


'''simple docstring'''






from __future__ import annotations

from typing import Any




class  a     :
 def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = 0						)					->							List[Any]:
      _a			      =							row, column
      _a			      =							[[default_value for c in range(__magic_name__						)] for r in range(__magic_name__						)]
 def __str__( self						)					->							List[str]:
      _a			      =							f'Matrix consist of {self.row} rows and {self.column} columns\n'

      # Make string identifier
      _a			      =							0
      for row_vector in self.array:
           for obj in row_vector:
                _a			      =							max(__magic_name__      ,				len(str(__magic_name__						)						)						)
      _a			      =							f'%{max_element_length}s'

      # Make string and return
      def single_line(__magic_name__						) -> str:
           nonlocal string_format_identifier
           _a			      =							'['
           line += ", ".join(string_format_identifier % (obj,) for obj in row_vector						)
           line += "]"
           return line

      s += "\n".join(single_line(__magic_name__						) for row_vector in self.array						)
      return s
 def __repr__( self						)					->							int:
      return str(self						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							int:







      if not (isinstance(__magic_name__      ,				(list, tuple)						) and len(__magic_name__						) == 2):
           return False
      elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
           return False
      else:
           return True
 def __getitem__( self      ,				__magic_name__						)					->							Optional[int]:
      assert self.validate_indicies(__magic_name__						)
      return self.array[loc[0]][loc[1]]
 def __setitem__( self      ,				__magic_name__      ,				__magic_name__						)					->							Any:
      assert self.validate_indicies(__magic_name__						)
      _a			      =							value
 def __add__( self      ,				__magic_name__						)					->							Tuple:
      assert isinstance(__magic_name__      ,				__magic_name__						)
      assert self.row == another.row and self.column == another.column

      # Add
      _a			      =							Matrix(self.row      ,				self.column						)
      for r in range(self.row						):
           for c in range(self.column						):
                _a			      =							self[r, c] + another[r, c]
      return result
 def __neg__( self						)					->							str:
      _a			      =							Matrix(self.row      ,				self.column						)
      for r in range(self.row						):
           for c in range(self.column						):
                _a			      =							-self[r, c]
      return result
 def __sub__( self      ,				__magic_name__						)					->							Optional[Any]:
      return self + (-another)
 def __mul__( self      ,				__magic_name__						)					->							Union[str, Any]:







      if isinstance(__magic_name__      ,				(int, float)						):  # Scalar multiplication
           _a			      =							Matrix(self.row      ,				self.column						)
           for r in range(self.row						):
                for c in range(self.column						):
                     _a			      =							self[r, c] * another
           return result
      elif isinstance(__magic_name__      ,				__magic_name__						):  # Matrix multiplication
           assert self.column == another.row
           _a			      =							Matrix(self.row      ,				another.column						)
           for r in range(self.row						):
                for c in range(another.column						):
                     for i in range(self.column						):
                          result[r, c] += self[r, i] * another[i, c]
           return result
      else:
           _a			      =							f'Unsupported type given for another ({type(__magic_name__						)})'
           raise TypeError(__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							Matrix(self.column      ,				self.row						)
      for r in range(self.row						):
           for c in range(self.column						):
                _a			      =							self[r, c]
      return result




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Tuple:
      assert isinstance(__magic_name__      ,				__magic_name__						) and isinstance(__magic_name__      ,				__magic_name__						)
      assert self.row == self.column == u.row == v.row  # u, v should be column vector
      assert u.column == v.column == 1  # u, v should be column vector

      # Calculate
      _a			      =							v.transpose()
      _a			      =							(v_t * self * u)[0, 0] + 1
      if numerator_factor == 0:
           return None  # It's not invertable
      return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":






 def 	_A				()   ->  None:




      '''simple docstring'''



      _a			      =							Matrix(3				,						3				,						0		)
      for i in range(3		):
           _a			      =							1
      print(f'a^(-1) is {ainv}'		)
      # u, v
      _a			      =							Matrix(3				,						1				,						0		)
      _a			      =							1, 2, -3
      _a			      =							Matrix(3				,						1				,						0		)
      _a			      =							4, -2, 5
      print(f'u is {u}'		)
      print(f'v is {v}'		)
      print(f'uv^T is {u * v.transpose()}'		)
      # Sherman Morrison
      print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_		)}'		)






 def 	_A				()   ->  None:




      '''simple docstring'''



      import doctest

      doctest.testmod()






 testa()





'''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_					:    int          =    "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
a_					:    Optional[int]          =    direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
a_					:    str          =    re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
a_					:    Union[str, Any]          =    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_					:    List[Any]          =    re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a_					:    int          =    [
    ("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 	_A				(lowerCAmelCase__  :List[Any]		)   ->  str:




     '''simple docstring'''



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






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




     '''simple docstring'''



     _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 	_A				(lowerCAmelCase__  :Dict		)   ->  str:




     '''simple docstring'''



     _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 	_A				(lowerCAmelCase__  :Any				,						lowerCAmelCase__  :str		)   ->  Union[str, Any]:




     '''simple docstring'''



     _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 	_A				()   ->  str:




     '''simple docstring'''



     _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_					:    Dict          =    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_					:    Union[str, Any]          =    parser.parse_args()

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

