'''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
UpperCamelCase__  :						str					=      'src/transformers'


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
UpperCamelCase__  :						Union[str, 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 __UpperCamelCase( _A       :					Union[str, Any]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Any 					=	re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''	,      _A     )
   return [m.group(0     ) for m in matches]







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[str] 					=	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   UpperCAmelCase__				:					int 					=	{
       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__				:					Optional[Any] 					=	collections.defaultdict(_A     )
   UpperCAmelCase__				:					Union[str, Any] 					=	collections.defaultdict(_A     )
   UpperCAmelCase__				:					str 					=	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__				:					List[Any] 					=	None
      if _re_tf_models.match(_A     ) is not None:
         UpperCAmelCase__				:					Union[str, Any] 					=	tf_models
         UpperCAmelCase__				:					Dict 					=	_re_tf_models.match(_A     ).groups()[0]
      elif _re_flax_models.match(_A     ) is not None:
         UpperCAmelCase__				:					Tuple 					=	flax_models
         UpperCAmelCase__				:					str 					=	_re_flax_models.match(_A     ).groups()[0]
      elif _re_pt_models.match(_A     ) is not None:
         UpperCAmelCase__				:					List[Any] 					=	pt_models
         UpperCAmelCase__				:					Optional[int] 					=	_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__				:					str 					=	True
               break
            # Try again after removing the last word in the name
            UpperCAmelCase__				:					Union[str, Any] 					=	''''''.join(camel_case_split(_A     )[:-1]     )

   UpperCAmelCase__				:					List[str] 					=	set(list(pt_models.keys()     ) + list(tf_models.keys()     ) + list(flax_models.keys()     )     )
   UpperCAmelCase__				:					Optional[int] 					=	list(_A     )
   all_models.sort()

   UpperCAmelCase__				:					Optional[int] 					=	{'''model_type''': all_models}
   UpperCAmelCase__				:					Optional[Any] 					=	[pt_models[t] for t in all_models]
   UpperCAmelCase__				:					List[str] 					=	[tf_models[t] for t in all_models]
   UpperCAmelCase__				:					Optional[Any] 					=	[flax_models[t] for t in all_models]

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

   UpperCAmelCase__				:					Optional[int] 					=	[processors[t] for t in all_models]

   return pd.DataFrame(_A     )







def __UpperCamelCase( _A       :					Tuple     ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	[
       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__				:					Tuple 					=	[model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
      UpperCAmelCase__				:					Any 					=	[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__				:					str 					=	[]
         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       :					Tuple	,      _A       :					List[str]     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[str] 					=	get_frameworks_table()
   UpperCAmelCase__				:					int 					=	Dataset.from_pandas(_A     )

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

   # Sort the model classes to avoid some nondeterministic updates to create false update commits.
   UpperCAmelCase__				:					Dict 					=	sorted(table.keys()     )
   UpperCAmelCase__				:					Optional[Any] 					=	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__				:					str 					=	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__				:					int 					=	(
             F'''Update with commit {commit_sha}\n\nSee: '''
             F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
         )
      else:
         UpperCAmelCase__				:					Optional[int] 					=	'''Update'''

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







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Union[str, Any] 					=	{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
   UpperCAmelCase__				:					Dict 					=	transformers_module.pipelines.SUPPORTED_TASKS
   UpperCAmelCase__				:					List[str] 					=	[]
   for key in pipeline_tasks:
      if key not in in_table:
         UpperCAmelCase__				:					str 					=	pipeline_tasks[key]['''pt''']
         if isinstance(_A	,      (list, tuple)     ):
            UpperCAmelCase__				:					Optional[Any] 					=	model[0]
         UpperCAmelCase__				:					List[Any] 					=	model.__name__
         if model not in in_table.values():
            missing.append(_A     )

   if len(_A     ) > 0:
      UpperCAmelCase__				:					List[Any] 					=	''', '''.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__":
  UpperCamelCase__  :						Tuple					=      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.')
  UpperCamelCase__  :						List[Any]					=      parser.parse_args()

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





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__  :						str					=      {
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__  :						str					=      [
      'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'GitForCausalLM',
      'GitModel',
      'GitPreTrainedModel',
      'GitVisionModel',
  ]

if TYPE_CHECKING:
  from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
  from .processing_git import GitProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_git import (
        GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GitForCausalLM,
        GitModel,
        GitPreTrainedModel,
        GitVisionModel,
    )

else:
  import sys

  UpperCamelCase__  :						Tuple					=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




