


'''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
lowerCamelCase				:  Tuple    		=      '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				:  int    		=      re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCamelCase				:  int    		=      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				:  List[Any]    		=      [
    ('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 (A			)			->			Any:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE ()			->			str:






       """simple docstring"""







       lowercase__      =					transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       lowercase__      =					{
           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.
       lowercase__      =					collections.defaultdict(A			)
       lowercase__      =					collections.defaultdict(A			)
       lowercase__      =					collections.defaultdict(A			)

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

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

       lowercase__      =					set(list(pt_models.keys()			) + list(tf_models.keys()			) + list(flax_models.keys()			)			)
       lowercase__      =					list(A			)
       all_models.sort()

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

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

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

       return pd.DataFrame(A			)

def _SCREAMING_SNAKE_CASE (A			)			->			Dict:






       """simple docstring"""







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

            # 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 (A    ,					A			)			->			Optional[Any]:






       """simple docstring"""







       lowercase__      =					get_frameworks_table()
       lowercase__      =					Dataset.from_pandas(A			)

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

       # Sort the model classes to avoid some nondeterministic updates to create false update commits.
       lowercase__      =					sorted(table.keys()			)
       lowercase__      =					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],
           }			)
       lowercase__      =					Dataset.from_pandas(A			)

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

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

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

def _SCREAMING_SNAKE_CASE ()			->			Optional[int]:






       """simple docstring"""







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

       if len(A			) > 0:
              lowercase__      =					''', '''.join(A			)
              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				:  Tuple    		=      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				:  Any    		=      parser.parse_args()

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








'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			list:






       """simple docstring"""







       lowercase__      =					len(A			)
       lowercase__      =					[[0] * n for i in range(A			)]
       for i in range(A			):
              lowercase__      =					y_points[i]

       for i in range(2    ,					A			):
              for j in range(A    ,					A			):
                     lowercase__      =					(
                         (xa - x_points[j - i + 1]) * q[j][i - 1]
                         - (xa - x_points[j]) * q[j - 1][i - 1]
                     ) / (x_points[j] - x_points[j - i + 1])

       return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




