object),
    )

  if getv(from_object, ['video']) is not None:
    setv(
        to_object,
        ['instances[0]', 'video'],
        _Video_to_mldev(getv(from_object, ['video']), to_object, root_object),
    )

  if getv(from_object, ['source']) is not None:
    _GenerateVideosSource_to_mldev(
        getv(from_object, ['source']), to_object, root_object
    )

  if getv(from_object, ['config']) is not None:
    _GenerateVideosConfig_to_mldev(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _GenerateVideosParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['prompt']) is not None:
    setv(to_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt']))

  if getv(from_object, ['image']) is not None:
    setv(
        to_object,
        ['instances[0]', 'image'],
        _Image_to_vertex(getv(from_object, ['image']), to_object, root_object),
    )

  if getv(from_object, ['video']) is not None:
    setv(
        to_object,
        ['instances[0]', 'video'],
        _Video_to_vertex(getv(from_object, ['video']), to_object, root_object),
    )

  if getv(from_object, ['source']) is not None:
    _GenerateVideosSource_to_vertex(
        getv(from_object, ['source']), to_object, root_object
    )

  if getv(from_object, ['config']) is not None:
    _GenerateVideosConfig_to_vertex(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _GenerateVideosResponse_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['generatedSamples']) is not None:
    setv(
        to_object,
        ['generated_videos'],
        [
            _GeneratedVideo_from_mldev(item, to_object, root_object)
            for item in getv(from_object, ['generatedSamples'])
        ],
    )

  if getv(from_object, ['raiMediaFilteredCount']) is not None:
    setv(
        to_object,
        ['rai_media_filtered_count'],
        getv(from_object, ['raiMediaFilteredCount']),
    )

  if getv(from_object, ['raiMediaFilteredReasons']) is not None:
    setv(
        to_object,
        ['rai_media_filtered_reasons'],
        getv(from_object, ['raiMediaFilteredReasons']),
    )

  return to_object


def _GenerateVideosResponse_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['videos']) is not None:
    setv(
        to_object,
        ['generated_videos'],
        [
            _GeneratedVideo_from_vertex(item, to_object, root_object)
            for item in getv(from_object, ['videos'])
        ],
    )

  if getv(from_object, ['raiMediaFilteredCount']) is not None:
    setv(
        to_object,
        ['rai_media_filtered_count'],
        getv(from_object, ['raiMediaFilteredCount']),
    )

  if getv(from_object, ['raiMediaFilteredReasons']) is not None:
    setv(
        to_object,
        ['rai_media_filtered_reasons'],
        getv(from_object, ['raiMediaFilteredReasons']),
    )

  return to_object


def _GenerateVideosSource_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['prompt']) is not None:
    setv(
        parent_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt'])
    )

  if getv(from_object, ['image']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'image'],
        _Image_to_mldev(getv(from_object, ['image']), to_object, root_object),
    )

  if getv(from_object, ['video']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'video'],
        _Video_to_mldev(getv(from_object, ['video']), to_object, root_object),
    )

  return to_object


def _GenerateVideosSource_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['prompt']) is not None:
    setv(
        parent_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt'])
    )

  if getv(from_object, ['image']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'image'],
        _Image_to_vertex(getv(from_object, ['image']), to_object, root_object),
    )

  if getv(from_object, ['video']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'video'],
        _Video_to_vertex(getv(from_object, ['video']), to_object, root_object),
    )

  return to_object


def _GeneratedImageMask_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['_self']) is not None:
    setv(
        to_object,
        ['mask'],
        _Image_from_vertex(
            getv(from_object, ['_self']), to_object, root_object
        ),
    )

  if getv(from_object, ['labels']) is not None:
    setv(
        to_object, ['labels'], [item for item in getv(from_object, ['labels'])]
    )

  return to_object


def _GeneratedImage_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['_self']) is not None:
    setv(
        to_object,
        ['image'],
        _Image_from_mldev(getv(from_object, ['_self']), to_object, root_object),
    )

  if getv(from_object, ['raiFilteredReason']) is not None:
    setv(
        to_object,
        ['rai_filtered_reason'],
        getv(from_object, ['raiFilteredReason']),
    )

  if getv(from_object, ['_self']) is not None:
    setv(
        to_object,
        ['safety_attributes'],
        _SafetyAttributes_from_mldev(
            getv(from_object, ['_self']), to_object, root_object
        ),
    )

  return to_object


def _GeneratedImage_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['_self']) is not None:
    setv(
        to_object,
        ['image'],
        _Image_from_vertex(
            getv(from_object, ['_self']), to_object, root_object
        ),
    )

  if getv(from_object, ['raiFilteredReason']) is not None:
    setv(
        to_object,
        ['rai_filtered_reason'],
        getv(from_object, ['raiFilteredReason']),
    )

  if getv(from_object, ['_self']) is not None:
    setv(
        to_object,
        ['safety_attributes'],
        _SafetyAttributes_from_vertex(
            getv(from_object, ['_self']), to_object, root_object
        ),
    )

  if getv(from_object, ['prompt']) is not None:
    setv(to_object, ['enhanced_prompt'], getv(from_object, ['prompt']))

  return to_object


def _GeneratedVideo_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['video']) is not None:
    setv(
        to_object,
        ['video'],
        _Video_from_mldev(getv(from_object, ['video']), to_object, root_object),
    )

  return to_object


def _GeneratedVideo_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['_self']) is not None:
    setv(
        to_object,
        ['video'],
        _Video_from_vertex(
            getv(from_object, ['_self']), to_object, root_object
        ),
    )

  return to_object


def _GenerationConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model_selection_config']) is not None:
    setv(
        to_object,
        ['modelConfig'],
        getv(from_object, ['model_selection_config']),
    )

  if getv(from_object, ['response_json_schema']) is not None:
    setv(
        to_object,
        ['responseJsonSchema'],
        getv(from_object, ['response_json_schema']),
    )

  if getv(from_object, ['audio_timestamp']) is not None:
    setv(to_object, ['audioTimestamp'], getv(from_object, ['audio_timestamp']))

  if getv(from_object, ['candidate_count']) is not None:
    setv(to_object, ['candidateCount'], getv(from_object, ['candidate_count']))

  if getv(from_object, ['enable_affective_dialog']) is not None:
    setv(
        to_object,
        ['enableAffectiveDialog'],
        getv(from_object, ['enable_affective_dialog']),
    )

  if getv(from_object, ['frequency_penalty']) is not None:
    setv(
        to_object,
        ['frequencyPenalty'],
        getv(from_object, ['frequency_penalty']),
    )

  if getv(from_object, ['logprobs']) is not None:
    setv(to_object, ['logprobs'], getv(from_object, ['logprobs']))

  if getv(from_object, ['max_output_tokens']) is not None:
    setv(
        to_object, ['maxOutputTokens'], getv(from_object, ['max_output_tokens'])
    )

  if getv(from_object, ['media_resolution']) is not None:
    setv(
        to_object, ['mediaResolution'], getv(from_object, ['media_resolution'])
    )

  if getv(from_object, ['presence_penalty']) is not None:
    setv(
        to_object, ['presencePenalty'], getv(from_object, ['presence_penalty'])
    )

  if getv(from_object, ['response_logprobs']) is not None:
    setv(
        to_object,
        ['responseLogprobs'],
        getv(from_object, ['response_logprobs']),
    )

  if getv(from_object, ['response_mime_type']) is not None:
    setv(
        to_object,
        ['responseMimeType'],
        getv(from_object, ['response_mime_type']),
    )

  if getv(from_object, ['response_modalities']) is not None:
    setv(
        to_object,
        ['responseModalities'],
        getv(from_object, ['response_modalities']),
    )

  if getv(from_object, ['response_schema']) is not None:
    setv(to_object, ['responseSchema'], getv(from_object, ['response_schema']))

  if getv(from_object, ['routing_config']) is not None:
    setv(to_object, ['routingConfig'], getv(from_object, ['routing_config']))

  if getv(from_object, ['seed']) is not None:
    setv(to_object, ['seed'], getv(from_object, ['seed']))

  if getv(from_object, ['speech_config']) is not None:
    setv(
        to_object,
        ['speechConfig'],
        _SpeechConfig_to_vertex(
            getv(from_object, ['speech_config']), to_object, root_object
        ),
    )

  if getv(from_object, ['stop_sequences']) is not None:
    setv(to_object, ['stopSequences'], getv(from_object, ['stop_sequences']))

  if getv(from_object, ['temperature']) is not None:
    setv(to_object, ['temperature'], getv(from_object, ['temperature']))

  if getv(from_object, ['thinking_config']) is not None:
    setv(to_object, ['thinkingConfig'], getv(from_object, ['thinking_config']))

  if getv(from_object, ['top_k']) is not None:
    setv(to_object, ['topK'], getv(from_object, ['top_k']))

  if getv(from_object, ['top_p']) is not None:
    setv(to_object, ['topP'], getv(from_object, ['top_p']))

  if getv(from_object, ['enable_enhanced_civic_answers']) is not None:
    raise ValueError(
        'enable_enhanced_civic_answers parameter is not supported in Vertex AI.'
    )

  return to_object


def _GetModelParameters_to_mldev(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'name'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  return to_object


def _GetModelParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'name'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  return to_object


def _GoogleMaps_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['auth_config']) is not None:
    setv(
        to_object,
        ['authConfig'],
        _AuthConfig_to_mldev(
            getv(from_object, ['auth_config']), to_object, root_object
        ),
    )

  if getv(from_object, ['enable_widget']) is not None:
    setv(to_object, ['enableWidget'], getv(from_object, ['enable_widget']))

  return to_object


def _GoogleSearch_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['search_types']) is not None:
    setv(to_object, ['searchTypes'], getv(from_object, ['search_types']))

  if getv(from_object, ['blocking_confidence']) is not None:
    raise ValueError(
        'blocking_confidence parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['exclude_domains']) is not None:
    raise ValueError(
        'exclude_domains parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['time_range_filter']) is not None:
    setv(
        to_object, ['timeRangeFilter'], getv(from_object, ['time_range_filter'])
    )

  return to_object


def _ImageConfig_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['aspect_ratio']) is not None:
    setv(to_object, ['aspectRatio'], getv(from_object, ['aspect_ratio']))

  if getv(from_object, ['image_size']) is not None:
    setv(to_object, ['imageSize'], getv(from_object, ['image_size']))

  if getv(from_object, ['person_generation']) is not None:
    raise ValueError(
        'person_generation parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['prominent_people']) is not None:
    raise ValueError(
        'prominent_people parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['output_mime_type']) is not None:
    raise ValueError(
        'output_mime_type parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['output_compression_quality']) is not None:
    raise ValueError(
        'output_compression_quality parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['image_output_options']) is not None:
    raise ValueError(
        'image_output_options parameter is not supported in Gemini API.'
    )

  return to_object


def _ImageConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['aspect_ratio']) is not None:
    setv(to_object, ['aspectRatio'], getv(from_object, ['aspect_ratio']))

  if getv(from_object, ['image_size']) is not None:
    setv(to_object, ['imageSize'], getv(from_object, ['image_size']))

  if getv(from_object, ['person_generation']) is not None:
    setv(
        to_object,
        ['personGeneration'],
        getv(from_object, ['person_generation']),
    )

  if getv(from_object, ['prominent_people']) is not None:
    setv(
        to_object, ['prominentPeople'], getv(from_object, ['prominent_people'])
    )

  if getv(from_object, ['output_mime_type']) is not None:
    setv(
        to_object,
        ['imageOutputOptions', 'mimeType'],
        getv(from_object, ['output_mime_type']),
    )

  if getv(from_object, ['output_compression_quality']) is not None:
    setv(
        to_object,
        ['imageOutputOptions', 'compressionQuality'],
        getv(from_object, ['output_compression_quality']),
    )

  if getv(from_object, ['image_output_options']) is not None:
    setv(
        to_object,
        ['imageOutputOptions'],
        getv(from_object, ['image_output_options']),
    )

  return to_object


def _Image_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['bytesBase64Encoded']) is not None:
    setv(
        to_object,
        ['image_bytes'],
        base_t.t_bytes(getv(from_object, ['bytesBase64Encoded'])),
    )

  if getv(from_object, ['mimeType']) is not None:
    setv(to_object, ['mime_type'], getv(from_object, ['mimeType']))

  return to_object


def _Image_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['gcsUri']) is not None:
    setv(to_object, ['gcs_uri'], getv(from_object, ['gcsUri']))

  if getv(from_object, ['bytesBase64Encoded']) is not None:
    setv(
        to_object,
        ['image_bytes'],
        base_t.t_bytes(getv(from_object, ['bytesBase64Encoded'])),
    )

  if getv(from_object, ['mimeType']) is not None:
    setv(to_object, ['mime_type'], getv(from_object, ['mimeType']))

  return to_object


def _Image_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['gcs_uri']) is not None:
    raise ValueError('gcs_uri parameter is not supported in Gemini API.')

  if getv(from_object, ['image_bytes']) is not None:
    setv(
        to_object,
        ['bytesBase64Encoded'],
        base_t.t_bytes(getv(from_object, ['image_bytes'])),
    )

  if getv(from_object, ['mime_type']) is not None:
    setv(to_object, ['mimeType'], getv(from_object, ['mime_type']))

  return to_object


def _Image_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['gcs_uri']) is not None:
    setv(to_object, ['gcsUri'], getv(from_object, ['gcs_uri']))

  if getv(from_object, ['image_bytes']) is not None:
    setv(
        to_object,
        ['bytesBase64Encoded'],
        base_t.t_bytes(getv(from_object, ['image_bytes'])),
    )

  if getv(from_object, ['mime_type']) is not None:
    setv(to_object, ['mimeType'], getv(from_object, ['mime_type']))

  return to_object


def _ListModelsConfig_to_mldev(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['page_size']) is not None:
    setv(
        parent_object, ['_query', 'pageSize'], getv(from_object, ['page_size'])
    )

  if getv(from_object, ['page_token']) is not None:
    setv(
        parent_object,
        ['_query', 'pageToken'],
        getv(from_object, ['page_token']),
    )

  if getv(from_object, ['filter']) is not None:
    setv(parent_object, ['_query', 'filter'], getv(from_object, ['filter']))

  if getv(from_object, ['query_base']) is not None:
    setv(
        parent_object,
        ['_url', 'models_url'],
        t.t_models_url(api_client, getv(from_object, ['query_base'])),
    )

  return to_object


def _ListModelsConfig_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['page_size']) is not None:
    setv(
        parent_object, ['_query', 'pageSize'], getv(from_object, ['page_size'])
    )

  if getv(from_object, ['page_token']) is not None:
    setv(
        parent_object,
        ['_query', 'pageToken'],
        getv(from_object, ['page_token']),
    )

  if getv(from_object, ['filter']) is not None:
    setv(parent_object, ['_query', 'filter'], getv(from_object, ['filter']))

  if getv(from_object, ['query_base']) is not None:
    setv(
        parent_object,
        ['_url', 'models_url'],
        t.t_models_url(api_client, getv(from_object, ['query_base'])),
    )

  return to_object


def _ListModelsParameters_to_mldev(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['config']) is not None:
    _ListModelsConfig_to_mldev(
        api_client, getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _ListModelsParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['config']) is not None:
    _ListModelsConfig_to_vertex(
        api_client, getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _ListModelsResponse_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['nextPageToken']) is not None:
    setv(to_object, ['next_page_token'], getv(from_object, ['nextPageToken']))

  if getv(from_object, ['_self']) is not None:
    setv(
        to_object,
        ['models'],
        [
            _Model_from_mldev(item, to_object, root_object)
            for item in t.t_extract_models(getv(from_object, ['_self']))
        ],
    )

