


"""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    =				"src/transformers"


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowercase    =				[
    ("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 					lowercase      (    A_	)->				Dict:
  '''simple docstring'''







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


def 					lowercase      (    )->				Dict:
  '''simple docstring'''







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

  a						:		List[Any]											=      set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
  a						:		Dict											=      list(lowerCamelCase_	)
  all_models.sort()

  a						:		Tuple											=      {'model_type': all_models}
  a						:		Dict											=      [pt_models[t] for t in all_models]
  a						:		List[Any]											=      [tf_models[t] for t in all_models]
  a						:		Any											=      [flax_models[t] for t in all_models]

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

  a						:		Any											=      [processors[t] for t in all_models]

  return pd.DataFrame(lowerCamelCase_	)


def 					lowercase      (    A_	)->				Any:
  '''simple docstring'''







  a						:		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:
    a						:		int											=      [model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
    a						:		List[str]											=      [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
      a						:		Union[str, Any]											=      []
      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 					lowercase      (    A_  ,       A_	)->				List[Any]:
  '''simple docstring'''







  a						:		Tuple											=      get_frameworks_table()
  a						:		Any											=      Dataset.from_pandas(lowerCamelCase_	)

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

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

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


def 					lowercase      (    )->				Optional[Any]:
  '''simple docstring'''







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

  if len(lowerCamelCase_	) > 0:
    a						:		Dict											=      ', '.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    =				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    =				parser.parse_args()

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










"""simple docstring"""







import sys
import turtle


def 					lowercase      (    A_  ,       A_	)->				tuple[float, float]:
  '''simple docstring'''







  return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       )->				None:
  '''simple docstring'''







  my_pen.up()
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.down()
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)

  if depth == 0:
    return

  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)
  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)
  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)


if __name__ == "__main__":
 if len(sys.argv) != 2:
  raise ValueError(
      """Correct format for using this script: """
      """python fractals.py <int:depth_for_fractal>"""
  )
 __lowercase    =				turtle.Turtle()
 my_pen.ht()
 my_pen.speed(5)
 my_pen.pencolor("""red""")

 __lowercase    =				[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
 triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))






