




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
_lowerCamelCase     :		Union[str, Any]								=       """src/transformers"""
_lowerCamelCase     :		str								=       """docs/source/en"""
_lowerCamelCase     :		Union[str, Any]								=       """."""







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Dict:



       """simple docstring"""

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

       A__							=   start_index
       while not lines[end_index].startswith(lowercase_						):
              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 |
_lowerCamelCase     :		Dict								=       """Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_lowerCamelCase     :		Dict								=       re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_lowerCamelCase     :		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.
_lowerCamelCase     :		Dict								=       re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the transformers module imported is the one in the repo.
_lowerCamelCase     :		List[Any]								=       direct_transformers_import(TRANSFORMERS_PATH)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Dict:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	str:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	)       ->	Dict:



       """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(lowercase_						)
       A__							=   collections.defaultdict(lowercase_						)
       A__							=   collections.defaultdict(lowercase_						)
       A__							=   collections.defaultdict(lowercase_						)
       A__							=   collections.defaultdict(lowercase_						)

       # Let's lookup through all transformers object (once).
       for attr_name in dir(lowercase_						):
              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(lowercase_						) is not None:
                     A__							=   tf_models
                     A__							=   _re_tf_models.match(lowercase_						).groups()[0]
              elif _re_flax_models.match(lowercase_						) is not None:
                     A__							=   flax_models
                     A__							=   _re_flax_models.match(lowercase_						).groups()[0]
              elif _re_pt_models.match(lowercase_						) is not None:
                     A__							=   pt_models
                     A__							=   _re_pt_models.match(lowercase_						).groups()[0]

              if lookup_dict is not None:
                     while len(lowercase_						) > 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(lowercase_						)[:-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(lowercase_						) + 2 for c in columns]
       A__							=   max([len(lowercase_						) for name in model_names]						) + 2

       # Build the table per se
       A__							=   '''|''' + '''|'''.join([_center_text(lowercase_			,    lowercase_						) for c, w in zip(lowercase_			,    lowercase_						)]						) + '''|\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(lowercase_			,    lowercase_						) for l, w in zip(lowercase_			,    lowercase_						)]						) + "|\n"
       return table







def SCREAMING_SNAKE_CASE    (	lowercase_=False						)       ->	Any:



       """simple docstring"""

       A__			,							A__			,							A__			,							A__							=   _find_text_in_file(
           filename=os.path.join(lowercase_			,    '''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(lowercase_			,    '''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__":
  _lowerCamelCase     :		Any								=       argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  _lowerCamelCase     :		Optional[Any]								=       parser.parse_args()

  check_model_table(args.fix_and_overwrite)







def SCREAMING_SNAKE_CASE    (	lowercase_ = 50						)       ->	int:



       """simple docstring"""

       A__							=   [1] * (length + 1)

       for row_length in range(3			,    length + 1						):
              for block_length in range(3			,    row_length + 1						):
                     for block_start in range(row_length - block_length						):
                            ways_number[row_length] += ways_number[
                                row_length - block_start - block_length - 1
                            ]

                     ways_number[row_length] += 1

       return ways_number[length]


if __name__ == "__main__":
  print(F'''{solution() = }''')