  return to_object


def _ListModelsResponse_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['nextPageToken']) is not None:
    setv(to_object, ['next_page_token'], getv(from_object, ['nextPageToken']))

  if getv(from_object, ['_self']) is not None:
    setv(
        to_object,
        ['models'],
        [
            _Model_from_vertex(item, to_object, root_object)
            for item in t.t_extract_models(getv(from_object, ['_self']))
        ],
    )

  return to_object


def _MaskReferenceConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['mask_mode']) is not None:
    setv(to_object, ['maskMode'], getv(from_object, ['mask_mode']))

  if getv(from_object, ['segmentation_classes']) is not None:
    setv(
        to_object, ['maskClasses'], getv(from_object, ['segmentation_classes'])
    )

  if getv(from_object, ['mask_dilation']) is not None:
    setv(to_object, ['dilation'], getv(from_object, ['mask_dilation']))

  return to_object


def _Model_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['name']) is not None:
    setv(to_object, ['name'], getv(from_object, ['name']))

  if getv(from_object, ['displayName']) is not None:
    setv(to_object, ['display_name'], getv(from_object, ['displayName']))

  if getv(from_object, ['description']) is not None:
    setv(to_object, ['description'], getv(from_object, ['description']))

  if getv(from_object, ['version']) is not None:
    setv(to_object, ['version'], getv(from_object, ['version']))

  if getv(from_object, ['_self']) is not None:
    setv(to_object, ['tuned_model_info'], getv(from_object, ['_self']))

  if getv(from_object, ['inputTokenLimit']) is not None:
    setv(
        to_object, ['input_token_limit'], getv(from_object, ['inputTokenLimit'])
    )

  if getv(from_object, ['outputTokenLimit']) is not None:
    setv(
        to_object,
        ['output_token_limit'],
        getv(from_object, ['outputTokenLimit']),
    )

  if getv(from_object, ['supportedGenerationMethods']) is not None:
    setv(
        to_object,
        ['supported_actions'],
        getv(from_object, ['supportedGenerationMethods']),
    )

  if getv(from_object, ['temperature']) is not None:
    setv(to_object, ['temperature'], getv(from_object, ['temperature']))

  if getv(from_object, ['maxTemperature']) is not None:
    setv(to_object, ['max_temperature'], getv(from_object, ['maxTemperature']))

  if getv(from_object, ['topP']) is not None:
    setv(to_object, ['top_p'], getv(from_object, ['topP']))

  if getv(from_object, ['topK']) is not None:
    setv(to_object, ['top_k'], getv(from_object, ['topK']))

  if getv(from_object, ['thinking']) is not None:
    setv(to_object, ['thinking'], getv(from_object, ['thinking']))

  return to_object


def _Model_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['name']) is not None:
    setv(to_object, ['name'], getv(from_object, ['name']))

  if getv(from_object, ['displayName']) is not None:
    setv(to_object, ['display_name'], getv(from_object, ['displayName']))

  if getv(from_object, ['description']) is not None:
    setv(to_object, ['description'], getv(from_object, ['description']))

  if getv(from_object, ['versionId']) is not None:
    setv(to_object, ['version'], getv(from_object, ['versionId']))

  if getv(from_object, ['deployedModels']) is not None:
    setv(
        to_object,
        ['endpoints'],
        [
            _Endpoint_from_vertex(item, to_object, root_object)
            for item in getv(from_object, ['deployedModels'])
        ],
    )

  if getv(from_object, ['labels']) is not None:
    setv(to_object, ['labels'], getv(from_object, ['labels']))

  if getv(from_object, ['_self']) is not None:
    setv(
        to_object,
        ['tuned_model_info'],
        _TunedModelInfo_from_vertex(
            getv(from_object, ['_self']), to_object, root_object
        ),
    )

  if getv(from_object, ['defaultCheckpointId']) is not None:
    setv(
        to_object,
        ['default_checkpoint_id'],
        getv(from_object, ['defaultCheckpointId']),
    )

  if getv(from_object, ['checkpoints']) is not None:
    setv(
        to_object,
        ['checkpoints'],
        [item for item in getv(from_object, ['checkpoints'])],
    )

  return to_object


def _MultiSpeakerVoiceConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['speaker_voice_configs']) is not None:
    setv(
        to_object,
        ['speakerVoiceConfigs'],
        [
            _SpeakerVoiceConfig_to_vertex(item, to_object, root_object)
            for item in getv(from_object, ['speaker_voice_configs'])
        ],
    )

  return to_object


def _Part_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['media_resolution']) is not None:
    setv(
        to_object, ['mediaResolution'], getv(from_object, ['media_resolution'])
    )

  if getv(from_object, ['code_execution_result']) is not None:
    setv(
        to_object,
        ['codeExecutionResult'],
        getv(from_object, ['code_execution_result']),
    )

  if getv(from_object, ['executable_code']) is not None:
    setv(to_object, ['executableCode'], getv(from_object, ['executable_code']))

  if getv(from_object, ['file_data']) is not None:
    setv(
        to_object,
        ['fileData'],
        _FileData_to_mldev(
            getv(from_object, ['file_data']), to_object, root_object
        ),
    )

  if getv(from_object, ['function_call']) is not None:
    setv(
        to_object,
        ['functionCall'],
        _FunctionCall_to_mldev(
            getv(from_object, ['function_call']), to_object, root_object
        ),
    )

  if getv(from_object, ['function_response']) is not None:
    setv(
        to_object,
        ['functionResponse'],
        getv(from_object, ['function_response']),
    )

  if getv(from_object, ['inline_data']) is not None:
    setv(
        to_object,
        ['inlineData'],
        _Blob_to_mldev(
            getv(from_object, ['inline_data']), to_object, root_object
        ),
    )

  if getv(from_object, ['text']) is not None:
    setv(to_object, ['text'], getv(from_object, ['text']))

  if getv(from_object, ['thought']) is not None:
    setv(to_object, ['thought'], getv(from_object, ['thought']))

  if getv(from_object, ['thought_signature']) is not None:
    setv(
        to_object,
        ['thoughtSignature'],
        getv(from_object, ['thought_signature']),
    )

  if getv(from_object, ['video_metadata']) is not None:
    setv(to_object, ['videoMetadata'], getv(from_object, ['video_metadata']))

  if getv(from_object, ['tool_call']) is not None:
    setv(to_object, ['toolCall'], getv(from_object, ['tool_call']))

  if getv(from_object, ['tool_response']) is not None:
    setv(to_object, ['toolResponse'], getv(from_object, ['tool_response']))

  if getv(from_object, ['part_metadata']) is not None:
    setv(to_object, ['partMetadata'], getv(from_object, ['part_metadata']))

  return to_object


def _Part_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['media_resolution']) is not None:
    setv(
        to_object, ['mediaResolution'], getv(from_object, ['media_resolution'])
    )

  if getv(from_object, ['code_execution_result']) is not None:
    setv(
        to_object,
        ['codeExecutionResult'],
        getv(from_object, ['code_execution_result']),
    )

  if getv(from_object, ['executable_code']) is not None:
    setv(to_object, ['executableCode'], getv(from_object, ['executable_code']))

  if getv(from_object, ['file_data']) is not None:
    setv(to_object, ['fileData'], getv(from_object, ['file_data']))

  if getv(from_object, ['function_call']) is not None:
    setv(to_object, ['functionCall'], getv(from_object, ['function_call']))

  if getv(from_object, ['function_response']) is not None:
    setv(
        to_object,
        ['functionResponse'],
        getv(from_object, ['function_response']),
    )

  if getv(from_object, ['inline_data']) is not None:
    setv(to_object, ['inlineData'], getv(from_object, ['inline_data']))

  if getv(from_object, ['text']) is not None:
    setv(to_object, ['text'], getv(from_object, ['text']))

  if getv(from_object, ['thought']) is not None:
    setv(to_object, ['thought'], getv(from_object, ['thought']))

  if getv(from_object, ['thought_signature']) is not None:
    setv(
        to_object,
        ['thoughtSignature'],
        getv(from_object, ['thought_signature']),
    )

  if getv(from_object, ['video_metadata']) is not None:
    setv(to_object, ['videoMetadata'], getv(from_object, ['video_metadata']))

  if getv(from_object, ['tool_call']) is not None:
    raise ValueError('tool_call parameter is not supported in Vertex AI.')

  if getv(from_object, ['tool_response']) is not None:
    raise ValueError('tool_response parameter is not supported in Vertex AI.')

  if getv(from_object, ['part_metadata']) is not None:
    raise ValueError('part_metadata parameter is not supported in Vertex AI.')

  return to_object


def _ProductImage_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['product_image']) is not None:
    setv(
        to_object,
        ['image'],
        _Image_to_vertex(
            getv(from_object, ['product_image']), to_object, root_object
        ),
    )

  return to_object


def _RecontextImageConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['number_of_images']) is not None:
    setv(
        parent_object,
        ['parameters', 'sampleCount'],
        getv(from_object, ['number_of_images']),
    )

  if getv(from_object, ['base_steps']) is not None:
    setv(
        parent_object,
        ['parameters', 'baseSteps'],
        getv(from_object, ['base_steps']),
    )

  if getv(from_object, ['output_gcs_uri']) is not None:
    setv(
        parent_object,
        ['parameters', 'storageUri'],
        getv(from_object, ['output_gcs_uri']),
    )

  if getv(from_object, ['seed']) is not None:
    setv(parent_object, ['parameters', 'seed'], getv(from_object, ['seed']))

  if getv(from_object, ['safety_filter_level']) is not None:
    setv(
        parent_object,
        ['parameters', 'safetySetting'],
        getv(from_object, ['safety_filter_level']),
    )

  if getv(from_object, ['person_generation']) is not None:
    setv(
        parent_object,
        ['parameters', 'personGeneration'],
        getv(from_object, ['person_generation']),
    )

  if getv(from_object, ['add_watermark']) is not None:
    setv(
        parent_object,
        ['parameters', 'addWatermark'],
        getv(from_object, ['add_watermark']),
    )

  if getv(from_object, ['output_mime_type']) is not None:
    setv(
        parent_object,
        ['parameters', 'outputOptions', 'mimeType'],
        getv(from_object, ['output_mime_type']),
    )

  if getv(from_object, ['output_compression_quality']) is not None:
    setv(
        parent_object,
        ['parameters', 'outputOptions', 'compressionQuality'],
        getv(from_object, ['output_compression_quality']),
    )

  if getv(from_object, ['enhance_prompt']) is not None:
    setv(
        parent_object,
        ['parameters', 'enhancePrompt'],
        getv(from_object, ['enhance_prompt']),
    )

  if getv(from_object, ['labels']) is not None:
    setv(parent_object, ['labels'], getv(from_object, ['labels']))

  return to_object


def _RecontextImageParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['source']) is not None:
    _RecontextImageSource_to_vertex(
        getv(from_object, ['source']), to_object, root_object
    )

  if getv(from_object, ['config']) is not None:
    _RecontextImageConfig_to_vertex(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _RecontextImageResponse_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['predictions']) is not None:
    setv(
        to_object,
        ['generated_images'],
        [
            _GeneratedImage_from_vertex(item, to_object, root_object)
            for item in getv(from_object, ['predictions'])
        ],
    )

  return to_object


def _RecontextImageSource_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['prompt']) is not None:
    setv(
        parent_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt'])
    )

  if getv(from_object, ['person_image']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'personImage', 'image'],
        _Image_to_vertex(
            getv(from_object, ['person_image']), to_object, root_object
        ),
    )

  if getv(from_object, ['product_images']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'productImages'],
        [
            _ProductImage_to_vertex(item, to_object, root_object)
            for item in getv(from_object, ['product_images'])
        ],
    )

  return to_object


def _ReferenceImageAPI_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['reference_image']) is not None:
    setv(
        to_object,
        ['referenceImage'],
        _Image_to_vertex(
            getv(from_object, ['reference_image']), to_object, root_object
        ),
    )

  if getv(from_object, ['reference_id']) is not None:
    setv(to_object, ['referenceId'], getv(from_object, ['reference_id']))

  if getv(from_object, ['reference_type']) is not None:
    setv(to_object, ['referenceType'], getv(from_object, ['reference_type']))

  if getv(from_object, ['mask_image_config']) is not None:
    setv(
        to_object,
        ['maskImageConfig'],
        _MaskReferenceConfig_to_vertex(
            getv(from_object, ['mask_image_config']), to_object, root_object
        ),
    )

  if getv(from_object, ['control_image_config']) is not None:
    setv(
        to_object,
        ['controlImageConfig'],
        _ControlReferenceConfig_to_vertex(
            getv(from_object, ['control_image_config']), to_object, root_object
        ),
    )

  if getv(from_object, ['style_image_config']) is not None:
    setv(
        to_object,
        ['styleImageConfig'],
        getv(from_object, ['style_image_config']),
    )

  if getv(from_object, ['subject_image_config']) is not None:
    setv(
        to_object,
        ['subjectImageConfig'],
        getv(from_object, ['subject_image_config']),
    )

  return to_object


def _ReplicatedVoiceConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['mime_type']) is not None:
    setv(to_object, ['mimeType'], getv(from_object, ['mime_type']))

  if getv(from_object, ['voice_sample_audio']) is not None:
    setv(
        to_object,
        ['voiceSampleAudio'],
        getv(from_object, ['voice_sample_audio']),
    )

  if getv(from_object, ['consent_audio']) is not None:
    raise ValueError('consent_audio parameter is not supported in Vertex AI.')

  if getv(from_object, ['voice_consent_signature']) is not None:
    raise ValueError(
        'voice_consent_signature parameter is not supported in Vertex AI.'
    )

  return to_object


def _SafetyAttributes_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['safetyAttributes', 'categories']) is not None:
    setv(
        to_object,
        ['categories'],
        getv(from_object, ['safetyAttributes', 'categories']),
    )

  if getv(from_object, ['safetyAttributes', 'scores']) is not None:
    setv(
        to_object, ['scores'], getv(from_object, ['safetyAttributes', 'scores'])
    )

  if getv(from_object, ['contentType']) is not None:
    setv(to_object, ['content_type'], getv(from_object, ['contentType']))

  return to_object


def _SafetyAttributes_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['safetyAttributes', 'categories']) is not None:
    setv(
        to_object,
        ['categories'],
        getv(from_object, ['safetyAttributes', 'categories']),
    )

  if getv(from_object, ['safetyAttributes', 'scores']) is not None:
    setv(
        to_object, ['scores'], getv(from_object, ['safetyAttributes', 'scores'])
    )

  if getv(from_object, ['contentType']) is not None:
    setv(to_object, ['content_type'], getv(from_object, ['contentType']))

  return to_object


def _SafetySetting_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['category']) is not None:
    setv(to_object, ['category'], getv(from_object, ['category']))

  if getv(from_object, ['method']) is not None:
    raise ValueError('method parameter is not supported in Gemini API.')

