


'''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__  :					List[str]        =       'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
a__  :					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__  :					List[str]        =       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__  :					Optional[int]        =       re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a__  :					Tuple        =       [
    ('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 _UpperCamelCase       (     __A    )							->		Any:


      '''simple docstring'''



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







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


      '''simple docstring'''



      UpperCamelCase__										=				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      UpperCamelCase__										=				{
          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.
      UpperCamelCase__										=				collections.defaultdict(__A    )
      UpperCamelCase__										=				collections.defaultdict(__A    )
      UpperCamelCase__										=				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    ):
            UpperCamelCase__										=				None
            if _re_tf_models.match(__A    ) is not None:
                  UpperCamelCase__										=				tf_models
                  UpperCamelCase__										=				_re_tf_models.match(__A    ).groups()[0]
            elif _re_flax_models.match(__A    ) is not None:
                  UpperCamelCase__										=				flax_models
                  UpperCamelCase__										=				_re_flax_models.match(__A    ).groups()[0]
            elif _re_pt_models.match(__A    ) is not None:
                  UpperCamelCase__										=				pt_models
                  UpperCamelCase__										=				_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:
                              UpperCamelCase__										=				True
                              break
                        # Try again after removing the last word in the name
                        UpperCamelCase__										=				"".join(camel_case_split(__A    )[:-1]    )

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

      UpperCamelCase__										=				{"model_type": all_models}
      UpperCamelCase__										=				[pt_models[t] for t in all_models]
      UpperCamelCase__										=				[tf_models[t] for t in all_models]
      UpperCamelCase__										=				[flax_models[t] for t in all_models]

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

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

      return pd.DataFrame(__A    )







def _UpperCamelCase       (     __A    )							->		int:


      '''simple docstring'''



      UpperCamelCase__										=				[
          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:
            UpperCamelCase__										=				[model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
            UpperCamelCase__										=				[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
                  UpperCamelCase__										=				[]
                  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 _UpperCamelCase       (     __A      ,			__A    )							->		Tuple:


      '''simple docstring'''



      UpperCamelCase__										=				get_frameworks_table()
      UpperCamelCase__										=				Dataset.from_pandas(__A    )

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

      # Sort the model classes to avoid some nondeterministic updates to create false update commits.
      UpperCamelCase__										=				sorted(table.keys()    )
      UpperCamelCase__										=				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],
          }    )
      UpperCamelCase__										=				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:
                  UpperCamelCase__										=				(
                      F'''Update with commit {commit_sha}\n\nSee: '''
                      F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
                  )
            else:
                  UpperCamelCase__										=				"Update"

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







def _UpperCamelCase       (     )							->		int:


      '''simple docstring'''



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

      if len(__A    ) > 0:
            UpperCamelCase__										=				", ".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__":
  a__  :					Union[str, 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.')
  a__  :					List[Any]        =       parser.parse_args()

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












'''simple docstring'''
from __future__ import annotations

class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							Optional[int]							,	__A       :							list[list[int]]	):
        __UpperCamelCase       =  TypeError(
            'Matrices must be formed from a list of zero or more lists containing at '
            'least one and the same number of values, each of which must be of type '
            'int or float.'	)
        if len(__A	) != 0:
               __UpperCamelCase       =  len(rows[0]	)
               if cols == 0:
                      raise error
               for row in rows:
                      if len(__A	) != cols:
                             raise error
                      for value in row:
                             if not isinstance(__A							,	(int, float)	):
                                    raise error
               __UpperCamelCase       =  rows
        else:
               __UpperCamelCase       =  []






 def        _lowerCamelCase					(       self       :							int	):
        return [[row[i] for row in self.rows] for i in range(len(self.rows[0]	)	)]






 @property
 def        _lowerCamelCase					(       self       :							str	):
        return len(self.rows	)






 @property
 def        _lowerCamelCase					(       self       :							Any	):
        return len(self.rows[0]	)






 @property
 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        return (self.num_rows, self.num_columns)






 @property
 def        _lowerCamelCase					(       self       :							Dict	):
        return self.order[0] == self.order[1]






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  [
            [0 if column_num != row_num else 1 for column_num in range(self.num_rows	)]
            for row_num in range(self.num_rows	)
        ]
        return Matrix(__A	)






 def        _lowerCamelCase					(       self       :							Any	):
        if not self.is_square:
               return 0
        if self.order == (0, 0):
               return 1
        if self.order == (1, 1):
               return int(self.rows[0][0]	)
        if self.order == (2, 2):
               return int(
                   (self.rows[0][0] * self.rows[1][1])
                   - (self.rows[0][1] * self.rows[1][0])	)
        else:
               return sum(
                   self.rows[0][column] * self.cofactors().rows[0][column]
                   for column in range(self.num_columns	)	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        return bool(self.determinant()	)






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							int							,	__A       :							int	):
        __UpperCamelCase       =  [
            [
                self.rows[other_row][other_column]
                for other_column in range(self.num_columns	)
                if other_column != column
            ]
            for other_row in range(self.num_rows	)
            if other_row != row
        ]
        return Matrix(__A	).determinant()






