


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    =					"""src/transformers"""


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
UpperCAmelCase    =					[
    ("""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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Optional[int]:




      """simple docstring"""

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






def  __lowerCAmelCase					()->						List[str]:




      """simple docstring"""

      snake_case_ =   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      snake_case_ =   {
          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_ =   collections.defaultdict(SCREAMING_SNAKE_CASE      )
      snake_case_ =   collections.defaultdict(SCREAMING_SNAKE_CASE      )
      snake_case_ =   collections.defaultdict(SCREAMING_SNAKE_CASE      )

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

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

      snake_case_ =   set(list(pt_models.keys()      ) + list(tf_models.keys()      ) + list(flax_models.keys()      )      )
      snake_case_ =   list(SCREAMING_SNAKE_CASE      )
      all_models.sort()

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

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

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

      return pd.DataFrame(SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Optional[int]:




      """simple docstring"""

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

            # 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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

      snake_case_ =   get_frameworks_table()
      snake_case_ =   Dataset.from_pandas(SCREAMING_SNAKE_CASE      )

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

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

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

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

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






def  __lowerCAmelCase					()->						Any:




      """simple docstring"""

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

      if len(SCREAMING_SNAKE_CASE      ) > 0:
            snake_case_ =   ''', '''.join(SCREAMING_SNAKE_CASE      )
            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    =					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    =					parser.parse_args()

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



import numpy as np

import datasets


UpperCAmelCase    =					"""
Compute the Mahalanobis Distance

Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
"""

UpperCAmelCase    =					"""\
@article{de2000mahalanobis,
  title={The mahalanobis distance},
  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
  journal={Chemometrics and intelligent laboratory systems},
  volume={50},
  number={1},
  pages={1--18},
  year={2000},
  publisher={Elsevier}
}
"""

UpperCAmelCase    =					"""
Args:
    X: List of datapoints to be compared with the `reference_distribution`.
    reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
    mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:

    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")
    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
    >>> print(results)
    {'mahalanobis': array([0.5])}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,		_KWARGS_DESCRIPTION )
class lowerCAmelCase_		(				datasets.Metric ):

 '''simple docstring'''

 def 		UpperCamelCase__  (			self      ):
       return datasets.MetricInfo(
           description=_DESCRIPTION	,   citation=_CITATION	,   inputs_description=_KWARGS_DESCRIPTION	,   features=datasets.Features(
               {
                   '''X''': datasets.Sequence(datasets.Value('''float'''	,   id='''sequence'''      )	,   id='''X'''      ),
               }      )	,   )





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       # convert to numpy arrays
       snake_case_ =   np.array(_UpperCAmelCase      )
       snake_case_ =   np.array(_UpperCAmelCase      )

       # Assert that arrays are 2D
       if len(X.shape      ) != 2:
             raise ValueError('''Expected `X` to be a 2D vector'''      )
       if len(reference_distribution.shape      ) != 2:
             raise ValueError('''Expected `reference_distribution` to be a 2D vector'''      )
       if reference_distribution.shape[0] < 2:
             raise ValueError(
                 '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension'''      )

       # Get mahalanobis distance for each prediction
       snake_case_ =   X - np.mean(_UpperCAmelCase      )
       snake_case_ =   np.cov(reference_distribution.T      )
       try:
             snake_case_ =   np.linalg.inv(_UpperCAmelCase      )
       except np.linalg.LinAlgError:
             snake_case_ =   np.linalg.pinv(_UpperCAmelCase      )
       snake_case_ =   np.dot(_UpperCAmelCase	,   _UpperCAmelCase      )
       snake_case_ =   np.dot(_UpperCAmelCase	,   X_minus_mu.T      ).diagonal()

       return {"mahalanobis": mahal_dist}