  if getv(from_object, ['threshold']) is not None:
    setv(to_object, ['threshold'], getv(from_object, ['threshold']))

  return to_object


def _ScribbleImage_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['image']) is not None:
    setv(
        to_object,
        ['image'],
        _Image_to_vertex(getv(from_object, ['image']), to_object, root_object),
    )

  return to_object


def _SegmentImageConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['mode']) is not None:
    setv(parent_object, ['parameters', 'mode'], getv(from_object, ['mode']))

  if getv(from_object, ['max_predictions']) is not None:
    setv(
        parent_object,
        ['parameters', 'maxPredictions'],
        getv(from_object, ['max_predictions']),
    )

  if getv(from_object, ['confidence_threshold']) is not None:
    setv(
        parent_object,
        ['parameters', 'confidenceThreshold'],
        getv(from_object, ['confidence_threshold']),
    )

  if getv(from_object, ['mask_dilation']) is not None:
    setv(
        parent_object,
        ['parameters', 'maskDilation'],
        getv(from_object, ['mask_dilation']),
    )

  if getv(from_object, ['binary_color_threshold']) is not None:
    setv(
        parent_object,
        ['parameters', 'binaryColorThreshold'],
        getv(from_object, ['binary_color_threshold']),
    )

  if getv(from_object, ['labels']) is not None:
    setv(parent_object, ['labels'], getv(from_object, ['labels']))

  return to_object


def _SegmentImageParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['source']) is not None:
    _SegmentImageSource_to_vertex(
        getv(from_object, ['source']), to_object, root_object
    )

  if getv(from_object, ['config']) is not None:
    _SegmentImageConfig_to_vertex(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _SegmentImageResponse_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['predictions']) is not None:
    setv(
        to_object,
        ['generated_masks'],
        [
            _GeneratedImageMask_from_vertex(item, to_object, root_object)
            for item in getv(from_object, ['predictions'])
        ],
    )

  return to_object


def _SegmentImageSource_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['prompt']) is not None:
    setv(
        parent_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt'])
    )

  if getv(from_object, ['image']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'image'],
        _Image_to_vertex(getv(from_object, ['image']), to_object, root_object),
    )

  if getv(from_object, ['scribble_image']) is not None:
    setv(
        parent_object,
        ['instances[0]', 'scribble'],
        _ScribbleImage_to_vertex(
            getv(from_object, ['scribble_image']), to_object, root_object
        ),
    )

  return to_object


def _SpeakerVoiceConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['speaker']) is not None:
    setv(to_object, ['speaker'], getv(from_object, ['speaker']))

  if getv(from_object, ['voice_config']) is not None:
    setv(
        to_object,
        ['voiceConfig'],
        _VoiceConfig_to_vertex(
            getv(from_object, ['voice_config']), to_object, root_object
        ),
    )

  return to_object


def _SpeechConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['voice_config']) is not None:
    setv(
        to_object,
        ['voiceConfig'],
        _VoiceConfig_to_vertex(
            getv(from_object, ['voice_config']), to_object, root_object
        ),
    )

  if getv(from_object, ['language_code']) is not None:
    setv(to_object, ['languageCode'], getv(from_object, ['language_code']))

  if getv(from_object, ['multi_speaker_voice_config']) is not None:
    setv(
        to_object,
        ['multiSpeakerVoiceConfig'],
        _MultiSpeakerVoiceConfig_to_vertex(
            getv(from_object, ['multi_speaker_voice_config']),
            to_object,
            root_object,
        ),
    )

  return to_object


def _ToolConfig_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['retrieval_config']) is not None:
    setv(
        to_object, ['retrievalConfig'], getv(from_object, ['retrieval_config'])
    )

  if getv(from_object, ['function_calling_config']) is not None:
    setv(
        to_object,
        ['functionCallingConfig'],
        _FunctionCallingConfig_to_mldev(
            getv(from_object, ['function_calling_config']),
            to_object,
            root_object,
        ),
    )

  if getv(from_object, ['include_server_side_tool_invocations']) is not None:
    setv(
        to_object,
        ['includeServerSideToolInvocations'],
        getv(from_object, ['include_server_side_tool_invocations']),
    )

  return to_object


def _ToolConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['retrieval_config']) is not None:
    setv(
        to_object, ['retrievalConfig'], getv(from_object, ['retrieval_config'])
    )

  if getv(from_object, ['function_calling_config']) is not None:
    setv(
        to_object,
        ['functionCallingConfig'],
        getv(from_object, ['function_calling_config']),
    )

  if getv(from_object, ['include_server_side_tool_invocations']) is not None:
    raise ValueError(
        'include_server_side_tool_invocations parameter is not supported in'
        ' Vertex AI.'
    )

  return to_object


def _Tool_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['retrieval']) is not None:
    raise ValueError('retrieval parameter is not supported in Gemini API.')

  if getv(from_object, ['computer_use']) is not None:
    setv(to_object, ['computerUse'], getv(from_object, ['computer_use']))

  if getv(from_object, ['file_search']) is not None:
    setv(to_object, ['fileSearch'], getv(from_object, ['file_search']))

  if getv(from_object, ['google_search']) is not None:
    setv(
        to_object,
        ['googleSearch'],
        _GoogleSearch_to_mldev(
            getv(from_object, ['google_search']), to_object, root_object
        ),
    )

  if getv(from_object, ['google_maps']) is not None:
    setv(
        to_object,
        ['googleMaps'],
        _GoogleMaps_to_mldev(
            getv(from_object, ['google_maps']), to_object, root_object
        ),
    )

  if getv(from_object, ['code_execution']) is not None:
    setv(to_object, ['codeExecution'], getv(from_object, ['code_execution']))

  if getv(from_object, ['enterprise_web_search']) is not None:
    raise ValueError(
        'enterprise_web_search parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['function_declarations']) is not None:
    setv(
        to_object,
        ['functionDeclarations'],
        [item for item in getv(from_object, ['function_declarations'])],
    )

  if getv(from_object, ['google_search_retrieval']) is not None:
    setv(
        to_object,
        ['googleSearchRetrieval'],
        getv(from_object, ['google_search_retrieval']),
    )

  if getv(from_object, ['parallel_ai_search']) is not None:
    raise ValueError(
        'parallel_ai_search parameter is not supported in Gemini API.'
    )

  if getv(from_object, ['url_context']) is not None:
    setv(to_object, ['urlContext'], getv(from_object, ['url_context']))

  if getv(from_object, ['mcp_servers']) is not None:
    setv(
        to_object,
        ['mcpServers'],
        [item for item in getv(from_object, ['mcp_servers'])],
    )

  return to_object


def _Tool_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['retrieval']) is not None:
    setv(to_object, ['retrieval'], getv(from_object, ['retrieval']))

  if getv(from_object, ['computer_use']) is not None:
    setv(to_object, ['computerUse'], getv(from_object, ['computer_use']))

  if getv(from_object, ['file_search']) is not None:
    raise ValueError('file_search parameter is not supported in Vertex AI.')

  if getv(from_object, ['google_search']) is not None:
    setv(to_object, ['googleSearch'], getv(from_object, ['google_search']))

  if getv(from_object, ['google_maps']) is not None:
    setv(to_object, ['googleMaps'], getv(from_object, ['google_maps']))

  if getv(from_object, ['code_execution']) is not None:
    setv(to_object, ['codeExecution'], getv(from_object, ['code_execution']))

  if getv(from_object, ['enterprise_web_search']) is not None:
    setv(
        to_object,
        ['enterpriseWebSearch'],
        getv(from_object, ['enterprise_web_search']),
    )

  if getv(from_object, ['function_declarations']) is not None:
    setv(
        to_object,
        ['functionDeclarations'],
        [
            _FunctionDeclaration_to_vertex(item, to_object, root_object)
            for item in getv(from_object, ['function_declarations'])
        ],
    )

  if getv(from_object, ['google_search_retrieval']) is not None:
    setv(
        to_object,
        ['googleSearchRetrieval'],
        getv(from_object, ['google_search_retrieval']),
    )

  if getv(from_object, ['parallel_ai_search']) is not None:
    setv(
        to_object,
        ['parallelAiSearch'],
        getv(from_object, ['parallel_ai_search']),
    )

  if getv(from_object, ['url_context']) is not None:
    setv(to_object, ['urlContext'], getv(from_object, ['url_context']))

  if getv(from_object, ['mcp_servers']) is not None:
    raise ValueError('mcp_servers parameter is not supported in Vertex AI.')

  return to_object


def _TunedModelInfo_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if (
      getv(from_object, ['labels', 'google-vertex-llm-tuning-base-model-id'])
      is not None
  ):
    setv(
        to_object,
        ['base_model'],
        getv(from_object, ['labels', 'google-vertex-llm-tuning-base-model-id']),
    )

  if getv(from_object, ['createTime']) is not None:
    setv(to_object, ['create_time'], getv(from_object, ['createTime']))

  if getv(from_object, ['updateTime']) is not None:
    setv(to_object, ['update_time'], getv(from_object, ['updateTime']))

  return to_object


def _UpdateModelConfig_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['display_name']) is not None:
    setv(parent_object, ['displayName'], getv(from_object, ['display_name']))

  if getv(from_object, ['description']) is not None:
    setv(parent_object, ['description'], getv(from_object, ['description']))

  if getv(from_object, ['default_checkpoint_id']) is not None:
    setv(
        parent_object,
        ['defaultCheckpointId'],
        getv(from_object, ['default_checkpoint_id']),
    )

  return to_object


def _UpdateModelConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['display_name']) is not None:
    setv(parent_object, ['displayName'], getv(from_object, ['display_name']))

  if getv(from_object, ['description']) is not None:
    setv(parent_object, ['description'], getv(from_object, ['description']))

  if getv(from_object, ['default_checkpoint_id']) is not None:
    setv(
        parent_object,
        ['defaultCheckpointId'],
        getv(from_object, ['default_checkpoint_id']),
    )

  return to_object


def _UpdateModelParameters_to_mldev(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'name'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['config']) is not None:
    _UpdateModelConfig_to_mldev(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _UpdateModelParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['config']) is not None:
    _UpdateModelConfig_to_vertex(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _UpscaleImageAPIConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}

  if getv(from_object, ['output_gcs_uri']) is not None:
    setv(
        parent_object,
        ['parameters', 'storageUri'],
        getv(from_object, ['output_gcs_uri']),
    )

  if getv(from_object, ['safety_filter_level']) is not None:
    setv(
        parent_object,
        ['parameters', 'safetySetting'],
        getv(from_object, ['safety_filter_level']),
    )

  if getv(from_object, ['person_generation']) is not None:
    setv(
        parent_object,
        ['parameters', 'personGeneration'],
        getv(from_object, ['person_generation']),
    )

  if getv(from_object, ['include_rai_reason']) is not None:
    setv(
        parent_object,
        ['parameters', 'includeRaiReason'],
        getv(from_object, ['include_rai_reason']),
    )

  if getv(from_object, ['output_mime_type']) is not None:
    setv(
        parent_object,
        ['parameters', 'outputOptions', 'mimeType'],
        getv(from_object, ['output_mime_type']),
    )

  if getv(from_object, ['output_compression_quality']) is not None:
    setv(
        parent_object,
        ['parameters', 'outputOptions', 'compressionQuality'],
        getv(from_object, ['output_compression_quality']),
    )

  if getv(from_object, ['enhance_input_image']) is not None:
    setv(
        parent_object,
        ['parameters', 'upscaleConfig', 'enhanceInputImage'],
        getv(from_object, ['enhance_input_image']),
    )

  if getv(from_object, ['image_preservation_factor']) is not None:
    setv(
        parent_object,
        ['parameters', 'upscaleConfig', 'imagePreservationFactor'],
        getv(from_object, ['image_preservation_factor']),
    )

  if getv(from_object, ['labels']) is not None:
    setv(parent_object, ['labels'], getv(from_object, ['labels']))

  if getv(from_object, ['number_of_images']) is not None:
    setv(
        parent_object,
        ['parameters', 'sampleCount'],
        getv(from_object, ['number_of_images']),
    )

  if getv(from_object, ['mode']) is not None:
    setv(parent_object, ['parameters', 'mode'], getv(from_object, ['mode']))

  return to_object


def _UpscaleImageAPIParameters_to_vertex(
    api_client: BaseApiClient,
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['model']) is not None:
    setv(
        to_object,
        ['_url', 'model'],
        t.t_model(api_client, getv(from_object, ['model'])),
    )

  if getv(from_object, ['image']) is not None:
    setv(
        to_object,
        ['instances[0]', 'image'],
        _Image_to_vertex(getv(from_object, ['image']), to_object, root_object),
    )

  if getv(from_object, ['upscale_factor']) is not None:
    setv(
        to_object,
        ['parameters', 'upscaleConfig', 'upscaleFactor'],
        getv(from_object, ['upscale_factor']),
    )

  if getv(from_object, ['config']) is not None:
    _UpscaleImageAPIConfig_to_vertex(
        getv(from_object, ['config']), to_object, root_object
    )

  return to_object


def _UpscaleImageResponse_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['sdkHttpResponse']) is not None:
    setv(
        to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
    )

  if getv(from_object, ['predictions']) is not None:
    setv(
        to_object,
        ['generated_images'],
        [
            _GeneratedImage_from_vertex(item, to_object, root_object)
            for item in getv(from_object, ['predictions'])
        ],
    )

  return to_object


def _VideoGenerationMask_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['image']) is not None:
    setv(
        to_object,
        ['_self'],
        _Image_to_vertex(getv(from_object, ['image']), to_object, root_object),
    )

  if getv(from_object, ['mask_mode']) is not None:
    setv(to_object, ['maskMode'], getv(from_object, ['mask_mode']))

  return to_object


def _VideoGenerationReferenceImage_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['image']) is not None:
    setv(
        to_object,
        ['image'],
        _Image_to_mldev(getv(from_object, ['image']), to_object, root_object),
    )

  if getv(from_object, ['reference_type']) is not None:
    _VideoGenerationReferenceType_to_mldev_enum_validate(
        getv(from_object, ['reference_type'])
    )
    setv(to_object, ['referenceType'], getv(from_object, ['reference_type']))

  return to_object


def _VideoGenerationReferenceImage_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['image']) is not None:
    setv(
        to_object,
        ['image'],
        _Image_to_vertex(getv(from_object, ['image']), to_object, root_object),
    )

  if getv(from_object, ['reference_type']) is not None:
    setv(to_object, ['referenceType'], getv(from_object, ['reference_type']))

  return to_object


def _Video_from_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['uri']) is not None:
    setv(to_object, ['uri'], getv(from_object, ['uri']))

  if getv(from_object, ['encodedVideo']) is not None:
    setv(
        to_object,
        ['video_bytes'],
        base_t.t_bytes(getv(from_object, ['encodedVideo'])),
    )

  if getv(from_object, ['encoding']) is not None:
    setv(to_object, ['mime_type'], getv(from_object, ['encoding']))

  return to_object


def _Video_from_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['gcsUri']) is not None:
    setv(to_object, ['uri'], getv(from_object, ['gcsUri']))

  if getv(from_object, ['bytesBase64Encoded']) is not None:
    setv(
        to_object,
        ['video_bytes'],
        base_t.t_bytes(getv(from_object, ['bytesBase64Encoded'])),
    )

  if getv(from_object, ['mimeType']) is not None:
    setv(to_object, ['mime_type'], getv(from_object, ['mimeType']))

  return to_object


def _Video_to_mldev(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['uri']) is not None:
    setv(to_object, ['uri'], getv(from_object, ['uri']))

  if getv(from_object, ['video_bytes']) is not None:
    setv(
        to_object,
        ['encodedVideo'],
        base_t.t_bytes(getv(from_object, ['video_bytes'])),
    )

  if getv(from_object, ['mime_type']) is not None:
    setv(to_object, ['encoding'], getv(from_object, ['mime_type']))

  return to_object


def _Video_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['uri']) is not None:
    setv(to_object, ['gcsUri'], getv(from_object, ['uri']))

  if getv(from_object, ['video_bytes']) is not None:
    setv(
        to_object,
        ['bytesBase64Encoded'],
        base_t.t_bytes(getv(from_object, ['video_bytes'])),
    )

  if getv(from_object, ['mime_type']) is not None:
    setv(to_object, ['mimeType'], getv(from_object, ['mime_type']))

  return to_object


def _VoiceConfig_to_vertex(
    from_object: Union[dict[str, Any], object],
    parent_object: Optional[dict[str, Any]] = None,
    root_object: Optional[Union[dict[str, Any], object]] = None,
) -> dict[str, Any]:
  to_object: dict[str, Any] = {}
  if getv(from_object, ['replicated_voice_config']) is not None:
    setv(
        to_object,
        ['replicatedVoiceConfig'],
        _ReplicatedVoiceConfig_to_vertex(
            getv(from_object, ['replicated_voice_config']),
            to_object,
            root_object,
        ),
    )

  if getv(from_object, ['prebuilt_voice_config']) is not None:
    setv(
        to_object,
        ['prebuiltVoiceConfig'],
        getv(from_object, ['prebuilt_voice_config']),
    )

  return to_object


class Models(_api_module.BaseModule):

  def _generate_content(
      self,
      *,
      model: str,
      contents: Union[types.ContentListUnion, types.ContentListUnionDict],
      config: Optional[types.GenerateContentConfigOrDict] = None,
  ) -> types.GenerateContentResponse:
    parameter_model = types._GenerateContentParameters(
        model=model,
        contents=contents,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _GenerateContentParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:generateContent'.format_map(request_url_dict)
      else:
        path = '{model}:generateContent'
    else:
      request_dict = _GenerateContentParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:generateContent'.format_map(request_url_dict)
      else:
        path = '{model}:generateContent'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request(
        'post', path, request_dict, http_options
    )

    if config is not None and getattr(
        config, 'should_return_http_response', None
    ):
      return_value = types.GenerateContentResponse(sdk_http_response=response)
      self._api_client._verify_response(return_value)
      return return_value

