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_     (		lowercase:							List[str]		)    ->				str:







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




def       lowerCAmelCase_     (		)    ->				List[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 _UpperCamelCase:     List[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.
 _UpperCamelCase:     Optional[int]							= collections.defaultdict(lowercase		)
 _UpperCamelCase:     int							= collections.defaultdict(lowercase		)
 _UpperCamelCase:     Optional[int]							= collections.defaultdict(lowercase		)

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

  if lookup_dict is not None:
   while len(lowercase		) > 0:
    if attr_name in model_prefix_to_model_type:
     _UpperCamelCase:     Union[str, Any]							= True
     break
    # Try again after removing the last word in the name
    _UpperCamelCase:     Tuple							= ''''''.join(camel_case_split(lowercase		)[:-1]		)

 _UpperCamelCase:     Optional[Any]							= set(list(pt_models.keys()		) + list(tf_models.keys()		) + list(flax_models.keys()		)		)
 _UpperCamelCase:     Union[str, Any]							= list(lowercase		)
 all_models.sort()

 _UpperCamelCase:     List[Any]							= {'''model_type''': all_models}
 _UpperCamelCase:     Optional[Any]							= [pt_models[t] for t in all_models]
 _UpperCamelCase:     Optional[Any]							= [tf_models[t] for t in all_models]
 _UpperCamelCase:     List[Any]							= [flax_models[t] for t in all_models]

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

 _UpperCamelCase:     Optional[Any]							= [processors[t] for t in all_models]

 return pd.DataFrame(lowercase		)




def       lowerCAmelCase_     (		lowercase:							str		)    ->				Union[str, Any]:







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

            # 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_     (		lowercase:							Union[str, Any]  ,					lowercase:							Any		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase:     str							= get_frameworks_table()
 _UpperCamelCase:     List[str]							= Dataset.from_pandas(lowercase		)

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

 # Sort the model classes to avoid some nondeterministic updates to create false update commits.
 _UpperCamelCase:     Any							= sorted(table.keys()		)
 _UpperCamelCase:     int							= 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],
     }		)
 _UpperCamelCase:     Optional[Any]							= Dataset.from_pandas(lowercase		)

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

  if commit_sha is not None:
   _UpperCamelCase:     int							= (
       F"""Update with commit {commit_sha}\n\nSee: """
       F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
   )
  else:
   _UpperCamelCase:     Optional[Any]							= '''Update'''

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




def       lowerCAmelCase_     (		)    ->				Optional[int]:







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

 if len(lowercase		) > 0:
  _UpperCamelCase:     List[str]							= ''', '''.join(lowercase		)
  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)
from __future__ import annotations




def       lowerCAmelCase_     (		lowercase:							int = 4		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     Dict							= abs(lowercase		) or 4
 return [[1 + x + y * row_size for x in range(lowercase		)] for y in range(lowercase		)]




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_row(transpose(lowercase		)		)
 # OR.. transpose(reverse_column(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_row(reverse_column(lowercase		)		)
 # OR.. reverse_column(reverse_row(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 return reverse_column(transpose(lowercase		)		)
 # OR.. transpose(reverse_row(matrix))




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     int							= [list(lowercase		) for x in zip(*lowercase		)]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     str							= matrix[::-1]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				list[list[int]]:







 '''simple docstring'''
 _UpperCamelCase:     str							= [x[::-1] for x in matrix]
 return matrix




def       lowerCAmelCase_     (		lowercase:							list[list[int]]		)    ->				None:







 '''simple docstring'''
 for i in matrix:
  print(*lowercase		)


if __name__ == "__main__":
  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 90 counterclockwise:\n''')
  print_matrix(rotate_aa(matrix))

  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 180:\n''')
  print_matrix(rotate_aaa(matrix))

  UpperCAmelCase_			    =							make_matrix()
  print('''\norigin:\n''')
  print_matrix(matrix)
  print('''\nrotate 270 counterclockwise:\n''')
  print_matrix(rotate_aaa(matrix))