



import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__snake_case						:int						=					'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
__snake_case						:Any						=					direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__snake_case						:List[str]						=					re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__snake_case						:Optional[int]						=					re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__snake_case						:str						=					re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__snake_case						:Optional[Any]						=					[
    ('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
    ('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
    ('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
    ('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
    ('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
    ('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
    ('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
    ('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
    (
        '''zero-shot-object-detection''',
        '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
        '''AutoModelForZeroShotObjectDetection''',
    ),
    ('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
    ('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
    ('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
    ('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
    (
        '''table-question-answering''',
        '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForTableQuestionAnswering''',
    ),
    ('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
    ('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
    (
        '''next-sentence-prediction''',
        '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
        '''AutoModelForNextSentencePrediction''',
    ),
    (
        '''audio-frame-classification''',
        '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForAudioFrameClassification''',
    ),
    ('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
    (
        '''document-question-answering''',
        '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForDocumentQuestionAnswering''',
    ),
    (
        '''visual-question-answering''',
        '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
        '''AutoModelForVisualQuestionAnswering''',
    ),
    ('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
    (
        '''zero-shot-image-classification''',
        '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
        '''AutoModelForZeroShotImageClassification''',
    ),
    ('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
    ('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
    ('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''       ,			__a    )
    return [m.group(0    ) for m in matches]





def 					__snake_case     (      ):
    __a							=    transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    __a							=    {
        config.replace('''Config'''       ,			''''''    ): model_type for model_type, config in config_maping_names.items()
    }

    # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
    __a							=    collections.defaultdict(__a    )
    __a							=    collections.defaultdict(__a    )
    __a							=    collections.defaultdict(__a    )

    # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
    for attr_name in dir(__a    ):
        __a							=    None
        if _re_tf_models.match(__a    ) is not None:
            __a							=    tf_models
            __a							=    _re_tf_models.match(__a    ).groups()[0]
        elif _re_flax_models.match(__a    ) is not None:
            __a							=    flax_models
            __a							=    _re_flax_models.match(__a    ).groups()[0]
        elif _re_pt_models.match(__a    ) is not None:
            __a							=    pt_models
            __a							=    _re_pt_models.match(__a    ).groups()[0]

        if lookup_dict is not None:
            while len(__a    ) > 0:
                if attr_name in model_prefix_to_model_type:
                    __a							=    True
                    break
                # Try again after removing the last word in the name
                __a							=    ''''''.join(camel_case_split(__a    )[:-1]    )

    __a							=    set(list(pt_models.keys()    ) + list(tf_models.keys()    ) + list(flax_models.keys()    )    )
    __a							=    list(__a    )
    all_models.sort()

    __a							=    {'''model_type''': all_models}
    __a							=    [pt_models[t] for t in all_models]
    __a							=    [tf_models[t] for t in all_models]
    __a							=    [flax_models[t] for t in all_models]

    # Now let's use the auto-mapping names to make sure
    __a							=    {}
    for t in all_models:
        if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
            __a							=    '''AutoProcessor'''
        elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
            __a							=    '''AutoTokenizer'''
        elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
            __a							=    '''AutoFeatureExtractor'''
        else:
            # Default to AutoTokenizer if a model has nothing, for backward compatibility.
            __a							=    '''AutoTokenizer'''

    __a							=    [processors[t] for t in all_models]

    return pd.DataFrame(__a    )





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    [
        transformers_module.models.auto.modeling_auto,
        transformers_module.models.auto.modeling_tf_auto,
        transformers_module.models.auto.modeling_flax_auto,
    ]
    for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
        __a							=    [model_mapping, f'TF_{model_mapping}', f'FLAX_{model_mapping}']
        __a							=    [auto_class, f'TF_{auto_class}', f'Flax_{auto_class}']
        # Loop through all three frameworks
        for module, cls, mapping in zip(__a       ,			__a       ,			__a    ):
            # The type of pipeline may not exist in this framework
            if not hasattr(__a       ,			__a    ):
                continue
            # First extract all model_names
            __a							=    []
            for name in getattr(__a       ,			__a    ).values():
                if isinstance(__a       ,			__a    ):
                    model_names.append(__a    )
                else:
                    model_names.extend(list(__a    )    )

            # Add pipeline tag and auto model class for those models
            table.update({model_name: (pipeline_tag, cls) for model_name in model_names}    )

    return table





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    get_frameworks_table()
    __a							=    Dataset.from_pandas(__a    )

    __a							=    hf_hub_download(
        '''huggingface/transformers-metadata'''       ,			'''pipeline_tags.json'''       ,			repo_type='''dataset'''       ,			token=__a    )
    __a							=    Dataset.from_json(__a    )
    __a							=    {
        tags_dataset[i]['''model_class''']: (tags_dataset[i]['''pipeline_tag'''], tags_dataset[i]['''auto_class'''])
        for i in range(len(__a    )    )
    }
    __a							=    update_pipeline_and_auto_class_table(__a    )