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _GenerateContentResponse_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _GenerateContentResponse_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.GenerateContentResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  def _generate_content_stream(
      self,
      *,
      model: str,
      contents: Union[types.ContentListUnion, types.ContentListUnionDict],
      config: Optional[types.GenerateContentConfigOrDict] = None,
  ) -> Iterator[types.GenerateContentResponse]:
    parameter_model = types._GenerateContentParameters(
        model=model,
        contents=contents,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _GenerateContentParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:streamGenerateContent?alt=sse'.format_map(
            request_url_dict
        )
      else:
        path = '{model}:streamGenerateContent?alt=sse'
    else:
      request_dict = _GenerateContentParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:streamGenerateContent?alt=sse'.format_map(
            request_url_dict
        )
      else:
        path = '{model}:streamGenerateContent?alt=sse'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    if config is not None and getattr(
        config, 'should_return_http_response', None
    ):
      raise ValueError(
          'Accessing the raw HTTP response is not supported in streaming'
          ' methods.'
      )

    for response in self._api_client.request_streamed(
        'post', path, request_dict, http_options
    ):

      response_dict = {} if not response.body else json.loads(response.body)

      if self._api_client.vertexai:
        response_dict = _GenerateContentResponse_from_vertex(
            response_dict, None, parameter_model
        )

      if not self._api_client.vertexai:
        response_dict = _GenerateContentResponse_from_mldev(
            response_dict, None, parameter_model
        )

      return_value = types.GenerateContentResponse._from_response(
          response=response_dict,
          kwargs={
              'config': {
                  'response_schema': getattr(
                      parameter_model.config, 'response_schema', None
                  ),
                  'response_json_schema': getattr(
                      parameter_model.config, 'response_json_schema', None
                  ),
                  'include_all_fields': getattr(
                      parameter_model.config, 'include_all_fields', None
                  ),
              }
          }
          if getattr(parameter_model, 'config', None)
          else {},
      )
      return_value.sdk_http_response = types.HttpResponse(
          headers=response.headers
      )
      self._api_client._verify_response(return_value)
      yield return_value

  def _embed_content(
      self,
      *,
      model: str,
      contents: Optional[
          Union[types.ContentListUnion, types.ContentListUnionDict]
      ] = None,
      content: Optional[
          Union[types.ContentUnion, types.ContentUnionDict]
      ] = None,
      embedding_api_type: Optional[types.EmbeddingApiType] = None,
      config: Optional[types.EmbedContentConfigOrDict] = None,
  ) -> types.EmbedContentResponse:
    """Calculates embeddings for the given contents. Only text is supported.

    Args:
      model (str): The model to use.
      contents (list[Content]): The contents to embed.
      config (EmbedContentConfig): Optional configuration for embeddings.

    Usage:

    .. code-block:: python

      embeddings = client.models.embed_content(
          model= 'text-embedding-004',
          contents=[
              'What is your name?',
              'What is your favorite color?',
          ],
          config={
              'output_dimensionality': 64
          },
      )
    """

    parameter_model = types._EmbedContentParametersPrivate(
        model=model,
        contents=contents,
        content=content,
        embedding_api_type=embedding_api_type,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _EmbedContentParametersPrivate_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      endpoint_url = '{model}:embedContent' if t.t_is_vertex_embed_content_model(parameter_model.model) else '{model}:predict'  # type: ignore[arg-type]
      if request_url_dict:
        path = endpoint_url.format_map(request_url_dict)
      else:
        path = endpoint_url
    else:
      request_dict = _EmbedContentParametersPrivate_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:batchEmbedContents'.format_map(request_url_dict)
      else:
        path = '{model}:batchEmbedContents'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _EmbedContentResponse_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _EmbedContentResponse_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.EmbedContentResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  def _generate_images(
      self,
      *,
      model: str,
      prompt: str,
      config: Optional[types.GenerateImagesConfigOrDict] = None,
  ) -> types.GenerateImagesResponse:
    """Private method for generating images."""

    parameter_model = types._GenerateImagesParameters(
        model=model,
        prompt=prompt,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _GenerateImagesParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predict'.format_map(request_url_dict)
      else:
        path = '{model}:predict'
    else:
      request_dict = _GenerateImagesParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predict'.format_map(request_url_dict)
      else:
        path = '{model}:predict'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _GenerateImagesResponse_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _GenerateImagesResponse_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.GenerateImagesResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  def _edit_image(
      self,
      *,
      model: str,
      prompt: str,
      reference_images: list[types._ReferenceImageAPIOrDict],
      config: Optional[types.EditImageConfigOrDict] = None,
  ) -> types.EditImageResponse:
    """Private method for editing an image."""

    parameter_model = types._EditImageParameters(
        model=model,
        prompt=prompt,
        reference_images=reference_images,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]
    if not self._api_client.vertexai:
      raise ValueError('This method is only supported in the Vertex AI client.')
    else:
      request_dict = _EditImageParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predict'.format_map(request_url_dict)
      else:
        path = '{model}:predict'

    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _EditImageResponse_from_vertex(
          response_dict, None, parameter_model
      )

    return_value = types.EditImageResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  def _upscale_image(
      self,
      *,
      model: str,
      image: types.ImageOrDict,
      upscale_factor: str,
      config: Optional[types._UpscaleImageAPIConfigOrDict] = None,
  ) -> types.UpscaleImageResponse:
    """Private method for upscaling an image."""

    parameter_model = types._UpscaleImageAPIParameters(
        model=model,
        image=image,
        upscale_factor=upscale_factor,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]
    if not self._api_client.vertexai:
      raise ValueError('This method is only supported in the Vertex AI client.')
    else:
      request_dict = _UpscaleImageAPIParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predict'.format_map(request_url_dict)
      else:
        path = '{model}:predict'

    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _UpscaleImageResponse_from_vertex(
          response_dict, None, parameter_model
      )

    return_value = types.UpscaleImageResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  def recontext_image(
      self,
      *,
      model: str,
      source: types.RecontextImageSourceOrDict,
      config: Optional[types.RecontextImageConfigOrDict] = None,
  ) -> types.RecontextImageResponse:
    """Recontextualizes an image.

    There is one type of recontextualization currently supported:
    1) Virtual Try-On: Generate images of persons modeling fashion products.

    Args:
      model (str): The model to use.
      source (RecontextImageSource): An object containing the source inputs
        (prompt, person_image, product_images) for image recontext. prompt is
        behind an allowlist. person_image is required. product_images is
        required. Only one product image is supported currently.
      config (RecontextImageConfig): Configuration for recontextualization.

    Usage:

      ```
      virtual_try_on_response = client.models.recontext_image(
          model="virtual-try-on-001",
          source=types.RecontextImageSource(
              person_image=types.Image.from_file(IMAGE1_FILE_PATH),
              product_images=[types.ProductImage.from_file(IMAGE2_FILE_PATH)],
          ),
          config=types.RecontextImageConfig(
              number_of_images=1,
          ),
      )
      image = virtual_try_on_response.generated_images[0].image
      ```
    """

    parameter_model = types._RecontextImageParameters(
        model=model,
        source=source,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]
    if not self._api_client.vertexai:
      raise ValueError('This method is only supported in the Vertex AI client.')
    else:
      request_dict = _RecontextImageParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predict'.format_map(request_url_dict)
      else:
        path = '{model}:predict'

    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _RecontextImageResponse_from_vertex(
          response_dict, None, parameter_model
      )

    return_value = types.RecontextImageResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )

    self._api_client._verify_response(return_value)
    return return_value

  def segment_image(
      self,
      *,
      model: str,
      source: types.SegmentImageSourceOrDict,
      config: Optional[types.SegmentImageConfigOrDict] = None,
  ) -> types.SegmentImageResponse:
    """Segments an image, creating a mask of a specified area.

    Args:
      model (str): The model to use.
      source (SegmentImageSource): An object containing the source inputs
        (prompt, image, scribble_image) for image segmentation. The prompt is
        required for prompt mode and semantic mode, disallowed for other modes.
        scribble_image is required for the interactive mode, disallowed for
        other modes.
      config (SegmentImageConfig): Configuration for segmentation.

    Usage:

      ```
      response = client.models.segment_image(
          model="image-segmentation-001",
          source=types.SegmentImageSource(
              image=types.Image.from_file(IMAGE_FILE_PATH),
          ),
      )

      mask_image = response.generated_masks[0].mask
      ```
    """

    parameter_model = types._SegmentImageParameters(
        model=model,
        source=source,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]
    if not self._api_client.vertexai:
      raise ValueError('This method is only supported in the Vertex AI client.')
    else:
      request_dict = _SegmentImageParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predict'.format_map(request_url_dict)
      else:
        path = '{model}:predict'

    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _SegmentImageResponse_from_vertex(
          response_dict, None, parameter_model
      )

    return_value = types.SegmentImageResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )

    self._api_client._verify_response(return_value)
    return return_value

  def get(
      self, *, model: str, config: Optional[types.GetModelConfigOrDict] = None
  ) -> types.Model:
    parameter_model = types._GetModelParameters(
        model=model,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _GetModelParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{name}'.format_map(request_url_dict)
      else:
        path = '{name}'
    else:
      request_dict = _GetModelParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{name}'.format_map(request_url_dict)
      else:
        path = '{name}'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request('get', path, request_dict, http_options)

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _Model_from_vertex(response_dict, None, parameter_model)

    if not self._api_client.vertexai:
      response_dict = _Model_from_mldev(response_dict, None, parameter_model)

    return_value = types.Model._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )

    self._api_client._verify_response(return_value)
    return return_value

  def _list(
      self, *, config: Optional[types.ListModelsConfigOrDict] = None
  ) -> types.ListModelsResponse:
    parameter_model = types._ListModelsParameters(
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _ListModelsParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{models_url}'.format_map(request_url_dict)
      else:
        path = '{models_url}'
    else:
      request_dict = _ListModelsParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{models_url}'.format_map(request_url_dict)
      else:
        path = '{models_url}'
    query_params = request_dict.get('_query')
    if query_params and query_params.get('filter'):
      query_param_filter = query_params.pop('filter')
      path = f'{path}?filter={query_param_filter}'
      if query_params:
        path += f'&{urlencode(query_params)}'
    elif query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request('get', path, request_dict, http_options)

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _ListModelsResponse_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _ListModelsResponse_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.ListModelsResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  def update(
      self,
      *,
      model: str,
      config: Optional[types.UpdateModelConfigOrDict] = None,
  ) -> types.Model:
    parameter_model = types._UpdateModelParameters(
        model=model,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _UpdateModelParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}'.format_map(request_url_dict)
      else:
        path = '{model}'
    else:
      request_dict = _UpdateModelParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{name}'.format_map(request_url_dict)
      else:
        path = '{name}'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request(
        'patch', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _Model_from_vertex(response_dict, None, parameter_model)

    if not self._api_client.vertexai:
      response_dict = _Model_from_mldev(response_dict, None, parameter_model)

    return_value = types.Model._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )

    self._api_client._verify_response(return_value)
    return return_value

  def delete(
      self,
      *,
      model: str,
      config: Optional[types.DeleteModelConfigOrDict] = None,
  ) -> types.DeleteModelResponse:
    parameter_model = types._DeleteModelParameters(
        model=model,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _DeleteModelParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{name}'.format_map(request_url_dict)
      else:
        path = '{name}'
    else:
      request_dict = _DeleteModelParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{name}'.format_map(request_url_dict)
      else:
        path = '{name}'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request(
        'delete', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _DeleteModelResponse_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _DeleteModelResponse_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.DeleteModelResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  def count_tokens(
      self,
      *,
      model: str,
      contents: Union[types.ContentListUnion, types.ContentListUnionDict],
      config: Optional[types.CountTokensConfigOrDict] = None,
  ) -> types.CountTokensResponse:
    """Counts the number of tokens in the given content.

    Multimodal input is supported for Gemini models.

    Args:
      model (str): The model to use for counting tokens.
      contents (list[types.Content]): The content to count tokens for.
      config (CountTokensConfig): The configuration for counting tokens.

    Usage:

    .. code-block:: python

      response = client.models.count_tokens(
          model='gemini-2.0-flash',
          contents='What is your name?',
      )
      print(response)
      # total_tokens=5 cached_content_token_count=None
    """

    parameter_model = types._CountTokensParameters(
        model=model,
        contents=contents,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _CountTokensParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:countTokens'.format_map(request_url_dict)
      else:
        path = '{model}:countTokens'
    else:
      request_dict = _CountTokensParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:countTokens'.format_map(request_url_dict)
      else:
        path = '{model}:countTokens'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _CountTokensResponse_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _CountTokensResponse_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.CountTokensResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  def compute_tokens(
      self,
      *,
      model: str,
      contents: Union[types.ContentListUnion, types.ContentListUnionDict],
      config: Optional[types.ComputeTokensConfigOrDict] = None,
  ) -> types.ComputeTokensResponse:
    """Given a list of contents, returns a corresponding TokensInfo containing the

    list of tokens and list of token ids.

    This method is not supported by the Gemini Developer API.

    Args:
      model (str): The model to use.
      contents (list[shared.Content]): The content to compute tokens for.

    Usage:

    .. code-block:: python

      response = client.models.compute_tokens(
          model='gemini-2.0-flash',
          contents='What is your name?',
      )
      print(response)
      # tokens_info=[TokensInfo(role='user', token_ids=['1841', ...],
      # tokens=[b'What', b' is', b' your', b' name', b'?'])]
    """

    parameter_model = types._ComputeTokensParameters(
        model=model,
        contents=contents,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]
    if not self._api_client.vertexai:
      raise ValueError('This method is only supported in the Vertex AI client.')
    else:
      request_dict = _ComputeTokensParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:computeTokens'.format_map(request_url_dict)
      else:
        path = '{model}:computeTokens'

    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _ComputeTokensResponse_from_vertex(
          response_dict, None, parameter_model
      )

    return_value = types.ComputeTokensResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  def _generate_videos(
      self,
      *,
      model: str,
      prompt: Optional[str] = None,
      image: Optional[types.ImageOrDict] = None,
      video: Optional[types.VideoOrDict] = None,
      source: Optional[types.GenerateVideosSourceOrDict] = None,
      config: Optional[types.GenerateVideosConfigOrDict] = None,
  ) -> types.GenerateVideosOperation:
    """Private method for generating videos."""

    parameter_model = types._GenerateVideosParameters(
        model=model,
        prompt=prompt,
        image=image,
        video=video,
        source=source,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _GenerateVideosParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predictLongRunning'.format_map(request_url_dict)
      else:
        path = '{model}:predictLongRunning'
    else:
      request_dict = _GenerateVideosParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predictLongRunning'.format_map(request_url_dict)
      else:
        path = '{model}:predictLongRunning'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = self._api_client.request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _GenerateVideosOperation_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _GenerateVideosOperation_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.GenerateVideosOperation._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )

    self._api_client._verify_response(return_value)
    return return_value

  def embed_content(
      self,
      *,
      model: str,
      contents: Union[types.ContentListUnion, types.ContentListUnionDict],
      config: Optional[types.EmbedContentConfigOrDict] = None,
  ) -> types.EmbedContentResponse:
    """Calculates embeddings for the given contents.

