


'''simple docstring'''






import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


a_					:    str          =    logging.get_logger(__name__)

a_					:    Any          =    {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

a_					:    List[Any]          =    {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
a_					:    Dict          =    {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
a_					:    Any          =    {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

a_					:    List[str]          =    {
    "facebook/dpr-ctx_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-ctx_encoder-multiset-base": 5_1_2,
}
a_					:    int          =    {
    "facebook/dpr-question_encoder-single-nq-base": 5_1_2,
    "facebook/dpr-question_encoder-multiset-base": 5_1_2,
}
a_					:    int          =    {
    "facebook/dpr-reader-single-nq-base": 5_1_2,
    "facebook/dpr-reader-multiset-base": 5_1_2,
}


a_					:    Dict          =    {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
a_					:    Optional[Any]          =    {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
a_					:    Union[str, Any]          =    {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	VOCAB_FILES_NAMES
 _lowerCAmelCase 					=	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase 					=	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase 					=	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
 _lowerCAmelCase 					=	DPRContextEncoderTokenizer





class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	VOCAB_FILES_NAMES
 _lowerCAmelCase 					=	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase 					=	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase 					=	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
 _lowerCAmelCase 					=	DPRQuestionEncoderTokenizer





a_					:    Optional[int]          =    collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

a_					:    int          =    collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


a_					:    List[str]          =    R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "




@add_start_docstrings(_SCREAMING_SNAKE_CASE       )
class  a     :
 def __call__( self      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = False      ,				__magic_name__ = False      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							BatchEncoding:
      if titles is None and texts is None:
           return super().__call__(
               __magic_name__      ,				padding=__magic_name__      ,				truncation=__magic_name__      ,				max_length=__magic_name__      ,				return_tensors=__magic_name__      ,				return_attention_mask=__magic_name__      ,				**__magic_name__      ,				)
      elif titles is None or texts is None:
           _a			      =							titles if texts is None else texts
           return super().__call__(
               __magic_name__      ,				__magic_name__      ,				padding=__magic_name__      ,				truncation=__magic_name__      ,				max_length=__magic_name__      ,				return_tensors=__magic_name__      ,				return_attention_mask=__magic_name__      ,				**__magic_name__      ,				)
      _a			      =							titles if not isinstance(__magic_name__      ,				__magic_name__						) else [titles]
      _a			      =							texts if not isinstance(__magic_name__      ,				__magic_name__						) else [texts]
      _a			      =							len(__magic_name__						)
      _a			      =							questions if not isinstance(__magic_name__      ,				__magic_name__						) else [questions] * n_passages
      assert len(__magic_name__						) == len(
          __magic_name__						), f'There should be as many titles than texts but got {len(__magic_name__						)} titles and {len(__magic_name__						)} texts.'
      _a			      =							super().__call__(__magic_name__      ,				__magic_name__      ,				padding=__magic_name__      ,				truncation=__magic_name__						)['input_ids']
      _a			      =							super().__call__(__magic_name__      ,				add_special_tokens=__magic_name__      ,				padding=__magic_name__      ,				truncation=__magic_name__						)['input_ids']
      _a			      =							{
          'input_ids': [
              (encoded_question_and_title + encoded_text)[:max_length]
              if max_length is not None and truncation
              else encoded_question_and_title + encoded_text
              for encoded_question_and_title, encoded_text in zip(__magic_name__      ,				__magic_name__						)
          ]
      }
      if return_attention_mask is not False:
           _a			      =							[]
           for input_ids in encoded_inputs["input_ids"]:
                attention_mask.append([int(input_id != self.pad_token_id						) for input_id in input_ids]						)
           _a			      =							attention_mask
      return self.pad(__magic_name__      ,				padding=__magic_name__      ,				max_length=__magic_name__      ,				return_tensors=__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = 16      ,				__magic_name__ = 64      ,				__magic_name__ = 4      ,				)					->							List[DPRSpanPrediction]:
      _a			      =							reader_input['input_ids']
      _a      ,   _a      ,   _a			      =							reader_output[:3]
      _a			      =							len(__magic_name__						)
      _a			      =							sorted(range(__magic_name__						)      ,				reverse=__magic_name__      ,				key=relevance_logits.__getitem__						)
      _a			      =							[]
      for doc_id in sorted_docs:
           _a			      =							list(input_ids[doc_id]						)
           # assuming question & title information is at the beginning of the sequence
           _a			      =							sequence_ids.index(self.sep_token_id      ,				2						) + 1  # second sep id
           if sequence_ids[-1] == self.pad_token_id:
                _a			      =							sequence_ids.index(self.pad_token_id						)
           else:
                _a			      =							len(__magic_name__						)

