




"""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         =      '''src/transformers'''


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


# Regexes that match TF/Flax/PT model names.
a         =      re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
a         =      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         =      re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a         =      [
    ('''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					( _snake_case							: Tuple							)   -> Tuple:






      '''simple docstring'''


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






def 						_snake_case					( )   -> Optional[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(_snake_case							)
      _A        =       collections.defaultdict(_snake_case							)
      _A        =       collections.defaultdict(_snake_case							)

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

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

      _A        =       set(list(pt_models.keys()							) + list(tf_models.keys()							) + list(flax_models.keys()							)							)
      _A        =       list(_snake_case							)
      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(_snake_case							)






def 						_snake_case					( _snake_case							: List[str]							)   -> Any:






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

            # 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					( _snake_case							: int    ,  _snake_case							: int							)   -> Tuple:






      '''simple docstring'''


      _A        =       get_frameworks_table()
      _A        =       Dataset.from_pandas(_snake_case							)

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

      # 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(_snake_case							)

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






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






      '''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(_snake_case    ,  (list, tuple)							):
                        _A        =       model[0]
                  _A        =       model.__name__
                  if model not in in_table.values():
                        missing.append(_snake_case							)

      if len(_snake_case							) > 0:
            _A        =       ', '.join(_snake_case							)
            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         =      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         =      parser.parse_args()

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







"""simple docstring"""

def 						_snake_case					( _snake_case							: str							)   -> str:






      '''simple docstring'''


      _A        =       ''
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups






def 						_snake_case					( _snake_case							: str							)   -> dict[str, str]:






      '''simple docstring'''


      _A        =       [chr(i + 65							) for i in range(26							)]
      # Remove duplicate characters from key
      _A        =       remove_duplicates(key.upper()							)
      _A        =       len(_snake_case							)
      # First fill cipher with key characters
      _A        =       {alphabet[i]: char for i, char in enumerate(_snake_case							)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(_snake_case							)    ,  26							):
            _A        =       alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  _A        =       alphabet[i - offset]
            _A        =       char
      return cipher_alphabet






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: dict[str, str]							)   -> str:






      '''simple docstring'''


      return "".join(cipher_map.get(_snake_case    ,  _snake_case							) for ch in message.upper()							)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: dict[str, str]							)   -> str:






      '''simple docstring'''


      _A        =       {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(_snake_case    ,  _snake_case							) for ch in message.upper()							)






def 						_snake_case					( )   -> None:






      '''simple docstring'''


      _A        =       input('Enter message to encode or decode: '							).strip()
      _A        =       input('Enter keyword: '							).strip()
      _A        =       input('Encipher or decipher? E/D:'							).strip()[0].lower()
      try:
            _A        =       {'e': encipher, 'd': decipher}[option]
      except KeyError:
            raise KeyError('invalid input option'							)
      _A        =       create_cipher_map(_snake_case							)
      print(func(_snake_case    ,  _snake_case							)							)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()