    Args:
      model (str): The model to use.
      contents (list[Content]): The contents to embed.
      config (EmbedContentConfig): Optional configuration for embeddings.

    Usage:

    .. code-block:: python

      embeddings = client.models.embed_content(
          model= 'text-embedding-004',
          contents=[
              'What is your name?',
              'What is your favorite color?',
          ],
          config={
              'output_dimensionality': 64
          },
      )

      multimodal_embeddings = client.models.embed_content(
          model='gemini-embedding-2-preview',
          contents=[
              types.Part.from_uri(
                  file_uri='gs://generativeai-downloads/images/scones.jpg',
                  mime_type='image/jpeg',
              ),
          ],
          config={
              'output_dimensionality': 64
          },
      )
    """
    if not self._api_client.vertexai:
      if 'gemini-embedding-2' in model:
        contents = t.t_contents(contents)  # type: ignore[assignment]
      return self._embed_content(model=model, contents=contents, config=config)

    if t.t_is_vertex_embed_content_model(model):
      normalized_contents = t.t_contents(contents)
      if len(normalized_contents) > 1:
        raise ValueError(
            'The embedContent API for this model only supports one content at a'
            ' time.'
        )
      return self._embed_content(
          model=model,
          contents=None,
          content=normalized_contents[0],
          embedding_api_type=types.EmbeddingApiType.EMBED_CONTENT,
          config=config,
      )
    else:
      return self._embed_content(
          model=model,
          content=None,
          contents=contents,
          embedding_api_type=types.EmbeddingApiType.PREDICT,
          config=config,
      )

  def generate_content(
      self,
      *,
      model: str,
      contents: types.ContentListUnionDict,
      config: Optional[types.GenerateContentConfigOrDict] = None,
  ) -> types.GenerateContentResponse:
    """Makes an API request to generate content using a model.

    For the `model` parameter, supported formats for Vertex AI API include:
    - The Gemini model ID, for example: 'gemini-2.0-flash'
    - The full resource name starts with 'projects/', for example:
      'projects/my-project-id/locations/us-central1/publishers/google/models/gemini-2.0-flash'
    - The partial resource name with 'publishers/', for example:
      'publishers/google/models/gemini-2.0-flash' or
    - `/` separated publisher and model name, for example:
      'google/gemini-2.0-flash'

    For the `model` parameter, supported formats for Gemini API include:
    - The Gemini model ID, for example: 'gemini-2.0-flash'
    - The model name starts with 'models/', for example:
      'models/gemini-2.0-flash'
    - For tuned models, the model name starts with 'tunedModels/',
      for example:
      'tunedModels/1234567890123456789'

    Some models support multimodal input and output.

    Built-in MCP support is an experimental feature.

    Usage:

    .. code-block:: python

      from google.genai import types
      from google import genai

      client = genai.Client(
          vertexai=True, project='my-project-id', location='us-central1'
      )

      response = client.models.generate_content(
        model='gemini-2.0-flash',
        contents='''What is a good name for a flower shop that specializes in
          selling bouquets of dried flowers?'''
      )
      print(response.text)
      # **Elegant & Classic:**
      # * The Dried Bloom
      # * Everlasting Florals
      # * Timeless Petals

      response = client.models.generate_content(
        model='gemini-2.0-flash',
        contents=[
          types.Part.from_text(text='What is shown in this image?'),
          types.Part.from_uri(file_uri='gs://generativeai-downloads/images/scones.jpg',
          mime_type='image/jpeg')
        ]
      )
      print(response.text)
      # The image shows a flat lay arrangement of freshly baked blueberry
      # scones.
    """

    incompatible_tools_indexes = (
        _extra_utils.find_afc_incompatible_tool_indexes(config)
    )
    parsed_config = _extra_utils.parse_config_for_mcp_usage(config)
    if (
        parsed_config
        and parsed_config.tools
        and _mcp_utils.has_mcp_session_usage(parsed_config.tools)
    ):
      raise errors.UnsupportedFunctionError(
          'MCP sessions are not supported in synchronous methods.'
      )
    if _extra_utils.should_disable_afc(parsed_config):
      return self._generate_content(
          model=model, contents=contents, config=parsed_config
      )
    if incompatible_tools_indexes:
      original_tools_length = 0
      if isinstance(config, types.GenerateContentConfig):
        if config.tools:
          original_tools_length = len(config.tools)
      elif isinstance(config, dict):
        tools = config.get('tools', [])
        if tools:
          original_tools_length = len(tools)
      if len(incompatible_tools_indexes) != original_tools_length:
        indices_str = ', '.join(map(str, incompatible_tools_indexes))
        logger.warning(
            'Tools at indices [%s] are not compatible with automatic function '
            'calling (AFC). AFC is disabled. If AFC is intended, please '
            'include python callables in the tool list, and do not include '
            'function declaration and MCP server in the tool list.',
            indices_str,
        )
      return self._generate_content(
          model=model, contents=contents, config=parsed_config
      )

    remaining_remote_calls_afc = _extra_utils.get_max_remote_calls_afc(
        parsed_config
    )
    logger.info(
        f'AFC is enabled with max remote calls: {remaining_remote_calls_afc}.'
    )
    automatic_function_calling_history: list[types.Content] = []
    response = types.GenerateContentResponse()
    i = 0
    while remaining_remote_calls_afc > 0:
      i += 1
      response = self._generate_content(
          model=model, contents=contents, config=parsed_config
      )

      function_map = _extra_utils.get_function_map(parsed_config)
      if not function_map:
        break
      if not response:
        break
      if (
          not response.candidates
          or not response.candidates[0].content
          or not response.candidates[0].content.parts
      ):
        break
      func_response_parts = _extra_utils.get_function_response_parts(
          response, function_map
      )
      if not func_response_parts:
        break
      logger.info(f'AFC remote call {i} is done.')
      remaining_remote_calls_afc -= 1
      if remaining_remote_calls_afc == 0:
        logger.info('Reached max remote calls for automatic function calling.')

      func_call_content = response.candidates[0].content
      func_response_content = types.Content(
          role='user',
          parts=func_response_parts,
      )
      contents = t.t_contents(contents)  # type: ignore[assignment]
      if not automatic_function_calling_history:
        automatic_function_calling_history.extend(contents)  # type: ignore[arg-type]
      if isinstance(contents, list):
        contents.append(func_call_content)  # type: ignore[arg-type]
        contents.append(func_response_content)  # type: ignore[arg-type]
      automatic_function_calling_history.append(func_call_content)
      automatic_function_calling_history.append(func_response_content)
    if (
        _extra_utils.should_append_afc_history(parsed_config)
        and response is not None
    ):
      response.automatic_function_calling_history = (
          automatic_function_calling_history
      )
    return response

  def generate_content_stream(
      self,
      *,
      model: str,
      contents: types.ContentListUnionDict,
      config: Optional[types.GenerateContentConfigOrDict] = None,
  ) -> Iterator[types.GenerateContentResponse]:
    """Makes an API request to generate content using a model and yields the model's response in chunks.

    For the `model` parameter, supported formats for Vertex AI API include:
    - The Gemini model ID, for example: 'gemini-2.0-flash'
    - The full resource name starts with 'projects/', for example:
      'projects/my-project-id/locations/us-central1/publishers/google/models/gemini-2.0-flash'
    - The partial resource name with 'publishers/', for example:
      'publishers/google/models/gemini-2.0-flash' or
    - `/` separated publisher and model name, for example:
      'google/gemini-2.0-flash'

    For the `model` parameter, supported formats for Gemini API include:
    - The Gemini model ID, for example: 'gemini-2.0-flash'
    - The model name starts with 'models/', for example:
      'models/gemini-2.0-flash'
    - For tuned models, the model name starts with 'tunedModels/',
      for example:
      'tunedModels/1234567890123456789'

    Some models support multimodal input and output.

    Built-in MCP support is an experimental feature.

    Usage:

    .. code-block:: python

      from google.genai import types
      from google import genai

      client = genai.Client(
          vertexai=True, project='my-project-id', location='us-central1'
      )

      for chunk in client.models.generate_content_stream(
        model='gemini-2.0-flash',
        contents='''What is a good name for a flower shop that specializes in
          selling bouquets of dried flowers?'''
      ):
        print(chunk.text)
      # **Elegant & Classic:**
      # * The Dried Bloom
      # * Everlasting Florals
      # * Timeless Petals

      for chunk in client.models.generate_content_stream(
        model='gemini-2.0-flash',
        contents=[
          types.Part.from_text('What is shown in this image?'),
          types.Part.from_uri('gs://generativeai-downloads/images/scones.jpg',
          'image/jpeg')
        ]
      ):
        print(chunk.text)
      # The image shows a flat lay arrangement of freshly baked blueberry
      # scones.
    """

    incompatible_tools_indexes = (
        _extra_utils.find_afc_incompatible_tool_indexes(config)
    )
    parsed_config = _extra_utils.parse_config_for_mcp_usage(config)
    if (
        parsed_config
        and parsed_config.tools
        and _mcp_utils.has_mcp_session_usage(parsed_config.tools)
    ):
      raise errors.UnsupportedFunctionError(
          'MCP sessions are not supported in synchronous methods.'
      )
    if _extra_utils.should_disable_afc(parsed_config):
      yield from self._generate_content_stream(
          model=model, contents=contents, config=parsed_config
      )
      return

    if incompatible_tools_indexes:
      original_tools_length = 0
      if isinstance(config, types.GenerateContentConfig):
        if config.tools:
          original_tools_length = len(config.tools)
      elif isinstance(config, dict):
        tools = config.get('tools', [])
        if tools:
          original_tools_length = len(tools)
      if len(incompatible_tools_indexes) != original_tools_length:
        indices_str = ', '.join(map(str, incompatible_tools_indexes))
        logger.warning(
            'Tools at indices [%s] are not compatible with automatic function '
            'calling. AFC will be disabled.',
            indices_str,
        )
      yield from self._generate_content_stream(
          model=model, contents=contents, config=parsed_config
      )
      return

    # With tool compatibility confirmed, validate that the configuration are
    # compatible with each other and raise an error if invalid.
    _extra_utils.raise_error_for_afc_incompatible_config(parsed_config)

    remaining_remote_calls_afc = _extra_utils.get_max_remote_calls_afc(
        parsed_config
    )
    logger.info(
        f'AFC is enabled with max remote calls: {remaining_remote_calls_afc}.'
    )
    automatic_function_calling_history: list[types.Content] = []
    chunk = None
    func_response_parts = None
    i = 0
    while remaining_remote_calls_afc > 0:
      i += 1
      response = self._generate_content_stream(
          model=model, contents=contents, config=parsed_config
      )

      function_map = _extra_utils.get_function_map(parsed_config)

      if i == 1:
        # First request gets a function call.
        # Then get function response parts.
        # Yield chunks only if there's no function response parts.
        for chunk in response:
          if not function_map:
            contents = _extra_utils.append_chunk_contents(contents, chunk)  # type: ignore[assignment]
            yield chunk
          else:
            if (
                not chunk.candidates
                or not chunk.candidates[0].content
                or not chunk.candidates[0].content.parts
            ):
              break
            func_response_parts = _extra_utils.get_function_response_parts(
                chunk, function_map
            )
            if not func_response_parts:
              contents = _extra_utils.append_chunk_contents(contents, chunk)  # type: ignore[assignment]
              yield chunk

      else:
        #  Second request and beyond, yield chunks.
        for chunk in response:
          if _extra_utils.should_append_afc_history(parsed_config):
            chunk.automatic_function_calling_history = (
                automatic_function_calling_history
            )
          contents = _extra_utils.append_chunk_contents(contents, chunk)  # type: ignore[assignment]
          yield chunk
        if (
            chunk is None
            or not chunk.candidates
            or not chunk.candidates[0].content
            or not chunk.candidates[0].content.parts
        ):
          break
        func_response_parts = _extra_utils.get_function_response_parts(
            chunk, function_map
        )

      if not function_map:
        break
      if not func_response_parts:
        break
      logger.info(f'AFC remote call {i} is done.')
      remaining_remote_calls_afc -= 1
      if remaining_remote_calls_afc == 0:
        logger.info('Reached max remote calls for automatic function calling.')

      # Append function response parts to contents for the next request.
      if chunk is not None and chunk.candidates is not None:
        func_call_content = chunk.candidates[0].content
        func_response_content = types.Content(
            role='user',
            parts=func_response_parts,
        )
        contents = t.t_contents(contents)  # type: ignore[assignment]
        if not automatic_function_calling_history:
          automatic_function_calling_history.extend(contents)  # type: ignore[arg-type]
        if isinstance(contents, list) and func_call_content is not None:
          contents.append(func_call_content)  # type: ignore[arg-type]
          contents.append(func_response_content)  # type: ignore[arg-type]
        if func_call_content is not None:
          automatic_function_calling_history.append(func_call_content)
        automatic_function_calling_history.append(func_response_content)

  def generate_images(
      self,
      *,
      model: str,
      prompt: str,
      config: Optional[types.GenerateImagesConfigOrDict] = None,
  ) -> types.GenerateImagesResponse:
    """Generates images based on a text description and configuration.

    Args:
      model (str): The model to use.
      prompt (str): A text description of the images to generate.
      config (GenerateImagesConfig): Configuration for generation.

    Usage:

    .. code-block:: python

      response = client.models.generate_images(
        model='imagen-3.0-generate-002',
        prompt='Man with a dog',
        config=types.GenerateImagesConfig(
            number_of_images= 1,
            include_rai_reason= True,
        )
      )
      response.generated_images[0].image.show()
      # Shows a man with a dog.
    """
    api_response = self._generate_images(
        model=model,
        prompt=prompt,
        config=config,
    )
    positive_prompt_safety_attributes = None
    generated_images = []
    if not api_response or not api_response.generated_images:
      return api_response

    for generated_image in api_response.generated_images:
      if (
          generated_image.safety_attributes
          and generated_image.safety_attributes.content_type
          == 'Positive Prompt'
      ):
        positive_prompt_safety_attributes = generated_image.safety_attributes
      else:
        generated_images.append(generated_image)

    response = types.GenerateImagesResponse(
        generated_images=generated_images,
        positive_prompt_safety_attributes=positive_prompt_safety_attributes,
    )
    return response

  def edit_image(
      self,
      *,
      model: str,
      prompt: str,
      reference_images: list[types._ReferenceImageAPIOrDict],
      config: Optional[types.EditImageConfigOrDict] = None,
  ) -> types.EditImageResponse:
    """Edits an image based on a text description and configuration.

    Args:
      model (str): The model to use.
      prompt (str): A text description of the edit to apply to the image.
        reference_images (list[Union[RawReferenceImage, MaskReferenceImage,
        ControlReferenceImage, StyleReferenceImage, SubjectReferenceImage]): The
        reference images for editing.
      config (EditImageConfig): Configuration for editing.