           _a			      =							self._get_best_spans(
               start_logits=start_logits[doc_id][passage_offset:sequence_len]      ,				end_logits=end_logits[doc_id][passage_offset:sequence_len]      ,				max_answer_length=__magic_name__      ,				top_spans=__magic_name__      ,				)
           for start_index, end_index in best_spans:
                start_index += passage_offset
                end_index += passage_offset
                nbest_spans_predictions.append(
                    DPRSpanPrediction(
                        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]      ,				relevance_score=relevance_logits[doc_id]      ,				doc_id=__magic_name__      ,				start_index=__magic_name__      ,				end_index=__magic_name__      ,				text=self.decode(sequence_ids[start_index : end_index + 1]						)      ,				)						)
           if len(__magic_name__						) >= num_spans:
                break
      return nbest_spans_predictions[:num_spans]




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				)					->							List[DPRSpanPrediction]:
      _a			      =							[]
      for start_index, start_score in enumerate(__magic_name__						):
           for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]						):
                scores.append(((start_index, start_index + answer_length), start_score + end_score)						)
      _a			      =							sorted(__magic_name__      ,				key=lambda __magic_name__						: x[1]      ,				reverse=__magic_name__						)
      _a			      =							[]
      for (start_index, end_index), score in scores:
           assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
           _a			      =							end_index - start_index + 1
           assert length <= max_answer_length, f'Span is too long: {length} > {max_answer_length}'
           if any(
               start_index <= prev_start_index <= prev_end_index <= end_index
               or prev_start_index <= start_index <= end_index <= prev_end_index
               for (prev_start_index, prev_end_index) in chosen_span_intervals						):
                continue
           chosen_span_intervals.append((start_index, end_index)						)

           if len(__magic_name__						) == top_spans:
                break
      return chosen_span_intervals





@add_end_docstrings(_SCREAMING_SNAKE_CASE       )
class  a     (     _SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	VOCAB_FILES_NAMES
 _lowerCAmelCase 					=	READER_PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase 					=	READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase 					=	READER_PRETRAINED_INIT_CONFIGURATION
 _lowerCAmelCase 					=	["""input_ids""", """attention_mask"""]
 _lowerCAmelCase 					=	DPRReaderTokenizer





'''simple docstring'''






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
a_					:    int          =    "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
a_					:    Optional[int]          =    direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
a_					:    str          =    re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
a_					:    Union[str, Any]          =    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.
a_					:    List[Any]          =    re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a_					:    int          =    [
    ("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 	_A				(lowerCAmelCase__  :List[Any]		)   ->  str:




     '''simple docstring'''



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






def 	_A				()   ->  Union[str, Any]:




     '''simple docstring'''



     _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(lowerCAmelCase__		)
     _a			      =							collections.defaultdict(lowerCAmelCase__		)
     _a			      =							collections.defaultdict(lowerCAmelCase__		)

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

          if lookup_dict is not None:
               while len(lowerCAmelCase__		) > 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(lowerCAmelCase__		)[:-1]		)

     _a			      =							set(list(pt_models.keys()		) + list(tf_models.keys()		) + list(flax_models.keys()		)		)
     _a			      =							list(lowerCAmelCase__		)
     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(lowerCAmelCase__		)






def 	_A				(lowerCAmelCase__  :Dict		)   ->  str:




     '''simple docstring'''



     _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(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__		):
               # The type of pipeline may not exist in this framework
               if not hasattr(lowerCAmelCase__				,						lowerCAmelCase__		):
                    continue
               # First extract all model_names
               _a			      =							[]
               for name in getattr(lowerCAmelCase__				,						lowerCAmelCase__		).values():
                    if isinstance(lowerCAmelCase__				,						lowerCAmelCase__		):
                         model_names.append(lowerCAmelCase__		)
                    else:
                         model_names.extend(list(lowerCAmelCase__		)		)

            # 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 	_A				(lowerCAmelCase__  :Any				,						lowerCAmelCase__  :str		)   ->  Union[str, Any]:




     '''simple docstring'''



     _a			      =							get_frameworks_table()
     _a			      =							Dataset.from_pandas(lowerCAmelCase__		)

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

     # 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(lowerCAmelCase__		)

     with tempfile.TemporaryDirectory() as tmp_dir:
          frameworks_dataset.to_json(os.path.join(lowerCAmelCase__				,						'frameworks.json'		)		)
          tags_dataset.to_json(os.path.join(lowerCAmelCase__				,						'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=lowerCAmelCase__				,						repo_type='dataset'				,						token=lowerCAmelCase__				,						commit_message=lowerCAmelCase__				,						)






def 	_A				()   ->  str:




     '''simple docstring'''



     _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(lowerCAmelCase__				,						(list, tuple)		):
                    _a			      =							model[0]
               _a			      =							model.__name__
               if model not in in_table.values():
                    missing.append(lowerCAmelCase__		)

     if len(lowerCAmelCase__		) > 0:
          _a			      =							', '.join(lowerCAmelCase__		)
          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__":
 a_					:    Dict          =    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.")
 a_					:    Union[str, Any]          =    parser.parse_args()

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

