import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


_lowerCAmelCase        =       get_logger(__name__)


def       a__      (					a							)		->	Optional[int]:
       return TestCommand(
           args.dataset  ,   args.name  ,   args.cache_dir  ,   args.data_dir  ,   args.all_configs  ,   args.save_info or args.save_infos  ,   args.ignore_verifications  ,   args.force_redownload  ,   args.clear_cache  ,   )






class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 __magic_name__          =   False  # to tell pytest it's not a test class
 @staticmethod
 def  UpperCAmelCase   (    __magic_name__      ):

        """simple docstring"""

        A_							:       Dict	      =				parser.add_parser('''test''' ,       help='''Test dataset implementation.'''      )
        test_parser.add_argument('''--name''' ,       type=__magic_name__ ,       default=__magic_name__ ,       help='''Dataset processing name'''      )
        test_parser.add_argument(
            '''--cache_dir''' ,       type=__magic_name__ ,       default=__magic_name__ ,       help='''Cache directory where the datasets are stored.''' ,       )
        test_parser.add_argument(
            '''--data_dir''' ,       type=__magic_name__ ,       default=__magic_name__ ,       help='''Can be used to specify a manual directory to get the files from.''' ,       )
        test_parser.add_argument('''--all_configs''' ,       action='''store_true''' ,       help='''Test all dataset configurations'''      )
        test_parser.add_argument(
            '''--save_info''' ,       action='''store_true''' ,       help='''Save the dataset infos in the dataset card (README.md)'''      )
        test_parser.add_argument(
            '''--ignore_verifications''' ,       action='''store_true''' ,       help='''Run the test without checksums and splits checks.''' ,       )
        test_parser.add_argument('''--force_redownload''' ,       action='''store_true''' ,       help='''Force dataset redownload'''      )
        test_parser.add_argument(
            '''--clear_cache''' ,       action='''store_true''' ,       help='''Remove downloaded files and cached datasets after each config test''' ,       )
        # aliases
        test_parser.add_argument('''--save_infos''' ,       action='''store_true''' ,       help='''alias to save_info'''      )
        test_parser.add_argument('''dataset''' ,       type=__magic_name__ ,       help='''Name of the dataset to download'''      )
        test_parser.set_defaults(func=_test_command_factory      )
 def __init__(    self ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       ):

        """simple docstring"""

        A_							:       Dict	      =				dataset
        A_							:       Any	      =				name
        A_							:       List[str]	      =				cache_dir
        A_							:       List[Any]	      =				data_dir
        A_							:       Any	      =				all_configs
        A_							:       int	      =				save_infos
        A_							:       Union[str, Any]	      =				ignore_verifications
        A_							:       List[Any]	      =				force_redownload
        A_							:       List[str]	      =				clear_cache
        if clear_cache and not cache_dir:
               print(
                   '''When --clear_cache is used, specifying a cache directory is mandatory.\n'''
                   '''The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'''
                   '''Please provide a --cache_dir that will be used to test the dataset script.'''      )
               exit(1      )
        if save_infos:
               A_							:       Optional[Any]	      =				True
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        fl_logger().setLevel(__magic_name__      )
        if self._name is not None and self._all_configs:
               print('''Both parameters `config` and `all_configs` can\'t be used at once.'''      )
               exit(1      )
        A_      ,							A_							:       Tuple	      =				self._dataset, self._name
        A_							:       Dict	      =				dataset_module_factory(__magic_name__      )
        A_							:       int	      =				import_main_class(module.module_path      )
        A_							:       str	      =				len(builder_cls.BUILDER_CONFIGS      ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

        def get_builders() -> Generator[DatasetBuilder, None, None]:
               if self._all_configs and builder_cls.BUILDER_CONFIGS:
                      for i, config in enumerate(builder_cls.BUILDER_CONFIGS      ):
                             if "config_name" in module.builder_kwargs:
                                    yield builder_cls(
                                        cache_dir=self._cache_dir ,       data_dir=self._data_dir ,       **module.builder_kwargs ,       )
                             else:
                                    yield builder_cls(
                                        config_name=config.name ,       cache_dir=self._cache_dir ,       data_dir=self._data_dir ,       **module.builder_kwargs ,       )
               else:
                      if "config_name" in module.builder_kwargs:
                             yield builder_cls(cache_dir=self._cache_dir ,       data_dir=self._data_dir ,       **module.builder_kwargs      )
                      else:
                             yield builder_cls(
                                 config_name=__magic_name__ ,       cache_dir=self._cache_dir ,       data_dir=self._data_dir ,       **module.builder_kwargs ,       )