    Usage:

    .. code-block:: python

      from google.genai.types import RawReferenceImage, MaskReferenceImage

      raw_ref_image = RawReferenceImage(
        reference_id=1,
        reference_image=types.Image.from_file(IMAGE_FILE_PATH),
      )

      mask_ref_image = MaskReferenceImage(
        reference_id=2,
        config=types.MaskReferenceConfig(
            mask_mode='MASK_MODE_FOREGROUND',
            mask_dilation=0.06,
        ),
      )
      response = client.models.edit_image(
        model='imagen-3.0-capability-001',
        prompt='man with dog',
        reference_images=[raw_ref_image, mask_ref_image],
        config=types.EditImageConfig(
            edit_mode= "EDIT_MODE_INPAINT_INSERTION",
            number_of_images= 1,
            include_rai_reason= True,
        )
      )
      response.generated_images[0].image.show()
      # Shows a man with a dog instead of a cat.
    """
    return self._edit_image(
        model=model,
        prompt=prompt,
        reference_images=reference_images,
        config=config,
    )

  def upscale_image(
      self,
      *,
      model: str,
      image: types.ImageOrDict,
      upscale_factor: str,
      config: Optional[types.UpscaleImageConfigOrDict] = None,
  ) -> types.UpscaleImageResponse:
    """Makes an API request to upscale a provided image.

    Args:
      model (str): The model to use.
      image (Image): The input image for upscaling.
      upscale_factor (str): The factor to upscale the image (x2 or x4).
      config (UpscaleImageConfig): Configuration for upscaling.

    Usage:

    .. code-block:: python

      from google.genai.types import Image

      IMAGE_FILE_PATH="my-image.png"
      response=client.models.upscale_image(
          model='imagen-3.0-generate-001',
          image=types.Image.from_file(IMAGE_FILE_PATH),
          upscale_factor='x2',
      )
      response.generated_images[0].image.show()
      # Opens my-image.png which is upscaled by a factor of 2.
    """

    # Validate config.
    types.UpscaleImageParameters(
        model=model,
        image=image,
        upscale_factor=upscale_factor,
        config=config,
    )

    # Convert to API config.
    config = config or {}
    if isinstance(config, types.UpscaleImageConfig):
      config_dct = config.model_dump()
    else:
      config_dct = dict(config)
    api_config = types._UpscaleImageAPIConfigDict(
        http_options=config_dct.get('http_options', None),
        output_gcs_uri=config_dct.get('output_gcs_uri', None),
        safety_filter_level=config_dct.get('safety_filter_level', None),
        person_generation=config_dct.get('person_generation', None),
        include_rai_reason=config_dct.get('include_rai_reason', None),
        output_mime_type=config_dct.get('output_mime_type', None),
        output_compression_quality=config_dct.get(
            'output_compression_quality', None
        ),
        enhance_input_image=config_dct.get('enhance_input_image', None),
        image_preservation_factor=config_dct.get(
            'image_preservation_factor', None
        ),
        labels=config_dct.get('labels', None),
    )  # pylint: disable=protected-access

    # Provide default values through API config.
    api_config['mode'] = 'upscale'
    api_config['number_of_images'] = 1

    return self._upscale_image(
        model=model,
        image=image,
        upscale_factor=upscale_factor,
        config=api_config,
    )

  def generate_videos(
      self,
      *,
      model: str,
      prompt: Optional[str] = None,
      image: Optional[types.ImageOrDict] = None,
      video: Optional[types.VideoOrDict] = None,
      source: Optional[types.GenerateVideosSourceOrDict] = None,
      config: Optional[types.GenerateVideosConfigOrDict] = None,
  ) -> types.GenerateVideosOperation:
    """Generates videos based on an input (text, image, or video) and configuration.

    The following use cases are supported:
    1. Text to video generation.
    2a. Image to video generation (additional text prompt is optional).
    2b. Image to video generation with frame interpolation (specify last_frame
    in config).
    3. Video extension (additional text prompt is optional)

    Args:
      model: The model to use.
      prompt: The text prompt for generating the videos. Optional for image to
        video and video extension use cases. This argument is deprecated, please
        use source instead.
      image: The input image for generating the videos. Optional if prompt is
        provided. This argument is deprecated, please use source instead.
      video: The input video for video extension use cases. Optional if prompt
        or image is provided. This argument is deprecated, please use source
        instead.
      source: The input source for generating the videos (prompt, image, and/or
        video)
      config: Configuration for generation.

    Usage:

      ```
      operation = client.models.generate_videos(
          model="veo-2.0-generate-001",
          source=types.GenerateVideosSource(
              prompt="A neon hologram of a cat driving at top speed",
          ),
      )
      while not operation.done:
          time.sleep(10)
          operation = client.operations.get(operation)

      operation.result.generated_videos[0].video.uri
      ```
    """
    if (prompt or image or video) and source:
      raise ValueError(
          'Source and prompt/image/video are mutually exclusive.'
          + ' Please only use source.'
      )
    # Gemini Developer API does not support video bytes.
    video_dct: dict[str, Any] = {}
    if not self._api_client.vertexai and video:
      if isinstance(video, types.Video):
        video_dct = video.model_dump()
      else:
        video_dct = dict(video)

      if video_dct.get('uri') and video_dct.get('video_bytes'):
        video = types.Video(
            uri=video_dct.get('uri'), mime_type=video_dct.get('mime_type')
        )
    elif not self._api_client.vertexai and source:
      if isinstance(source, types.GenerateVideosSource):
        source_dct = source.model_dump()
        video_dct = source_dct.get('video', {})
      else:
        source_dct = dict(source)
        if isinstance(source_dct.get('video'), types.Video):
          video_obj: types.Video = source_dct.get('video', types.Video())
          video_dct = video_obj.model_dump()
      if video_dct and video_dct.get('uri') and video_dct.get('video_bytes'):
        source = types.GenerateVideosSource(
            prompt=source_dct.get('prompt'),
            image=source_dct.get('image'),
            video=types.Video(
                uri=video_dct.get('uri'),
                mime_type=video_dct.get('mime_type'),
            ),
        )
    return self._generate_videos(
        model=model,
        prompt=prompt,
        image=image,
        video=video,
        source=source,
        config=config,
    )

  def list(
      self,
      *,
      config: Optional[types.ListModelsConfigOrDict] = None,
  ) -> Pager[types.Model]:
    """Makes an API request to list the available models.

    If `query_base` is set to True in the config or not set (default), the
    API will return all available base models. If set to False, it will return
    all tuned models.

    Args:
      config (ListModelsConfigOrDict): Configuration for retrieving models.

    Usage:

    .. code-block:: python

      response=client.models.list(config={'page_size': 5})
      print(response.page)
      # [Model(name='projects/./locations/./models/123', display_name='my_model'

      response=client.models.list(config={'page_size': 5, 'query_base': True})
      print(response.page)
      # [Model(name='publishers/google/models/gemini-2.0-flash-exp' ...
    """

    config = (
        types._ListModelsParameters(config=config).config
        or types.ListModelsConfig()
    )
    if config.query_base is None:
      config.query_base = True
    if self._api_client.vertexai:
      config = config.copy()
      if not config.query_base:
        # Filter for tuning jobs artifacts by labels.
        filter_value = config.filter
        config.filter = (
            filter_value + '&filter=labels.tune-type:*'
            if filter_value
            else 'labels.tune-type:*'
        )
    return Pager(
        'models',
        self._list,
        self._list(config=config),
        config,
    )


class AsyncModels(_api_module.BaseModule):

  async def _generate_content(
      self,
      *,
      model: str,
      contents: Union[types.ContentListUnion, types.ContentListUnionDict],
      config: Optional[types.GenerateContentConfigOrDict] = None,
  ) -> types.GenerateContentResponse:
    parameter_model = types._GenerateContentParameters(
        model=model,
        contents=contents,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _GenerateContentParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:generateContent'.format_map(request_url_dict)
      else:
        path = '{model}:generateContent'
    else:
      request_dict = _GenerateContentParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:generateContent'.format_map(request_url_dict)
      else:
        path = '{model}:generateContent'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'post', path, request_dict, http_options
    )

    if config is not None and getattr(
        config, 'should_return_http_response', None
    ):
      return_value = types.GenerateContentResponse(sdk_http_response=response)
      self._api_client._verify_response(return_value)
      return return_value

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _GenerateContentResponse_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _GenerateContentResponse_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.GenerateContentResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  async def _generate_content_stream(
      self,
      *,
      model: str,
      contents: Union[types.ContentListUnion, types.ContentListUnionDict],
      config: Optional[types.GenerateContentConfigOrDict] = None,
  ) -> Awaitable[AsyncIterator[types.GenerateContentResponse]]:
    parameter_model = types._GenerateContentParameters(
        model=model,
        contents=contents,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _GenerateContentParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:streamGenerateContent?alt=sse'.format_map(
            request_url_dict
        )
      else:
        path = '{model}:streamGenerateContent?alt=sse'
    else:
      request_dict = _GenerateContentParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:streamGenerateContent?alt=sse'.format_map(
            request_url_dict
        )
      else:
        path = '{model}:streamGenerateContent?alt=sse'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    if config is not None and getattr(
        config, 'should_return_http_response', None
    ):
      raise ValueError(
          'Accessing the raw HTTP response is not supported in streaming'
          ' methods.'
      )

    response_stream = await self._api_client.async_request_streamed(
        'post', path, request_dict, http_options
    )

    async def async_generator():  # type: ignore[no-untyped-def]
      async for response in response_stream:

        response_dict = {} if not response.body else json.loads(response.body)

        if self._api_client.vertexai:
          response_dict = _GenerateContentResponse_from_vertex(
              response_dict, None, parameter_model
          )

        if not self._api_client.vertexai:
          response_dict = _GenerateContentResponse_from_mldev(
              response_dict, None, parameter_model
          )

        return_value = types.GenerateContentResponse._from_response(
            response=response_dict,
            kwargs={
                'config': {
                    'response_schema': getattr(
                        parameter_model.config, 'response_schema', None
                    ),
                    'response_json_schema': getattr(
                        parameter_model.config, 'response_json_schema', None
                    ),
                    'include_all_fields': getattr(
                        parameter_model.config, 'include_all_fields', None
                    ),
                }
            }
            if getattr(parameter_model, 'config', None)
            else {},
        )
        return_value.sdk_http_response = types.HttpResponse(
            headers=response.headers
        )
        self._api_client._verify_response(return_value)
        yield return_value

    return async_generator()  # type: ignore[no-untyped-call, no-any-return]

  async def _embed_content(
      self,
      *,
      model: str,
      contents: Optional[
          Union[types.ContentListUnion, types.ContentListUnionDict]
      ] = None,
      content: Optional[
          Union[types.ContentUnion, types.ContentUnionDict]
      ] = None,
      embedding_api_type: Optional[types.EmbeddingApiType] = None,
      config: Optional[types.EmbedContentConfigOrDict] = None,
  ) -> types.EmbedContentResponse:
    """Calculates embeddings for the given contents. Only text is supported.

    Args:
      model (str): The model to use.
      contents (list[Content]): The contents to embed.
      config (EmbedContentConfig): Optional configuration for embeddings.

    Usage:

    .. code-block:: python

      embeddings = await client.aio.models.embed_content(
          model= 'text-embedding-004',
          contents=[
              'What is your name?',
              'What is your favorite color?',
          ],
          config={
              'output_dimensionality': 64
          },
      )
    """

    parameter_model = types._EmbedContentParametersPrivate(
        model=model,
        contents=contents,
        content=content,
        embedding_api_type=embedding_api_type,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _EmbedContentParametersPrivate_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      endpoint_url = '{model}:embedContent' if t.t_is_vertex_embed_content_model(parameter_model.model) else '{model}:predict'  # type: ignore[arg-type]
      if request_url_dict:
        path = endpoint_url.format_map(request_url_dict)
      else:
        path = endpoint_url
    else:
      request_dict = _EmbedContentParametersPrivate_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:batchEmbedContents'.format_map(request_url_dict)
      else:
        path = '{model}:batchEmbedContents'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _EmbedContentResponse_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _EmbedContentResponse_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.EmbedContentResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  async def _generate_images(
      self,
      *,
      model: str,
      prompt: str,
      config: Optional[types.GenerateImagesConfigOrDict] = None,
  ) -> types.GenerateImagesResponse:
    """Private method for generating images asynchronously."""

    parameter_model = types._GenerateImagesParameters(
        model=model,
        prompt=prompt,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _GenerateImagesParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predict'.format_map(request_url_dict)
      else:
        path = '{model}:predict'
    else:
      request_dict = _GenerateImagesParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predict'.format_map(request_url_dict)
      else:
        path = '{model}:predict'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _GenerateImagesResponse_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _GenerateImagesResponse_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.GenerateImagesResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  async def _edit_image(
      self,
      *,
      model: str,
      prompt: str,
      reference_images: list[types._ReferenceImageAPIOrDict],
      config: Optional[types.EditImageConfigOrDict] = None,
  ) -> types.EditImageResponse:
    """Private method for editing an image asynchronously."""

    parameter_model = types._EditImageParameters(
        model=model,
        prompt=prompt,
        reference_images=reference_images,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]
    if not self._api_client.vertexai:
      raise ValueError('This method is only supported in the Vertex AI client.')
    else:
      request_dict = _EditImageParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predict'.format_map(request_url_dict)
      else:
        path = '{model}:predict'

    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _EditImageResponse_from_vertex(
          response_dict, None, parameter_model
      )

    return_value = types.EditImageResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  async def _upscale_image(
      self,
      *,
      model: str,
      image: types.ImageOrDict,
      upscale_factor: str,
      config: Optional[types._UpscaleImageAPIConfigOrDict] = None,
  ) -> types.UpscaleImageResponse:
    """Private method for upscaling an image asynchronously."""

    parameter_model = types._UpscaleImageAPIParameters(
        model=model,
        image=image,
        upscale_factor=upscale_factor,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]
    if not self._api_client.vertexai:
      raise ValueError('This method is only supported in the Vertex AI client.')
    else:
      request_dict = _UpscaleImageAPIParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predict'.format_map(request_url_dict)
      else:
        path = '{model}:predict'

    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _UpscaleImageResponse_from_vertex(
          response_dict, None, parameter_model
      )

    return_value = types.UpscaleImageResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  async def recontext_image(
      self,
      *,
      model: str,
      source: types.RecontextImageSourceOrDict,
      config: Optional[types.RecontextImageConfigOrDict] = None,
  ) -> types.RecontextImageResponse:
    """Recontextualizes an image.

    There is one type of recontextualization currently supported:
    1) Virtual Try-On: Generate images of persons modeling fashion products.

    Args:
      model (str): The model to use.
      source (RecontextImageSource): An object containing the source inputs
        (prompt, person_image, product_images) for image recontext. prompt is
        behind an allowlist. person_image is required. product_images is
        required. Only one product image is supported currently.
      config (RecontextImageConfig): Configuration for recontextualization.

    Usage:

      ```
      virtual_try_on_response = await client.aio.models.recontext_image(
          model="virtual-try-on-001",
          source=types.RecontextImageSource(
              person_image=types.Image.from_file(IMAGE1_FILE_PATH),
              product_images=[types.ProductImage.from_file(IMAGE2_FILE_PATH)],
          ),
          config=types.RecontextImageConfig(
              number_of_images=1,
          ),
      )
      image = virtual_try_on_response.generated_images[0].image
      ```
    """

    parameter_model = types._RecontextImageParameters(
        model=model,
        source=source,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]
    if not self._api_client.vertexai:
      raise ValueError('This method is only supported in the Vertex AI client.')
    else:
      request_dict = _RecontextImageParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predict'.format_map(request_url_dict)
      else:
        path = '{model}:predict'

    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _RecontextImageResponse_from_vertex(
          response_dict, None, parameter_model
      )

    return_value = types.RecontextImageResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )

    self._api_client._verify_response(return_value)
    return return_value

  async def segment_image(
      self,
      *,
      model: str,
      source: types.SegmentImageSourceOrDict,
      config: Optional[types.SegmentImageConfigOrDict] = None,
  ) -> types.SegmentImageResponse:
    """Segments an image, creating a mask of a specified area.

