

import math

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int					):


      '''simple docstring'''



      return math.sqrt(lowercase					) * math.sqrt(lowercase					) == num

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	int					):


      '''simple docstring'''



      lowerCamelCase_							=   0
      lowerCamelCase_							=   n
      while left <= right:
            lowerCamelCase_							=   (left + right) // 2
            if mid**2 == n:
                  return True
            elif mid**2 > n:
                  lowerCamelCase_							=   mid - 1
            else:
                  lowerCamelCase_							=   mid + 1
      return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()









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
lowerCamelCase    :		List[Any]			   =       "src/transformers"


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


# Regexes that match TF/Flax/PT model names.
lowerCamelCase    :		Optional[Any]			   =       re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowerCamelCase    :		List[Any]			   =       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.
lowerCamelCase    :		Union[str, Any]			   =       re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCamelCase    :		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       _SCREAMING_SNAKE_CASE      (							lowercase     :	int					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      lowerCamelCase_							=   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      lowerCamelCase_							=   {
          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_							=   collections.defaultdict(lowercase					)
      lowerCamelCase_							=   collections.defaultdict(lowercase					)
      lowerCamelCase_							=   collections.defaultdict(lowercase					)

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

            if lookup_dict is not None:
                  while len(lowercase					) > 0:
                        if attr_name in model_prefix_to_model_type:
                              lowerCamelCase_							=   True
                              break
                        # Try again after removing the last word in the name
                        lowerCamelCase_							=   ''.join(camel_case_split(lowercase					)[:-1]					)

      lowerCamelCase_							=   set(list(pt_models.keys()					) + list(tf_models.keys()					) + list(flax_models.keys()					)					)
      lowerCamelCase_							=   list(lowercase					)
      all_models.sort()

      lowerCamelCase_							=   {'model_type': all_models}
      lowerCamelCase_							=   [pt_models[t] for t in all_models]
      lowerCamelCase_							=   [tf_models[t] for t in all_models]
      lowerCamelCase_							=   [flax_models[t] for t in all_models]

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

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

      return pd.DataFrame(lowercase					)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_							=   [
          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_							=   [model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
            lowerCamelCase_							=   [auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
            # Loop through all three frameworks
            for module, cls, mapping in zip(lowercase     ,							lowercase     ,							lowercase					):
                  # The type of pipeline may not exist in this framework
                  if not hasattr(lowercase     ,							lowercase					):
                        continue
                  # First extract all model_names
                  lowerCamelCase_							=   []
                  for name in getattr(lowercase     ,							lowercase					).values():
                        if isinstance(lowercase     ,							lowercase					):
                              model_names.append(lowercase					)
                        else:
                              model_names.extend(list(lowercase					)					)

            # 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       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	Optional[int]					):


      '''simple docstring'''



      lowerCamelCase_							=   get_frameworks_table()
      lowerCamelCase_							=   Dataset.from_pandas(lowercase					)

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

      # Sort the model classes to avoid some nondeterministic updates to create false update commits.
      lowerCamelCase_							=   sorted(table.keys()					)
      lowerCamelCase_							=   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_							=   Dataset.from_pandas(lowercase					)

      with tempfile.TemporaryDirectory() as tmp_dir:
            frameworks_dataset.to_json(os.path.join(lowercase     ,							'frameworks.json'					)					)
            tags_dataset.to_json(os.path.join(lowercase     ,							'pipeline_tags.json'					)					)

            if commit_sha is not None:
                  lowerCamelCase_							=   (
                      f"""Update with commit {commit_sha}\n\nSee: """
                      f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
                  )
            else:
                  lowerCamelCase_							=   'Update'

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

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



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

      if len(lowercase					) > 0:
            lowerCamelCase_							=   ', '.join(lowercase					)
            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__":
 lowerCamelCase    :		List[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.")
 lowerCamelCase    :		Optional[int]			   =       parser.parse_args()

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