        for j, builder in enumerate(get_builders()      ):
               print(f"""Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"""      )
               A_							:       str	      =				os.path.exists(
                   os.path.join(builder.get_imported_module_dir() ,       datasets.config.DATASETDICT_INFOS_FILENAME      )      )  # record checksums only if we need to update a (deprecated) dataset_infos.json
               builder.download_and_prepare(
                   download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
                   if not self._force_redownload
                   else DownloadMode.FORCE_REDOWNLOAD ,       verification_mode=VerificationMode.NO_CHECKS
                   if self._ignore_verifications
                   else VerificationMode.ALL_CHECKS ,       try_from_hf_gcs=__magic_name__ ,       )
               builder.as_dataset()
               if self._save_infos:
                      builder._save_infos()

               # If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
               # The dataset_infos are saved in the YAML part of the README.md

               # Let's move it to the original directory of the dataset script, to allow the user to
               # upload them on S3 at the same time afterwards.
               if self._save_infos:
                      A_							:       Union[str, Any]	      =				os.path.join(builder_cls.get_imported_module_dir() ,       '''README.md'''      )
                      A_							:       Union[str, Any]	      =				Path(__magic_name__      ).name + '''.py'''
                      A_							:       List[str]	      =				os.path.join(__magic_name__ ,       __magic_name__      )
                      if os.path.isfile(__magic_name__      ):
                             A_							:       str	      =				os.path.dirname(__magic_name__      )
                      elif os.path.isfile(__magic_name__      ):
                             A_							:       Any	      =				path
                      elif os.path.isdir(__magic_name__      ):  # for local directories containing only data files
                             A_							:       Optional[Any]	      =				path
                      else:  # in case of a remote dataset
                             A_							:       List[Any]	      =				None
                             print(f"""Dataset card saved at {dataset_readme_path}"""      )

                      # Move dataset_info back to the user
                      if dataset_dir is not None:
                             A_							:       Any	      =				os.path.join(__magic_name__ ,       '''README.md'''      )
                             copyfile(__magic_name__ ,       __magic_name__      )
                             print(f"""Dataset card saved at {user_dataset_readme_path}"""      )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
               if self._clear_cache:
                      if os.path.isdir(builder._cache_dir      ):
                             logger.warning(f"""Clearing cache at {builder._cache_dir}"""      )
                             rmtree(builder._cache_dir      )
                      A_							:       Optional[Any]	      =				os.path.join(self._cache_dir ,       datasets.config.DOWNLOADED_DATASETS_DIR      )
                      if os.path.isdir(__magic_name__      ):
                             logger.warning(f"""Clearing cache at {download_dir}"""      )
                             rmtree(__magic_name__      )

        print('''Test successful.'''      )