    Args:
      model (str): The model to use.
      source (SegmentImageSource): An object containing the source inputs
        (prompt, image, scribble_image) for image segmentation. The prompt is
        required for prompt mode and semantic mode, disallowed for other modes.
        scribble_image is required for the interactive mode, disallowed for
        other modes.
      config (SegmentImageConfig): Configuration for segmentation.

    Usage:

      ```
      response = client.models.segment_image(
          model="image-segmentation-001",
          source=types.SegmentImageSource(
              image=types.Image.from_file(IMAGE_FILE_PATH),
          ),
          config=types.SegmentImageConfig(
              mode=types.SegmentMode.foreground,
          ),
      )

      mask_image = response.generated_masks[0].mask
      ```
    """

    parameter_model = types._SegmentImageParameters(
        model=model,
        source=source,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]
    if not self._api_client.vertexai:
      raise ValueError('This method is only supported in the Vertex AI client.')
    else:
      request_dict = _SegmentImageParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predict'.format_map(request_url_dict)
      else:
        path = '{model}:predict'

    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _SegmentImageResponse_from_vertex(
          response_dict, None, parameter_model
      )

    return_value = types.SegmentImageResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )

    self._api_client._verify_response(return_value)
    return return_value

  async def get(
      self, *, model: str, config: Optional[types.GetModelConfigOrDict] = None
  ) -> types.Model:
    parameter_model = types._GetModelParameters(
        model=model,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _GetModelParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{name}'.format_map(request_url_dict)
      else:
        path = '{name}'
    else:
      request_dict = _GetModelParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{name}'.format_map(request_url_dict)
      else:
        path = '{name}'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'get', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _Model_from_vertex(response_dict, None, parameter_model)

    if not self._api_client.vertexai:
      response_dict = _Model_from_mldev(response_dict, None, parameter_model)

    return_value = types.Model._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )

    self._api_client._verify_response(return_value)
    return return_value

  async def _list(
      self, *, config: Optional[types.ListModelsConfigOrDict] = None
  ) -> types.ListModelsResponse:
    parameter_model = types._ListModelsParameters(
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _ListModelsParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{models_url}'.format_map(request_url_dict)
      else:
        path = '{models_url}'
    else:
      request_dict = _ListModelsParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{models_url}'.format_map(request_url_dict)
      else:
        path = '{models_url}'
    query_params = request_dict.get('_query')
    if query_params and query_params.get('filter'):
      query_param_filter = query_params.pop('filter')
      path = f'{path}?filter={query_param_filter}'
      if query_params:
        path += f'&{urlencode(query_params)}'
    elif query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'get', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _ListModelsResponse_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _ListModelsResponse_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.ListModelsResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  async def update(
      self,
      *,
      model: str,
      config: Optional[types.UpdateModelConfigOrDict] = None,
  ) -> types.Model:
    parameter_model = types._UpdateModelParameters(
        model=model,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _UpdateModelParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}'.format_map(request_url_dict)
      else:
        path = '{model}'
    else:
      request_dict = _UpdateModelParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{name}'.format_map(request_url_dict)
      else:
        path = '{name}'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'patch', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _Model_from_vertex(response_dict, None, parameter_model)

    if not self._api_client.vertexai:
      response_dict = _Model_from_mldev(response_dict, None, parameter_model)

    return_value = types.Model._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )

    self._api_client._verify_response(return_value)
    return return_value

  async def delete(
      self,
      *,
      model: str,
      config: Optional[types.DeleteModelConfigOrDict] = None,
  ) -> types.DeleteModelResponse:
    parameter_model = types._DeleteModelParameters(
        model=model,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _DeleteModelParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{name}'.format_map(request_url_dict)
      else:
        path = '{name}'
    else:
      request_dict = _DeleteModelParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{name}'.format_map(request_url_dict)
      else:
        path = '{name}'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'delete', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _DeleteModelResponse_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _DeleteModelResponse_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.DeleteModelResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  async def count_tokens(
      self,
      *,
      model: str,
      contents: Union[types.ContentListUnion, types.ContentListUnionDict],
      config: Optional[types.CountTokensConfigOrDict] = None,
  ) -> types.CountTokensResponse:
    """Counts the number of tokens in the given content.

    Multimodal input is supported for Gemini models.

    Args:
      model (str): The model to use for counting tokens.
      contents (list[types.Content]): The content to count tokens for.
      config (CountTokensConfig): The configuration for counting tokens.

    Usage:

    .. code-block:: python

      response = await client.aio.models.count_tokens(
          model='gemini-2.0-flash',
          contents='What is your name?',
      )
      print(response)
      # total_tokens=5 cached_content_token_count=None
    """

    parameter_model = types._CountTokensParameters(
        model=model,
        contents=contents,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _CountTokensParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:countTokens'.format_map(request_url_dict)
      else:
        path = '{model}:countTokens'
    else:
      request_dict = _CountTokensParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:countTokens'.format_map(request_url_dict)
      else:
        path = '{model}:countTokens'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _CountTokensResponse_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _CountTokensResponse_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.CountTokensResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  async def compute_tokens(
      self,
      *,
      model: str,
      contents: Union[types.ContentListUnion, types.ContentListUnionDict],
      config: Optional[types.ComputeTokensConfigOrDict] = None,
  ) -> types.ComputeTokensResponse:
    """Given a list of contents, returns a corresponding TokensInfo containing the

    list of tokens and list of token ids.


    Args:
      model (str): The model to use.
      contents (list[shared.Content]): The content to compute tokens for.

    Usage:

    .. code-block:: python

      response = await client.aio.models.compute_tokens(
          model='gemini-2.0-flash',
          contents='What is your name?',
      )
      print(response)
      # tokens_info=[TokensInfo(role='user', token_ids=['1841', ...],
      # tokens=[b'What', b' is', b' your', b' name', b'?'])]
    """

    parameter_model = types._ComputeTokensParameters(
        model=model,
        contents=contents,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]
    if not self._api_client.vertexai:
      raise ValueError('This method is only supported in the Vertex AI client.')
    else:
      request_dict = _ComputeTokensParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:computeTokens'.format_map(request_url_dict)
      else:
        path = '{model}:computeTokens'

    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _ComputeTokensResponse_from_vertex(
          response_dict, None, parameter_model
      )

    return_value = types.ComputeTokensResponse._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )
    return_value.sdk_http_response = types.HttpResponse(
        headers=response.headers
    )
    self._api_client._verify_response(return_value)
    return return_value

  async def _generate_videos(
      self,
      *,
      model: str,
      prompt: Optional[str] = None,
      image: Optional[types.ImageOrDict] = None,
      video: Optional[types.VideoOrDict] = None,
      source: Optional[types.GenerateVideosSourceOrDict] = None,
      config: Optional[types.GenerateVideosConfigOrDict] = None,
  ) -> types.GenerateVideosOperation:
    """Private method for generating videos asynchronously."""

    parameter_model = types._GenerateVideosParameters(
        model=model,
        prompt=prompt,
        image=image,
        video=video,
        source=source,
        config=config,
    )

    request_url_dict: Optional[dict[str, str]]

    if self._api_client.vertexai:
      request_dict = _GenerateVideosParameters_to_vertex(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predictLongRunning'.format_map(request_url_dict)
      else:
        path = '{model}:predictLongRunning'
    else:
      request_dict = _GenerateVideosParameters_to_mldev(
          self._api_client, parameter_model, None, parameter_model
      )
      request_url_dict = request_dict.get('_url')
      if request_url_dict:
        path = '{model}:predictLongRunning'.format_map(request_url_dict)
      else:
        path = '{model}:predictLongRunning'
    query_params = request_dict.get('_query')
    if query_params:
      path = f'{path}?{urlencode(query_params)}'
    # TODO: remove the hack that pops config.
    request_dict.pop('config', None)

    http_options: Optional[types.HttpOptions] = None
    if (
        parameter_model.config is not None
        and parameter_model.config.http_options is not None
    ):
      http_options = parameter_model.config.http_options

    request_dict = _common.convert_to_dict(request_dict)
    request_dict = _common.encode_unserializable_types(request_dict)

    response = await self._api_client.async_request(
        'post', path, request_dict, http_options
    )

    response_dict = {} if not response.body else json.loads(response.body)

    if self._api_client.vertexai:
      response_dict = _GenerateVideosOperation_from_vertex(
          response_dict, None, parameter_model
      )

    if not self._api_client.vertexai:
      response_dict = _GenerateVideosOperation_from_mldev(
          response_dict, None, parameter_model
      )

    return_value = types.GenerateVideosOperation._from_response(
        response=response_dict,
        kwargs={
            'config': {
                'response_schema': getattr(
                    parameter_model.config, 'response_schema', None
                ),
                'response_json_schema': getattr(
                    parameter_model.config, 'response_json_schema', None
                ),
                'include_all_fields': getattr(
                    parameter_model.config, 'include_all_fields', None
                ),
            }
        }
        if getattr(parameter_model, 'config', None)
        else {},
    )

    self._api_client._verify_response(return_value)
    return return_value

  async def generate_content(
      self,
      *,
      model: str,
      contents: Union[types.ContentListUnion, types.ContentListUnionDict],
      config: Optional[types.GenerateContentConfigOrDict] = None,
  ) -> types.GenerateContentResponse:
    """Makes an API request to generate content using a model.

    Some models support multimodal input and output.

    Built-in MCP support is an experimental feature.

    Usage:

    .. code-block:: python

      from google.genai import types
      from google import genai

      client = genai.Client(
          vertexai=True, project='my-project-id', location='us-central1'
      )

      response = await client.aio.models.generate_content(
          model='gemini-2.0-flash',
          contents='User input: I like bagels. Answer:',
          config=types.GenerateContentConfig(
              system_instruction=
                [
                  'You are a helpful language translator.',
                  'Your mission is to translate text in English to French.'
                ]
          ),
      )
      print(response.text)
      # J'aime les bagels.
    """
    # Retrieve and cache any MCP sessions if provided.
    incompatible_tools_indexes = (
        _extra_utils.find_afc_incompatible_tool_indexes(config)
    )
    parsed_config, mcp_to_genai_tool_adapters = (
        await _extra_utils.parse_config_for_mcp_sessions(config)
    )
    if _extra_utils.should_disable_afc(parsed_config):
      return await self._generate_content(
          model=model, contents=contents, config=parsed_config
      )
    if incompatible_tools_indexes:
      original_tools_length = 0
      if isinstance(config, types.GenerateContentConfig):
        if config.tools:
          original_tools_length = len(config.tools)
      elif isinstance(config, dict):
        tools = config.get('tools', [])
        if tools:
          original_tools_length = len(tools)
      if len(incompatible_tools_indexes) != original_tools_length:
        indices_str = ', '.join(map(str, incompatible_tools_indexes))
        logger.warning(
            'Tools at indices [%s] are not compatible with automatic function '
            'calling (AFC). AFC is disabled. If AFC is intended, please '
            'include python callables in the tool list, and do not include '
            'function declaration and MCP server in the tool list.',
            indices_str,
        )
      return await self._generate_content(
          model=model, contents=contents, config=parsed_config
      )
    remaining_remote_calls_afc = _extra_utils.get_max_remote_calls_afc(
        parsed_config
    )
    logger.info(
        f'AFC is enabled with max remote calls: {remaining_remote_calls_afc}.'
    )
    automatic_function_calling_history: list[types.Content] = []
    response = types.GenerateContentResponse()
    while remaining_remote_calls_afc > 0:
      response = await self._generate_content(
          model=model, contents=contents, config=parsed_config
      )
      remaining_remote_calls_afc -= 1
      if remaining_remote_calls_afc == 0:
        logger.info('Reached max remote calls for automatic function calling.')

      function_map = _extra_utils.get_function_map(
          parsed_config, mcp_to_genai_tool_adapters, is_caller_method_async=True
      )
      if not function_map:
        break
      if not response:
        break
      if (
          not response.candidates
          or not response.candidates[0].content
          or not response.candidates[0].content.parts
      ):
        break
      func_response_parts = (
          await _extra_utils.get_function_response_parts_async(
              response, function_map
          )
      )
      if not func_response_parts:
        break
      func_call_content = response.candidates[0].content
      func_response_content = types.Content(
          role='user',
          parts=func_response_parts,
      )
      contents = t.t_contents(contents)  # type: ignore[assignment]
      if not automatic_function_calling_history:
        automatic_function_calling_history.extend(contents)  # type: ignore[arg-type]
      if isinstance(contents, list):
        contents.append(func_call_content)  # type: ignore[arg-type]
        contents.append(func_response_content)  # type: ignore[arg-type]
      automatic_function_calling_history.append(func_call_content)
      automatic_function_calling_history.append(func_response_content)

    if (
        _extra_utils.should_append_afc_history(parsed_config)
        and response is not None
    ):
      response.automatic_function_calling_history = (
          automatic_function_calling_history
      )
    return response

  async def generate_content_stream(
      self,
      *,
      model: str,
      contents: Union[types.ContentListUnion, types.ContentListUnionDict],
      config: Optional[types.GenerateContentConfigOrDict] = None,
  ) -> AsyncIterator[types.GenerateContentResponse]:
    """Makes an API request to generate content using a model and yields the model's response in chunks.

    For the `model` parameter, supported formats for Vertex AI API include:
    - The Gemini model ID, for example: 'gemini-2.0-flash'
    - The full resource name starts with 'projects/', for example:
      'projects/my-project-id/locations/us-central1/publishers/google/models/gemini-2.0-flash'
    - The partial resource name with 'publishers/', for example:
      'publishers/google/models/gemini-2.0-flash' or
    - `/` separated publisher and model name, for example:
      'google/gemini-2.0-flash'

    For the `model` parameter, supported formats for Gemini API include:
    - The Gemini model ID, for example: 'gemini-2.0-flash'
    - The model name starts with 'models/', for example:
      'models/gemini-2.0-flash'
    - For tuned models, the model name starts with 'tunedModels/',
      for example:
      'tunedModels/1234567890123456789'

    Some models support multimodal input and output.

    Built-in MCP support is an experimental feature.

