




"""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
a							      =     '''src/transformers'''
a							      =     '''docs/source/en'''
a							      =     '''.'''






def 		_snake_case	(   _snake_case				:   Tuple							,    _snake_case				:   Dict							,    _snake_case				:   Tuple				)       ->   Optional[Any]:






      '''simple docstring'''


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

      _A     =  start_index
      while not lines[end_index].startswith(_snake_case				):
            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 |
a							      =     '''Model|Encoder|Decoder|ForConditionalGeneration'''
# 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)''')


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






def 		_snake_case	(   _snake_case				:   Dict				)       ->   List[str]:






      '''simple docstring'''


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






def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   Any				)       ->   int:






      '''simple docstring'''


      _A     =  2 if text == '✅' or text == '❌' else len(_snake_case				)
      _A     =  (width - text_length) // 2
      _A     =  width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent






def 		_snake_case	(   )       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      _A     =  {
          name: config_maping_names[code]
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if code in config_maping_names
      }
      _A     =  {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.
      _A     =  collections.defaultdict(_snake_case				)
      _A     =  collections.defaultdict(_snake_case				)
      _A     =  collections.defaultdict(_snake_case				)
      _A     =  collections.defaultdict(_snake_case				)
      _A     =  collections.defaultdict(_snake_case				)

      # Let's lookup through all transformers object (once).
      for attr_name in dir(_snake_case				):
            _A     =  None
            if attr_name.endswith('Tokenizer'				):
                  _A     =  slow_tokenizers
                  _A     =  attr_name[:-9]
            elif attr_name.endswith('TokenizerFast'				):
                  _A     =  fast_tokenizers
                  _A     =  attr_name[:-13]
            elif _re_tf_models.match(_snake_case				) is not None:
                  _A     =  tf_models
                  _A     =  _re_tf_models.match(_snake_case				).groups()[0]
            elif _re_flax_models.match(_snake_case				) is not None:
                  _A     =  flax_models
                  _A     =  _re_flax_models.match(_snake_case				).groups()[0]
            elif _re_pt_models.match(_snake_case				) is not None:
                  _A     =  pt_models
                  _A     =  _re_pt_models.match(_snake_case				).groups()[0]

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

    # Let's build that table!
      _A     =  list(model_name_to_config.keys()				)
      model_names.sort(key=str.lower				)
      _A     =  ['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).
      _A     =  [len(_snake_case				) + 2 for c in columns]
      _A     =  max([len(_snake_case				) for name in model_names]				) + 2

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

      _A     =  {True: '✅', False: '❌'}
      for name in model_names:
            _A     =  model_name_to_prefix[name]
            _A     =  [
                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(_snake_case							,    _snake_case				) for l, w in zip(_snake_case							,    _snake_case				)]				) + "|\n"
      return table






def 		_snake_case	(   _snake_case				:   List[str]=False				)       ->   Union[str, Any]:






      '''simple docstring'''


      _A  ,       _A  ,       _A  ,       _A     =  _find_text_in_file(
          filename=os.path.join(_snake_case							,    'index.md'				)							,    start_prompt='<!--This table is updated automatically from the auto modules'							,    end_prompt='<!-- End table-->'							,    )
      _A     =  get_model_table_from_auto_modules()

      if current_table != new_table:
            if overwrite:
                  with open(os.path.join(_snake_case							,    '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__":
       a							      =     argparse.ArgumentParser()
       parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
       a							      =     parser.parse_args()

       check_model_table(args.fix_and_overwrite)







"""simple docstring"""

import math






def 		_snake_case	(   _snake_case				:   float							,    _snake_case				:   float				)       ->   float:






      '''simple docstring'''


      if (
          not isinstance(_snake_case							,    (int, float)				)
          or power_factor < -1
          or power_factor > 1
      ):
            raise ValueError('power_factor must be a valid float value between -1 and 1.'				)
      return apparent_power * power_factor






def 		_snake_case	(   _snake_case				:   float							,    _snake_case				:   float				)       ->   float:






      '''simple docstring'''


      if (
          not isinstance(_snake_case							,    (int, float)				)
          or power_factor < -1
          or power_factor > 1
      ):
            raise ValueError('power_factor must be a valid float value between -1 and 1.'				)
      return apparent_power * math.sqrt(1 - power_factor**2				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