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowerCAmelCase        =       {
    'auto_factory': ['get_values'],
    'configuration_auto': ['ALL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CONFIG_MAPPING', 'MODEL_NAMES_MAPPING', 'AutoConfig'],
    'feature_extraction_auto': ['FEATURE_EXTRACTOR_MAPPING', 'AutoFeatureExtractor'],
    'image_processing_auto': ['IMAGE_PROCESSOR_MAPPING', 'AutoImageProcessor'],
    'processing_auto': ['PROCESSOR_MAPPING', 'AutoProcessor'],
    'tokenization_auto': ['TOKENIZER_MAPPING', 'AutoTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCAmelCase        =       [
        'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING',
        'MODEL_FOR_AUDIO_XVECTOR_MAPPING',
        'MODEL_FOR_BACKBONE_MAPPING',
        'MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING',
        'MODEL_FOR_CAUSAL_LM_MAPPING',
        'MODEL_FOR_CTC_MAPPING',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING',
        'MODEL_FOR_DEPTH_ESTIMATION_MAPPING',
        'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
        'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING',
        'MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING',
        'MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING',
        'MODEL_FOR_MASKED_LM_MAPPING',
        'MODEL_FOR_MASK_GENERATION_MAPPING',
        'MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
        'MODEL_FOR_OBJECT_DETECTION_MAPPING',
        'MODEL_FOR_PRETRAINING_MAPPING',
        'MODEL_FOR_QUESTION_ANSWERING_MAPPING',
        'MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING',
        'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
        'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
        'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING',
        'MODEL_FOR_TEXT_ENCODING_MAPPING',
        'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
        'MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING',
        'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING',
        'MODEL_FOR_VISION_2_SEQ_MAPPING',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING',
        'MODEL_MAPPING',
        'MODEL_WITH_LM_HEAD_MAPPING',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING',
        'AutoModel',
        'AutoBackbone',
        'AutoModelForAudioClassification',
        'AutoModelForAudioFrameClassification',
        'AutoModelForAudioXVector',
        'AutoModelForCausalLM',
        'AutoModelForCTC',
        'AutoModelForDepthEstimation',
        'AutoModelForImageClassification',
        'AutoModelForImageSegmentation',
        'AutoModelForInstanceSegmentation',
        'AutoModelForMaskGeneration',
        'AutoModelForTextEncoding',
        'AutoModelForMaskedImageModeling',
        'AutoModelForMaskedLM',
        'AutoModelForMultipleChoice',
        'AutoModelForNextSentencePrediction',
        'AutoModelForObjectDetection',
        'AutoModelForPreTraining',
        'AutoModelForQuestionAnswering',
        'AutoModelForSemanticSegmentation',
        'AutoModelForSeq2SeqLM',
        'AutoModelForSequenceClassification',
        'AutoModelForSpeechSeq2Seq',
        'AutoModelForTableQuestionAnswering',
        'AutoModelForTokenClassification',
        'AutoModelForUniversalSegmentation',
        'AutoModelForVideoClassification',
        'AutoModelForVision2Seq',
        'AutoModelForVisualQuestionAnswering',
        'AutoModelForDocumentQuestionAnswering',
        'AutoModelWithLMHead',
        'AutoModelForZeroShotImageClassification',
        'AutoModelForZeroShotObjectDetection',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCAmelCase        =       [
        'TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
        'TF_MODEL_FOR_CAUSAL_LM_MAPPING',
        'TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
        'TF_MODEL_FOR_MASK_GENERATION_MAPPING',
        'TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING',
        'TF_MODEL_FOR_MASKED_LM_MAPPING',
        'TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
        'TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
        'TF_MODEL_FOR_PRETRAINING_MAPPING',
        'TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING',
        'TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING',
        'TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING',
        'TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
        'TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
        'TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
        'TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING',
        'TF_MODEL_FOR_TEXT_ENCODING_MAPPING',
        'TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
        'TF_MODEL_FOR_VISION_2_SEQ_MAPPING',
        'TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING',
        'TF_MODEL_MAPPING',
        'TF_MODEL_WITH_LM_HEAD_MAPPING',
        'TFAutoModel',
        'TFAutoModelForAudioClassification',
        'TFAutoModelForCausalLM',
        'TFAutoModelForImageClassification',
        'TFAutoModelForMaskedImageModeling',
        'TFAutoModelForMaskedLM',
        'TFAutoModelForMaskGeneration',
        'TFAutoModelForMultipleChoice',
        'TFAutoModelForNextSentencePrediction',
        'TFAutoModelForPreTraining',
        'TFAutoModelForDocumentQuestionAnswering',
        'TFAutoModelForQuestionAnswering',
        'TFAutoModelForSemanticSegmentation',
        'TFAutoModelForSeq2SeqLM',
        'TFAutoModelForSequenceClassification',
        'TFAutoModelForSpeechSeq2Seq',
        'TFAutoModelForTableQuestionAnswering',
        'TFAutoModelForTextEncoding',
        'TFAutoModelForTokenClassification',
        'TFAutoModelForVision2Seq',
        'TFAutoModelForZeroShotImageClassification',
        'TFAutoModelWithLMHead',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCAmelCase        =       [
        'FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING',
        'FLAX_MODEL_FOR_CAUSAL_LM_MAPPING',
        'FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING',
        'FLAX_MODEL_FOR_MASKED_LM_MAPPING',
        'FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING',
        'FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING',
        'FLAX_MODEL_FOR_PRETRAINING_MAPPING',
        'FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING',
        'FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING',
        'FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING',
        'FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING',
        'FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING',
        'FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING',
        'FLAX_MODEL_MAPPING',
        'FlaxAutoModel',
        'FlaxAutoModelForCausalLM',
        'FlaxAutoModelForImageClassification',
        'FlaxAutoModelForMaskedLM',
        'FlaxAutoModelForMultipleChoice',
        'FlaxAutoModelForNextSentencePrediction',
        'FlaxAutoModelForPreTraining',
        'FlaxAutoModelForQuestionAnswering',
        'FlaxAutoModelForSeq2SeqLM',
        'FlaxAutoModelForSequenceClassification',
        'FlaxAutoModelForSpeechSeq2Seq',
        'FlaxAutoModelForTokenClassification',
        'FlaxAutoModelForVision2Seq',
    ]


