



"""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
lowercase__		:				Any          =	"""src/transformers"""


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


# Regexes that match TF/Flax/PT model names.
lowercase__		:				int          =	re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowercase__		:				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.
lowercase__		:				Tuple          =	re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowercase__		:				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_    (    lowerCAmelCase__      :				List[Any]						)						->   Optional[int]:

  """simple docstring"""




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

def 				UpperCamelCase_    (    )						->   List[Any]:

  """simple docstring"""




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

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

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

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

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

  return pd.DataFrame(lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]						)						->   str:

  """simple docstring"""




  lowerCAmelCase_   :				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:
    lowerCAmelCase_   :				Union[str, Any]								= [model_mapping, f"TF_{model_mapping}", f"FLAX_{model_mapping}"]
    lowerCAmelCase_   :				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
      lowerCAmelCase_   :				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 				UpperCamelCase_    (    lowerCAmelCase__      :				Tuple    ,   lowerCAmelCase__      :				List[str]						)						->   Any:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= get_frameworks_table()
  lowerCAmelCase_   :				Any								= Dataset.from_pandas(lowerCAmelCase__						)

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

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

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

def 				UpperCamelCase_    (    )						->   str:

  """simple docstring"""




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

  if len(lowerCAmelCase__						) > 0:
    lowerCAmelCase_   :				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__":
     lowercase__		:				str          =	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.""")
     lowercase__		:				List[str]          =	parser.parse_args()

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










"""simple docstring"""







from __future__ import annotations

def 				UpperCamelCase_    (    lowerCAmelCase__      :				list[int]    ,   lowerCAmelCase__      :				int						)						->   bool:

  """simple docstring"""




  if len(lowerCAmelCase__						) == 0:
    return False
  lowerCAmelCase_   :				Union[str, Any]								= len(lowerCAmelCase__						) // 2
  if a_list[midpoint] == item:
    return True
  if item < a_list[midpoint]:
    return binary_search(a_list[:midpoint]    ,   lowerCAmelCase__						)
  else:
    return binary_search(a_list[midpoint + 1 :]    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:				str          =	input("""Enter numbers separated by comma:\n""").strip()
     lowercase__		:				Optional[int]          =	[int(item.strip()) for item in user_input.split(""",""")]
     lowercase__		:				Optional[Any]          =	int(input("""Enter the number to be found in the list:\n""").strip())
     lowercase__		:				int          =	"""""" if binary_search(sequence, target) else """not """
     print(f'{target} was {not_str}found in {sequence}')





