



"""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
A    =			'src/transformers'


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
A    =			[
    ('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__			(     lowerCamelCase__			) ->							Optional[Any]:
       A         =					re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'				,      lowerCamelCase__			)
       return [m.group(0			) for m in matches]

def   lowerCAmelCase__			(     ) ->							List[str]:
       A         =					transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       A         =					{
           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         =					collections.defaultdict(lowerCamelCase__			)
       A         =					collections.defaultdict(lowerCamelCase__			)
       A         =					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         =					None
              if _re_tf_models.match(lowerCamelCase__			) is not None:
                     A         =					tf_models
                     A         =					_re_tf_models.match(lowerCamelCase__			).groups()[0]
              elif _re_flax_models.match(lowerCamelCase__			) is not None:
                     A         =					flax_models
                     A         =					_re_flax_models.match(lowerCamelCase__			).groups()[0]
              elif _re_pt_models.match(lowerCamelCase__			) is not None:
                     A         =					pt_models
                     A         =					_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         =					True
                                   break
                            # Try again after removing the last word in the name
                            A         =					''.join(camel_case_split(lowerCamelCase__			)[:-1]			)

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

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

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

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

       return pd.DataFrame(lowerCamelCase__			)

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							List[str]:
       A         =					[
           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         =					[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
              A         =					[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         =					[]
                     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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							Optional[Any]:
       A         =					get_frameworks_table()
       A         =					Dataset.from_pandas(lowerCamelCase__			)

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

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

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

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

       if len(lowerCamelCase__			) > 0:
              A         =					', '.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__":
       A    =			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.')
       A    =			parser.parse_args()

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








"""simple docstring"""







import re

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							list:
							return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]'				,      str_			)]

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							A         =					split_input(str_			)
							return "".join(
							    [''.join([char.capitalize() for char in sub_str]			) for sub_str in string_split]			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							try:
														A         =					split_input(lowerCamelCase__			)
														if upper:
																					A         =					''.join(
																					    [
																					        separator.join([char.upper() for char in sub_str]			)
																					        for sub_str in string_split
																					    ]			)
														else:
																					A         =					''.join(
																					    [
																					        separator.join([char.lower() for char in sub_str]			)
																					        for sub_str in string_split
																					    ]			)
														return res_str
							except IndexError:
														return "not valid string"

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							return to_simple_case(lowerCamelCase__			)

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							try:
														A         =					to_simple_case(lowerCamelCase__			)
														return res_str[0].lower() + res_str[1:]
							except IndexError:
														return "not valid string"

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							return to_complex_case(lowerCamelCase__				,      lowerCamelCase__				,      '_'			)

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							return to_complex_case(lowerCamelCase__				,      lowerCamelCase__				,      '-'			)


if __name__ == "__main__":
							__import__('doctest').testmod()