if TYPE_CHECKING:
    from .auto_factory import get_values
    from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
    from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
    from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
    from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
    from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_auto import (
            MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
            MODEL_FOR_AUDIO_XVECTOR_MAPPING,
            MODEL_FOR_BACKBONE_MAPPING,
            MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
            MODEL_FOR_CAUSAL_LM_MAPPING,
            MODEL_FOR_CTC_MAPPING,
            MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
            MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
            MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
            MODEL_FOR_MASK_GENERATION_MAPPING,
            MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
            MODEL_FOR_MASKED_LM_MAPPING,
            MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            MODEL_FOR_OBJECT_DETECTION_MAPPING,
            MODEL_FOR_PRETRAINING_MAPPING,
            MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
            MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_TEXT_ENCODING_MAPPING,
            MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
            MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
            MODEL_FOR_VISION_2_SEQ_MAPPING,
            MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
            MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
            MODEL_MAPPING,
            MODEL_WITH_LM_HEAD_MAPPING,
            AutoBackbone,
            AutoModel,
            AutoModelForAudioClassification,
            AutoModelForAudioFrameClassification,
            AutoModelForAudioXVector,
            AutoModelForCausalLM,
            AutoModelForCTC,
            AutoModelForDepthEstimation,
            AutoModelForDocumentQuestionAnswering,
            AutoModelForImageClassification,
            AutoModelForImageSegmentation,
            AutoModelForInstanceSegmentation,
            AutoModelForMaskedImageModeling,
            AutoModelForMaskedLM,
            AutoModelForMaskGeneration,
            AutoModelForMultipleChoice,
            AutoModelForNextSentencePrediction,
            AutoModelForObjectDetection,
            AutoModelForPreTraining,
            AutoModelForQuestionAnswering,
            AutoModelForSemanticSegmentation,
            AutoModelForSeqaSeqLM,
            AutoModelForSequenceClassification,
            AutoModelForSpeechSeqaSeq,
            AutoModelForTableQuestionAnswering,
            AutoModelForTextEncoding,
            AutoModelForTokenClassification,
            AutoModelForUniversalSegmentation,
            AutoModelForVideoClassification,
            AutoModelForVisionaSeq,
            AutoModelForVisualQuestionAnswering,
            AutoModelForZeroShotImageClassification,
            AutoModelForZeroShotObjectDetection,
            AutoModelWithLMHead,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_auto import (
            TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_CAUSAL_LM_MAPPING,
            TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_MASK_GENERATION_MAPPING,
            TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
            TF_MODEL_FOR_MASKED_LM_MAPPING,
            TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            TF_MODEL_FOR_PRETRAINING_MAPPING,
            TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
            TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
            TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
            TF_MODEL_MAPPING,
            TF_MODEL_WITH_LM_HEAD_MAPPING,
            TFAutoModel,
            TFAutoModelForAudioClassification,
            TFAutoModelForCausalLM,
            TFAutoModelForDocumentQuestionAnswering,
            TFAutoModelForImageClassification,
            TFAutoModelForMaskedImageModeling,
            TFAutoModelForMaskedLM,
            TFAutoModelForMaskGeneration,
            TFAutoModelForMultipleChoice,
            TFAutoModelForNextSentencePrediction,
            TFAutoModelForPreTraining,
            TFAutoModelForQuestionAnswering,
            TFAutoModelForSemanticSegmentation,
            TFAutoModelForSeqaSeqLM,
            TFAutoModelForSequenceClassification,
            TFAutoModelForSpeechSeqaSeq,
            TFAutoModelForTableQuestionAnswering,
            TFAutoModelForTextEncoding,
            TFAutoModelForTokenClassification,
            TFAutoModelForVisionaSeq,
            TFAutoModelForZeroShotImageClassification,
            TFAutoModelWithLMHead,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_auto import (
            FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
            FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_MASKED_LM_MAPPING,
            FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            FLAX_MODEL_FOR_PRETRAINING_MAPPING,
            FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
            FLAX_MODEL_MAPPING,
            FlaxAutoModel,
            FlaxAutoModelForCausalLM,
            FlaxAutoModelForImageClassification,
            FlaxAutoModelForMaskedLM,
            FlaxAutoModelForMultipleChoice,
            FlaxAutoModelForNextSentencePrediction,
            FlaxAutoModelForPreTraining,
            FlaxAutoModelForQuestionAnswering,
            FlaxAutoModelForSeqaSeqLM,
            FlaxAutoModelForSequenceClassification,
            FlaxAutoModelForSpeechSeqaSeq,
            FlaxAutoModelForTokenClassification,
            FlaxAutoModelForVisionaSeq,
        )

else:
    import sys

    _lowerCAmelCase        =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