    Usage:

    .. code-block:: python

      from google.genai import types
      from google import genai

      client = genai.Client(
          vertexai=True, project='my-project-id', location='us-central1'
      )

      async for chunk in await client.aio.models.generate_content_stream(
        model='gemini-2.0-flash',
        contents='''What is a good name for a flower shop that specializes in
          selling bouquets of dried flowers?'''
      ):
        print(chunk.text)
      # **Elegant & Classic:**
      # * The Dried Bloom
      # * Everlasting Florals
      # * Timeless Petals

      async for chunk in await client.aio.models.generate_content_stream(
        model='gemini-2.0-flash',
        contents=[
          types.Part.from_text('What is shown in this image?'),
          types.Part.from_uri('gs://generativeai-downloads/images/scones.jpg',
          'image/jpeg')
        ]
      ):
        print(chunk.text)
      # The image shows a flat lay arrangement of freshly baked blueberry
      # scones.
    """

    # Retrieve and cache any MCP sessions if provided.
    incompatible_tools_indexes = (
        _extra_utils.find_afc_incompatible_tool_indexes(config)
    )
    # Retrieve and cache any MCP sessions if provided.
    parsed_config, mcp_to_genai_tool_adapters = (
        await _extra_utils.parse_config_for_mcp_sessions(config)
    )
    if _extra_utils.should_disable_afc(parsed_config):
      response = await self._generate_content_stream(
          model=model, contents=contents, config=parsed_config
      )

      async def base_async_generator(model, contents, config):  # type: ignore[no-untyped-def]
        async for chunk in response:  # type: ignore[attr-defined]
          yield chunk

      return base_async_generator(model, contents, parsed_config)  # type: ignore[no-untyped-call, no-any-return]

    if incompatible_tools_indexes:
      original_tools_length = 0
      if isinstance(config, types.GenerateContentConfig):
        if config.tools:
          original_tools_length = len(config.tools)
      elif isinstance(config, dict):
        tools = config.get('tools', [])
        if tools:
          original_tools_length = len(tools)
      if len(incompatible_tools_indexes) != original_tools_length:
        indices_str = ', '.join(map(str, incompatible_tools_indexes))
        logger.warning(
            'Tools at indices [%s] are not compatible with automatic function '
            'calling (AFC). AFC is disabled. If AFC is intended, please '
            'include python callables in the tool list, and do not include '
            'function declaration and MCP server in the tool list.',
            indices_str,
        )
      response = await self._generate_content_stream(
          model=model, contents=contents, config=parsed_config
      )

      async def base_async_generator(model, contents, config):  # type: ignore[no-untyped-def]
        async for chunk in response:  # type: ignore[attr-defined]
          yield chunk

      return base_async_generator(model, contents, parsed_config)  # type: ignore[no-untyped-call, no-any-return]

    # With tool compatibility confirmed, validate that the configuration are
    # compatible with each other and raise an error if invalid.
    _extra_utils.raise_error_for_afc_incompatible_config(parsed_config)

    async def async_generator(model, contents, config):  # type: ignore[no-untyped-def]
      remaining_remote_calls_afc = _extra_utils.get_max_remote_calls_afc(config)
      logger.info(
          f'AFC is enabled with max remote calls: {remaining_remote_calls_afc}.'
      )
      automatic_function_calling_history: list[types.Content] = []
      func_response_parts = None
      chunk = None
      i = 0
      while remaining_remote_calls_afc > 0:
        i += 1
        response = await self._generate_content_stream(
            model=model, contents=contents, config=config
        )
        # TODO: b/453739108 - make AFC logic more robust like the other 3 methods.
        if i > 1:
          logger.info(f'AFC remote call {i} is done.')
        remaining_remote_calls_afc -= 1
        if i > 1 and remaining_remote_calls_afc == 0:
          logger.info(
              'Reached max remote calls for automatic function calling.'
          )

        function_map = _extra_utils.get_function_map(
            config, mcp_to_genai_tool_adapters, is_caller_method_async=True
        )

        if i == 1:
          # First request gets a function call.
          # Then get function response parts.
          # Yield chunks only if there's no function response parts.
          async for chunk in response:  # type: ignore[attr-defined]
            if not function_map:
              contents = _extra_utils.append_chunk_contents(contents, chunk)
              yield chunk
            else:
              if (
                  not chunk.candidates
                  or not chunk.candidates[0].content
                  or not chunk.candidates[0].content.parts
              ):
                break
              func_response_parts = (
                  await _extra_utils.get_function_response_parts_async(
                      chunk, function_map
                  )
              )
              if not func_response_parts:
                contents = _extra_utils.append_chunk_contents(contents, chunk)
                yield chunk

        else:
          #  Second request and beyond, yield chunks.
          async for chunk in response:  # type: ignore[attr-defined]

            if _extra_utils.should_append_afc_history(config):
              chunk.automatic_function_calling_history = (
                  automatic_function_calling_history
              )
            contents = _extra_utils.append_chunk_contents(contents, chunk)
            yield chunk
          if (
              chunk is None
              or not chunk.candidates
              or not chunk.candidates[0].content
              or not chunk.candidates[0].content.parts
          ):
            break
          func_response_parts = (
              await _extra_utils.get_function_response_parts_async(
                  chunk, function_map
              )
          )
        if not function_map:
          break

        if not func_response_parts:
          break

        if chunk is None:
          continue
        # Append function response parts to contents for the next request.
        func_call_content = chunk.candidates[0].content
        func_response_content = types.Content(
            role='user',
            parts=func_response_parts,
        )
        contents = t.t_contents(contents)
        if not automatic_function_calling_history:
          automatic_function_calling_history.extend(contents)
        if isinstance(contents, list) and func_call_content is not None:
          contents.append(func_call_content)
          contents.append(func_response_content)
        if func_call_content is not None:
          automatic_function_calling_history.append(func_call_content)
        automatic_function_calling_history.append(func_response_content)

    return async_generator(model, contents, parsed_config)  # type: ignore[no-untyped-call, no-any-return]

  async def edit_image(
      self,
      *,
      model: str,
      prompt: str,
      reference_images: list[types._ReferenceImageAPIOrDict],
      config: Optional[types.EditImageConfigOrDict] = None,
  ) -> types.EditImageResponse:
    """Edits an image based on a text description and configuration.

    Args:
      model (str): The model to use.
      prompt (str): A text description of the edit to apply to the image.
        reference_images (list[Union[RawReferenceImage, MaskReferenceImage,
        ControlReferenceImage, StyleReferenceImage, SubjectReferenceImage]): The
        reference images for editing.
      config (EditImageConfig): Configuration for editing.

    Usage:

    .. code-block:: python

      from google.genai.types import RawReferenceImage, MaskReferenceImage

      raw_ref_image = RawReferenceImage(
        reference_id=1,
        reference_image=types.Image.from_file(IMAGE_FILE_PATH),
      )

      mask_ref_image = MaskReferenceImage(
        reference_id=2,
        config=types.MaskReferenceConfig(
            mask_mode='MASK_MODE_FOREGROUND',
            mask_dilation=0.06,
        ),
      )
      response = await client.aio.models.edit_image(
        model='imagen-3.0-capability-001',
        prompt='man with dog',
        reference_images=[raw_ref_image, mask_ref_image],
        config=types.EditImageConfig(
            edit_mode= "EDIT_MODE_INPAINT_INSERTION",
            number_of_images= 1,
            include_rai_reason= True,
        )
      )
      response.generated_images[0].image.show()
      # Shows a man with a dog instead of a cat.
    """
    return await self._edit_image(
        model=model,
        prompt=prompt,
        reference_images=reference_images,
        config=config,
    )

  async def list(
      self,
      *,
      config: Optional[types.ListModelsConfigOrDict] = None,
  ) -> AsyncPager[types.Model]:
    """Makes an API request to list the available models.

    If `query_base` is set to True in the config or not set (default), the
    API will return all available base models. If set to False, it will return
    all tuned models.

    Args:
      config (ListModelsConfigOrDict): Configuration for retrieving models.

    Usage:

    .. code-block:: python

      response = await client.aio.models.list(config={'page_size': 5})
      print(response.page)
      # [Model(name='projects/./locations/./models/123', display_name='my_model'

      response = await client.aio.models.list(
          config={'page_size': 5, 'query_base': True}
        )
      print(response.page)
      # [Model(name='publishers/google/models/gemini-2.0-flash-exp' ...
    """

    config = (
        types._ListModelsParameters(config=config).config
        or types.ListModelsConfig()
    )
    if config.query_base is None:
      config.query_base = True
    if self._api_client.vertexai:
      config = config.copy()
      if not config.query_base:
        # Filter for tuning jobs artifacts by labels.
        filter_value = config.filter
        config.filter = (
            filter_value + '&filter=labels.tune-type:*'
            if filter_value
            else 'labels.tune-type:*'
        )
    return AsyncPager(
        'models',
        self._list,
        await self._list(config=config),
        config,
    )

  async def generate_images(
      self,
      *,
      model: str,
      prompt: str,
      config: Optional[types.GenerateImagesConfigOrDict] = None,
  ) -> types.GenerateImagesResponse:
    """Generates images based on a text description and configuration.

    Args:
      model (str): The model to use.
      prompt (str): A text description of the images to generate.
      config (GenerateImagesConfig): Configuration for generation.

    Usage:

    .. code-block:: python

      response = await client.aio.models.generate_images(
        model='imagen-3.0-generate-002',
        prompt='Man with a dog',
        config=types.GenerateImagesConfig(
            number_of_images= 1,
            include_rai_reason= True,
        )
      )
      response.generated_images[0].image.show()
      # Shows a man with a dog.
    """
    api_response = await self._generate_images(
        model=model,
        prompt=prompt,
        config=config,
    )
    positive_prompt_safety_attributes = None
    generated_images = []
    if not api_response or not api_response.generated_images:
      return api_response

    for generated_image in api_response.generated_images:
      if (
          generated_image.safety_attributes
          and generated_image.safety_attributes.content_type
          == 'Positive Prompt'
      ):
        positive_prompt_safety_attributes = generated_image.safety_attributes
      else:
        generated_images.append(generated_image)

    response = types.GenerateImagesResponse(
        generated_images=generated_images,
        positive_prompt_safety_attributes=positive_prompt_safety_attributes,
    )
    return response

  async def upscale_image(
      self,
      *,
      model: str,
      image: types.ImageOrDict,
      upscale_factor: str,
      config: Optional[types.UpscaleImageConfigOrDict] = None,
  ) -> types.UpscaleImageResponse:
    """Makes an API request to upscale a provided image.

    Args:
      model (str): The model to use.
      image (Image): The input image for upscaling.
      upscale_factor (str): The factor to upscale the image (x2 or x4).
      config (UpscaleImageConfig): Configuration for upscaling.

    Usage:

    .. code-block:: python

      from google.genai.types import Image

      IMAGE_FILE_PATH="my-image.png"
      response = await client.aio.models.upscale_image(
          model='imagen-3.0-generate-001',
          image=types.Image.from_file(IMAGE_FILE_PATH),
          upscale_factor='x2',
      )
      response.generated_images[0].image.show()
      # Opens my-image.png which is upscaled by a factor of 2.
    """

    # Validate config.
    types.UpscaleImageParameters(
        model=model,
        image=image,
        upscale_factor=upscale_factor,
        config=config,
    )

    # Convert to API config.
    config = config or {}
    if isinstance(config, types.UpscaleImageConfig):
      config_dct = config.model_dump()
    else:
      config_dct = dict(config)
    api_config = types._UpscaleImageAPIConfigDict(
        http_options=config_dct.get('http_options', None),
        output_gcs_uri=config_dct.get('output_gcs_uri', None),
        safety_filter_level=config_dct.get('safety_filter_level', None),
        person_generation=config_dct.get('person_generation', None),
        include_rai_reason=config_dct.get('include_rai_reason', None),
        output_mime_type=config_dct.get('output_mime_type', None),
        output_compression_quality=config_dct.get(
            'output_compression_quality', None
        ),
        enhance_input_image=config_dct.get('enhance_input_image', None),
        image_preservation_factor=config_dct.get(
            'image_preservation_factor', None
        ),
        labels=config_dct.get('labels', None),
    )  # pylint: disable=protected-access

    # Provide default values through API config.
    api_config['mode'] = 'upscale'
    api_config['number_of_images'] = 1

    return await self._upscale_image(
        model=model,
        image=image,
        upscale_factor=upscale_factor,
        config=api_config,
    )

  async def generate_videos(
      self,
      *,
      model: str,
      prompt: Optional[str] = None,
      image: Optional[types.ImageOrDict] = None,
      video: Optional[types.VideoOrDict] = None,
      source: Optional[types.GenerateVideosSourceOrDict] = None,
      config: Optional[types.GenerateVideosConfigOrDict] = None,
  ) -> types.GenerateVideosOperation:
    """Generates videos based on an input (text, image, or video) and configuration.

    The following use cases are supported:
    1. Text to video generation.
    2a. Image to video generation (additional text prompt is optional).
    2b. Image to video generation with frame interpolation (specify last_frame
    in config).
    3. Video extension (additional text prompt is optional)

    Args:
      model: The model to use.
      prompt: The text prompt for generating the videos. Optional for image to
        video and video extension use cases. This argument is deprecated, please
        use source instead.
      image: The input image for generating the videos. Optional if prompt is
        provided. This argument is deprecated, please use source instead.
      video: The input video for video extension use cases. Optional if prompt
        or image is provided. This argument is deprecated, please use source
        instead.
      source: The input source for generating the videos (prompt, image, and/or
        video)
      config: Configuration for generation.

    Usage:

      ```
      operation = client.models.generate_videos(
          model="veo-2.0-generate-001",
          source=types.GenerateVideosSource(
              prompt="A neon hologram of a cat driving at top speed",
          ),
      )
      while not operation.done:
          time.sleep(10)
          operation = client.operations.get(operation)

      operation.result.generated_videos[0].video.uri
      ```
    """
    if (prompt or image or video) and source:
      raise ValueError(
          'Source and prompt/image/video are mutually exclusive.'
          + ' Please only use source.'
      )
    # Gemini Developer API does not support video bytes.
    video_dct: dict[str, Any] = {}
    if not self._api_client.vertexai and video:
      if isinstance(video, types.Video):
        video_dct = video.model_dump()
      else:
        video_dct = dict(video)

      if video_dct.get('uri') and video_dct.get('video_bytes'):
        video = types.Video(
            uri=video_dct.get('uri'), mime_type=video_dct.get('mime_type')
        )
    elif not self._api_client.vertexai and source:
      if isinstance(source, types.GenerateVideosSource):
        source_dct = source.model_dump()
        video_dct = source_dct.get('video', {})
      else:
        source_dct = dict(source)
        if isinstance(source_dct.get('video'), types.Video):
          video_obj: types.Video = source_dct.get('video', types.Video())
          video_dct = video_obj.model_dump()
      if video_dct and video_dct.get('uri') and video_dct.get('video_bytes'):
        source = types.GenerateVideosSource(
            prompt=source_dct.get('prompt'),
            image=source_dct.get('image'),
            video=types.Video(
                uri=video_dct.get('uri'),
                mime_type=video_dct.get('mime_type'),
            ),
        )
    return await self._generate_videos(
        model=model,
        prompt=prompt,
        image=image,
        video=video,
        source=source,
        config=config,
    )

  async def embed_content(
      self,
      *,
      model: str,
      contents: Union[types.ContentListUnion, types.ContentListUnionDict],
      config: Optional[types.EmbedContentConfigOrDict] = None,
  ) -> types.EmbedContentResponse:
    """Calculates embeddings for the given contents.

    Args:
      model (str): The model to use.
      contents (list[Content]): The contents to embed.
      config (EmbedContentConfig): Optional configuration for embeddings.

    Usage:

    .. code-block:: python

      embeddings = await client.aio.models.embed_content(
          model= 'text-embedding-004',
          contents=[
              'What is your name?',
              'What is your favorite color?',
          ],
          config={
              'output_dimensionality': 64
          },
      )

      multimodal_embeddings = await client.aio.models.embed_content(
          model='gemini-embedding-2-preview',
          contents=[
              types.Part.from_uri(
                  file_uri='gs://generativeai-downloads/images/scones.jpg',
                  mime_type='image/jpeg',
              ),
          ],
          config={
              'output_dimensionality': 64
          },
      )
    """
    if not self._api_client.vertexai:
      if 'gemini-embedding-2' in model:
        contents = t.t_contents(contents)  # type: ignore[assignment]
      return await self._embed_content(
          model=model, contents=contents, config=config
      )
    if t.t_is_vertex_embed_content_model(model):
      normalized_contents = t.t_contents(contents)
      if len(normalized_contents) > 1:
        raise ValueError(
            'The embedContent API for this model only supports one content at a'
            ' time.'
        )
      return await self._embed_content(
          model=model,
          contents=contents,
          content=normalized_contents[0],
          embedding_api_type=types.EmbeddingApiType.EMBED_CONTENT,
          config=config,
      )
    else:
      return await self._embed_content(
          model=model,
          contents=contents,
          embedding_api_type=types.EmbeddingApiType.PREDICT,
          config=config,
      )
