"""simple docstring"""





import argparse
import collections
import os
import re

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/check_table.py
_UpperCamelCase     :		List[Any]											=					"src/transformers"
_UpperCamelCase     :		List[str]											=					"docs/source/en"
_UpperCamelCase     :		List[Any]											=					"."





def        a_  (   _lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				List[Any]  ):





       '''simple docstring'''

       with open(_lowerCAmelCase     ,				'r'     ,				encoding='utf-8'     ,				newline='\n'  ) as f:
              lowercase__    :	List[Any]     					=			f.readlines()
       # Find the start prompt.
       lowercase__    :	Dict     					=			0
       while not lines[start_index].startswith(_lowerCAmelCase  ):
              start_index += 1
       start_index += 1

       lowercase__    :	Any     					=			start_index
       while not lines[end_index].startswith(_lowerCAmelCase  ):
              end_index += 1
       end_index -= 1

       while len(lines[start_index]  ) <= 1:
              start_index += 1
       while len(lines[end_index]  ) <= 1:
              end_index -= 1
       end_index += 1
       return "".join(lines[start_index:end_index]  ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
_UpperCamelCase     :		List[str]											=					"Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
_UpperCamelCase     :		str											=					re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_UpperCamelCase     :		Tuple											=					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     :		Union[str, Any]											=					re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
_UpperCamelCase     :		Union[str, Any]											=					direct_transformers_import(TRANSFORMERS_PATH)





def        a_  (   _lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

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





def        a_  (   _lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				str  ):





       '''simple docstring'''

       lowercase__    :	Dict     					=			2 if text == '✅' or text == '❌' else len(_lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			(width - text_length) // 2
       lowercase__    :	int     					=			width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       lowercase__    :	Union[str, Any]     					=			{
           name: config_maping_names[code]
           for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
           if code in config_maping_names
       }
       lowercase__    :	int     					=			{name: config.replace('Config'     ,				''  ) for name, config in model_name_to_config.items()}

       # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
       lowercase__    :	List[str]     					=			collections.defaultdict(_lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			collections.defaultdict(_lowerCAmelCase  )
       lowercase__    :	Dict     					=			collections.defaultdict(_lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			collections.defaultdict(_lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			collections.defaultdict(_lowerCAmelCase  )

       # Let's lookup through all transformers object (once).
       for attr_name in dir(_lowerCAmelCase  ):
              lowercase__    :	List[Any]     					=			None
              if attr_name.endswith('Tokenizer'  ):
                     lowercase__    :	List[Any]     					=			slow_tokenizers
                     lowercase__    :	Any     					=			attr_name[:-9]
              elif attr_name.endswith('TokenizerFast'  ):
                     lowercase__    :	Any     					=			fast_tokenizers
                     lowercase__    :	Tuple     					=			attr_name[:-13]
              elif _re_tf_models.match(_lowerCAmelCase  ) is not None:
                     lowercase__    :	Optional[int]     					=			tf_models
                     lowercase__    :	str     					=			_re_tf_models.match(_lowerCAmelCase  ).groups()[0]
              elif _re_flax_models.match(_lowerCAmelCase  ) is not None:
                     lowercase__    :	Union[str, Any]     					=			flax_models
                     lowercase__    :	Any     					=			_re_flax_models.match(_lowerCAmelCase  ).groups()[0]
              elif _re_pt_models.match(_lowerCAmelCase  ) is not None:
                     lowercase__    :	Union[str, Any]     					=			pt_models
                     lowercase__    :	Dict     					=			_re_pt_models.match(_lowerCAmelCase  ).groups()[0]

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

    # Let's build that table!
       lowercase__    :	str     					=			list(model_name_to_config.keys()  )
       model_names.sort(key=str.lower  )
       lowercase__    :	str     					=			['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
       # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
       lowercase__    :	Dict     					=			[len(_lowerCAmelCase  ) + 2 for c in columns]
       lowercase__    :	List[str]     					=			max([len(_lowerCAmelCase  ) for name in model_names]  ) + 2

       # Build the table per se
       lowercase__    :	Optional[int]     					=			'|' + '|'.join([_center_text(_lowerCAmelCase     ,				_lowerCAmelCase  ) for c, w in zip(_lowerCAmelCase     ,				_lowerCAmelCase  )]  ) + '|\n'
       # Use ":-----:" format to center-aligned table cell texts
       table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]  ) + "|\n"

       lowercase__    :	Tuple     					=			{True: '✅', False: '❌'}
       for name in model_names:
              lowercase__    :	Tuple     					=			model_name_to_prefix[name]
              lowercase__    :	Optional[int]     					=			[
                  name,
                  check[slow_tokenizers[prefix]],
                  check[fast_tokenizers[prefix]],
                  check[pt_models[prefix]],
                  check[tf_models[prefix]],
                  check[flax_models[prefix]],
              ]
              table += "|" + "|".join([_center_text(_lowerCAmelCase     ,				_lowerCAmelCase  ) for l, w in zip(_lowerCAmelCase     ,				_lowerCAmelCase  )]  ) + "|\n"
       return table





def        a_  (   _lowerCAmelCase						:				Dict=False  ):





       '''simple docstring'''

       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	Optional[int]     					=			_find_text_in_file(
           filename=os.path.join(_lowerCAmelCase     ,				'index.md'  )     ,				start_prompt='<!--This table is updated automatically from the auto modules'     ,				end_prompt='<!-- End table-->'     ,				)
       lowercase__    :	List[str]     					=			get_model_table_from_auto_modules()

       if current_table != new_table:
              if overwrite:
                     with open(os.path.join(_lowerCAmelCase     ,				'index.md'  )     ,				'w'     ,				encoding='utf-8'     ,				newline='\n'  ) as f:
                            f.writelines(lines[:start_index] + [new_table] + lines[end_index:]  )
              else:
                     raise ValueError(
                         'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'  )


if __name__ == "__main__":
       _UpperCamelCase     :		List[str]											=					argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       _UpperCamelCase     :		str											=					parser.parse_args()

       check_model_table(args.fix_and_overwrite)


"""simple docstring"""





from __future__ import annotations





def        a_  (   _lowerCAmelCase						:				float     ,				_lowerCAmelCase						:				float     ,				_lowerCAmelCase						:				float     ,				):





       '''simple docstring'''

       if (stress, tangential_force, area).count(0  ) != 1:
              raise ValueError('You cannot supply more or less than 2 values'  )
       elif stress < 0:
              raise ValueError('Stress cannot be negative'  )
       elif tangential_force < 0:
              raise ValueError('Tangential Force cannot be negative'  )
       elif area < 0:
              raise ValueError('Area cannot be negative'  )
       elif stress == 0:
              return (
                  "stress",
                  tangential_force / area,
              )
       elif tangential_force == 0:
              return (
                  "tangential_force",
                  stress * area,
              )
       else:
              return (
                  "area",
                  tangential_force / stress,
              )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

