



'''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(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    Any    						=      re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""     ,   lowerCAmelCase__		)
      return [m.group(0		) for m in matches]





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

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

      snake_case__  :    Optional[int]    						=      {'model_type': all_models}
      snake_case__  :    str    						=      [pt_models[t] for t in all_models]
      snake_case__  :    Tuple    						=      [tf_models[t] for t in all_models]
      snake_case__  :    Union[str, Any]    						=      [flax_models[t] for t in all_models]

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

      snake_case__  :    int    						=      [processors[t] for t in all_models]

      return pd.DataFrame(lowerCAmelCase__		)





def 						__snake_case(				_lowerCAmelCase		)		-> str:
      snake_case__  :    Union[str, Any]    						=      [
          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:
            snake_case__  :    Union[str, Any]    						=      [model_mapping, f"TF_{model_mapping}", f"FLAX_{model_mapping}"]
            snake_case__  :    Union[str, Any]    						=      [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
                  snake_case__  :    Dict    						=      []
                  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 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      snake_case__  :    Any    						=      get_frameworks_table()
      snake_case__  :    Any    						=      Dataset.from_pandas(lowerCAmelCase__		)

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

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

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





def 						__snake_case(				)		-> str:
      snake_case__  :    Optional[Any]    						=      {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
      snake_case__  :    Optional[Any]    						=      transformers_module.pipelines.SUPPORTED_TASKS
      snake_case__  :    Tuple    						=      []
      for key in pipeline_tasks:
            if key not in in_table:
                  snake_case__  :    List[str]    						=      pipeline_tasks[key]['pt']
                  if isinstance(lowerCAmelCase__     ,   (list, tuple)		):
                        snake_case__  :    Any    						=      model[0]
                  snake_case__  :    Tuple    						=      model.__name__
                  if model not in in_table.values():
                        missing.append(lowerCAmelCase__		)

      if len(lowerCAmelCase__		) > 0:
            snake_case__  :    int    						=      ', '.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          =		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 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]						)						->   Optional[int]:

  """simple docstring"""




  lowerCAmelCase_   :				Tuple								= [0] * len(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[str]								= []
  lowerCAmelCase_   :				Tuple								= [1] * len(lowerCAmelCase__						)

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(lowerCAmelCase__						)						):
    if indegree[i] == 0:
      queue.append(lowerCAmelCase__						)

  while queue:
    lowerCAmelCase_   :				Union[str, Any]								= queue.pop(0						)
    for x in graph[vertex]:
      indegree[x] -= 1

      if long_dist[vertex] + 1 > long_dist[x]:
        lowerCAmelCase_   :				Any								= long_dist[vertex] + 1

      if indegree[x] == 0:
        queue.append(lowerCAmelCase__						)

  print(max(lowerCAmelCase__						)						)


# Adjacency list of Graph
lowercase__		:				Any          =	{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)