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							int							,	__A       :							int	):
        if (row + column) % 2 == 0:
               return self.get_minor(__A							,	__A	)
        return -1 * self.get_minor(__A							,	__A	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        return Matrix(
            [
                [self.get_minor(__A							,	__A	) for column in range(self.num_columns	)]
                for row in range(self.num_rows	)
            ]	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        return Matrix(
            [
                [
                    self.minors().rows[row][column]
                    if (row + column) % 2 == 0
                    else self.minors().rows[row][column] * -1
                    for column in range(self.minors().num_columns	)
                ]
                for row in range(self.minors().num_rows	)
            ]	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  [
            [self.cofactors().rows[column][row] for column in range(self.num_columns	)]
            for row in range(self.num_rows	)
        ]
        return Matrix(__A	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  self.determinant()
        if not determinant:
               raise TypeError('Only matrices with a non-zero determinant have an inverse'	)
        return self.adjugate() * (1 / determinant)






 def __repr__(       self       :							Optional[Any]	):
        return str(self.rows	)






 def __str__(       self       :							Union[str, Any]	):
        if self.num_rows == 0:
               return "[]"
        if self.num_rows == 1:
               return "[[" + ". ".join(str(self.rows[0]	)	) + "]]"
        return (
            "["
            + "\n ".join(
                [
                    '[' + '. '.join([str(__A	) for value in row]	) + '.]'
                    for row in self.rows
                ]	)
            + "]"
        )






 def        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							list[int]							,	__A       :							int | None = None	):
        __UpperCamelCase       =  TypeError('Row must be a list containing all ints and/or floats'	)
        if not isinstance(__A							,	__A	):
               raise type_error
        for value in row:
               if not isinstance(__A							,	(int, float)	):
                      raise type_error
        if len(__A	) != self.num_columns:
               raise ValueError(
                   'Row must be equal in length to the other rows in the matrix'	)
        if position is None:
               self.rows.append(__A	)
        else:
               __UpperCamelCase       =  self.rows[0:position] + [row] + self.rows[position:]






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	__A       :							list[int]							,	__A       :							int | None = None	):
        __UpperCamelCase       =  TypeError(
            'Column must be a list containing all ints and/or floats'	)
        if not isinstance(__A							,	__A	):
               raise type_error
        for value in column:
               if not isinstance(__A							,	(int, float)	):
                      raise type_error
        if len(__A	) != self.num_rows:
               raise ValueError(
                   'Column must be equal in length to the other columns in the matrix'	)
        if position is None:
               __UpperCamelCase       =  [self.rows[i] + [column[i]] for i in range(self.num_rows	)]
        else:
               __UpperCamelCase       =  [
                   self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                   for i in range(self.num_rows	)
               ]






 def __eq__(       self       :							Tuple							,	__A       :							object	):
        if not isinstance(__A							,	__A	):
               return NotImplemented
        return self.rows == other.rows






 def __ne__(       self       :							Any							,	__A       :							object	):
        return not self == other






 def __neg__(       self       :							List[Any]	):
        return self * -1






 def __add__(       self       :							List[str]							,	__A       :							Matrix	):
        if self.order != other.order:
               raise ValueError('Addition requires matrices of the same order'	)
        return Matrix(
            [
                [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns	)]
                for i in range(self.num_rows	)
            ]	)






 def __sub__(       self       :							str							,	__A       :							Matrix	):
        if self.order != other.order:
               raise ValueError('Subtraction requires matrices of the same order'	)
        return Matrix(
            [
                [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns	)]
                for i in range(self.num_rows	)
            ]	)






 def __mul__(       self       :							str							,	__A       :							Matrix | int | float	):
        if isinstance(__A							,	(int, float)	):
               return Matrix(
                   [[int(element * other	) for element in row] for row in self.rows]	)
        elif isinstance(__A							,	__A	):
               if self.num_columns != other.num_rows:
                      raise ValueError(
                          'The number of columns in the first matrix must '
                          'be equal to the number of rows in the second'	)
               return Matrix(
                   [
                       [Matrix.dot_product(__A							,	__A	) for column in other.columns()]
                       for row in self.rows
                   ]	)
        else:
               raise TypeError(
                   'A Matrix can only be multiplied by an int, float, or another matrix'	)






 def __pow__(       self       :							Union[str, Any]							,	__A       :							int	):
        if not isinstance(__A							,	__A	):
               raise TypeError('A Matrix can only be raised to the power of an int'	)
        if not self.is_square:
               raise ValueError('Only square matrices can be raised to a power'	)
        if other == 0:
               return self.identity()
        if other < 0:
               if self.is_invertable():
                      return self.inverse() ** (-other)
               raise ValueError(
                   'Only invertable matrices can be raised to a negative power'	)
        __UpperCamelCase       =  self
        for _ in range(other - 1	):
               result *= self
        return result






 @classmethod
 def        _lowerCamelCase					(       cls       :							Tuple							,	__A       :							list[int]							,	__A       :							list[int]	):
        return sum(row[i] * column[i] for i in range(len(__A	)	)	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