    # Sort the model classes to avoid some nondeterministic updates to create false update commits.
    __a							=    sorted(table.keys()    )
    __a							=    pd.DataFrame(
        {
            '''model_class''': model_classes,
            '''pipeline_tag''': [table[m][0] for m in model_classes],
            '''auto_class''': [table[m][1] for m in model_classes],
        }    )
    __a							=    Dataset.from_pandas(__a    )

    with tempfile.TemporaryDirectory() as tmp_dir:
        frameworks_dataset.to_json(os.path.join(__a       ,			'''frameworks.json'''    )    )
        tags_dataset.to_json(os.path.join(__a       ,			'''pipeline_tags.json'''    )    )

        if commit_sha is not None:
            __a							=    (
                f'Update with commit {commit_sha}\n\nSee: '
                f'https://github.com/huggingface/transformers/commit/{commit_sha}'
            )
        else:
            __a							=    '''Update'''

        upload_folder(
            repo_id='''huggingface/transformers-metadata'''       ,			folder_path=__a       ,			repo_type='''dataset'''       ,			token=__a       ,			commit_message=__a       ,			)





def 					__snake_case     (      ):
    __a							=    {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
    __a							=    transformers_module.pipelines.SUPPORTED_TASKS
    __a							=    []
    for key in pipeline_tasks:
        if key not in in_table:
            __a							=    pipeline_tasks[key]['''pt''']
            if isinstance(__a       ,			(list, tuple)    ):
                __a							=    model[0]
            __a							=    model.__name__
            if model not in in_table.values():
                missing.append(__a    )

    if len(__a    ) > 0:
        __a							=    ''', '''.join(__a    )
        raise ValueError(
            '''The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '''
            f'`utils/update_metadata.py`: {msg}. Please add them!'    )


if __name__ == "__main__":
 __snake_case						:Optional[Any]						=					argparse.ArgumentParser()
 parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
 parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
 parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
 __snake_case						:Optional[int]						=					parser.parse_args()

 if args.check_only:
  check_pipeline_tags()
 else:
  update_metadata(args.token, args.commit_sha)










# Lint as: python3
import itertools
import os
import re


__snake_case						:int						=					re.compile(r'''([A-Z]+)([A-Z][a-z])''')
__snake_case						:List[Any]						=					re.compile(r'''([a-z\d])([A-Z])''')

__snake_case						:Dict						=					re.compile(r'''(?<!_)_(?!_)''')
__snake_case						:Optional[int]						=					re.compile(r'''(_{2,})''')

__snake_case						:Optional[Any]						=					r'''^\w+(\.\w+)*$'''

__snake_case						:Union[str, Any]						=					r'''<>:/\|?*'''





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    _uppercase_uppercase_re.sub(R'''\1_\2'''       ,			_UpperCAmelCase    )
    __a							=    _lowercase_uppercase_re.sub(R'''\1_\2'''       ,			_UpperCAmelCase    )
    return name.lower()





def 					__snake_case     (      _UpperCAmelCase    ):
    __a							=    _single_underscore_re.split(_UpperCAmelCase    )
    __a							=    [_multiple_underscores_re.split(_UpperCAmelCase    ) for n in name]
    return "".join(n.capitalize() for n in itertools.chain.from_iterable(_UpperCAmelCase    ) if n != ''''''    )





def 					__snake_case     (      _UpperCAmelCase    ):
    if os.path.basename(_UpperCAmelCase    ) != name:
        raise ValueError(f'Should be a dataset name, not a path: {name}'    )
    return camelcase_to_snakecase(_UpperCAmelCase    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    if os.path.basename(_UpperCAmelCase    ) != name:
        raise ValueError(f'Should be a dataset name, not a path: {name}'    )
    if not re.match(_split_re       ,			_UpperCAmelCase    ):
        raise ValueError(f'Split name should match \'{_split_re}\'\' but got \'{split}\'.'    )
    return f'{filename_prefix_for_name(_UpperCAmelCase    )}-{split}'





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None    ):
    __a							=    filename_prefix_for_split(_UpperCAmelCase       ,			_UpperCAmelCase    )
    if filetype_suffix:
        prefix += f'.{filetype_suffix}'
    __a							=    os.path.join(_UpperCAmelCase       ,			_UpperCAmelCase    )
    return f'{filepath}*'





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None    ):
    __a							=    filename_prefix_for_split(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    os.path.join(_UpperCAmelCase       ,			_UpperCAmelCase    )

    if shard_lengths:
        __a							=    len(_UpperCAmelCase    )
        __a							=    [f'{prefix}-{shard_id:05d}-of-{num_shards:05d}' for shard_id in range(_UpperCAmelCase    )]
        if filetype_suffix:
            __a							=    [filename + f'.{filetype_suffix}' for filename in filenames]
        return filenames
    else:
        __a							=    prefix
        if filetype_suffix:
            filename += f'.{filetype_suffix}'
        return [filename]





