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







def 					UpperCAmelCase    (		)				->      Dict:
     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(UpperCAmelCase					)
     snake_case_					       =					collections.defaultdict(UpperCAmelCase					)
     snake_case_					       =					collections.defaultdict(UpperCAmelCase					)

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

          if lookup_dict is not None:
               while len(UpperCAmelCase					) > 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(UpperCAmelCase					)[:-1]					)

     snake_case_					       =					set(list(pt_models.keys()					) + list(tf_models.keys()					) + list(flax_models.keys()					)					)
     snake_case_					       =					list(UpperCAmelCase					)
     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(UpperCAmelCase					)







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Dict:
     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(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					):
               # The type of pipeline may not exist in this framework
               if not hasattr(UpperCAmelCase						,					UpperCAmelCase					):
                    continue
               # First extract all model_names
               snake_case_					       =					[]
               for name in getattr(UpperCAmelCase						,					UpperCAmelCase					).values():
                    if isinstance(UpperCAmelCase						,					UpperCAmelCase					):
                         model_names.append(UpperCAmelCase					)
                    else:
                         model_names.extend(list(UpperCAmelCase					)					)

            # 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 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      Any:
     snake_case_					       =					get_frameworks_table()
     snake_case_					       =					Dataset.from_pandas(UpperCAmelCase					)

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

     # 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(UpperCAmelCase					)

     with tempfile.TemporaryDirectory() as tmp_dir:
          frameworks_dataset.to_json(os.path.join(UpperCAmelCase						,					'frameworks.json'					)					)
          tags_dataset.to_json(os.path.join(UpperCAmelCase						,					'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=UpperCAmelCase						,					repo_type='dataset'						,					token=UpperCAmelCase						,					commit_message=UpperCAmelCase						,					)







def 					UpperCAmelCase    (		)				->      int:
     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(UpperCAmelCase						,					(list, tuple)					):
                    snake_case_					       =					model[0]
               snake_case_					       =					model.__name__
               if model not in in_table.values():
                    missing.append(UpperCAmelCase					)

     if len(UpperCAmelCase					) > 0:
          snake_case_					       =					', '.join(UpperCAmelCase					)
          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)


"""simple docstring"""







def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list      ):

     if len(__SCREAMING_SNAKE_CASE      ) < 2:
          return collection

     def circle_sort_util(__SCREAMING_SNAKE_CASE							:     list     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int      ) -> bool:
          lowercase_	:				Any													=	False

          if low == high:
               return swapped

          lowercase_	:				str													=	low
          lowercase_	:				int													=	high

          while left < right:
               if collection[left] > collection[right]:
                    lowercase_							,  lowercase_	:				Optional[Any]													=	(
                        collection[right],
                        collection[left],
                    )
                    lowercase_	:				Tuple													=	True

               left += 1
               right -= 1

          if left == right and collection[left] > collection[right + 1]:
               lowercase_							,  lowercase_	:				Dict													=	(
                   collection[right + 1],
                   collection[left],
               )

               lowercase_	:				str													=	True

          lowercase_	:				Optional[Any]													=	low + int((high - low) / 2      )
          lowercase_	:				str													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
          lowercase_	:				List[Any]													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			mid + 1     ,			__SCREAMING_SNAKE_CASE      )

          return swapped or left_swap or right_swap

     lowercase_	:				Dict													=	True

     while is_not_sorted is True:
          lowercase_	:				Optional[Any]													=	circle_sort_util(__SCREAMING_SNAKE_CASE     ,			0     ,			len(__SCREAMING_SNAKE_CASE      ) - 1      )

     return collection


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=input("Enter numbers separated by a comma:\n").strip()
       __SCREAMING_SNAKE_CASE    	=[int(item) for item in user_input.split(",")]
       print(circle_sort(unsorted))

