





'''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
__lowerCamelCase						: Optional[int]		=   'src/transformers'
__lowerCamelCase						: Tuple		=   'docs/source/en'
__lowerCamelCase						: Tuple		=   '.'


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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

  _UpperCamelCase       			=start_index
  while not lines[end_index].startswith(__SCREAMING_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 |
__lowerCamelCase						: Optional[Any]		=   'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__lowerCamelCase						: str		=   re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__lowerCamelCase						: Optional[int]		=   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						: 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.
__lowerCamelCase						: str		=   direct_transformers_import(TRANSFORMERS_PATH)


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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


def 					_a   ():


  """simple docstring"""

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

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

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

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

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

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


def 					_a   (__SCREAMING_SNAKE_CASE=False   ):


  """simple docstring"""

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

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(__SCREAMING_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__":
      __lowerCamelCase						: Union[str, Any]		=   argparse.ArgumentParser()
      parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
      __lowerCamelCase						: int		=   parser.parse_args()

      check_model_table(args.fix_and_overwrite)













'''simple docstring'''






from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

__lowerCamelCase						: Optional[int]		=   TypeVar('T')







class 		UpperCAmelCase   (				Generic[T]):


  """simple docstring"""







  lowerCAmelCase_					=				42  # Cache store of keys
  lowerCAmelCase_					=				42  # References of the keys in cache
  lowerCAmelCase_					=				10  # Maximum capacity of cache


  def __init__(				self    :					Union[str, Any]							,	UpperCamelCase__    :					int  )     ->						None:
    _UpperCamelCase       			=deque()
    _UpperCamelCase       			=set()
    if not n:
      _UpperCamelCase       			=sys.maxsize
    elif n < 0:
      raise ValueError('''n should be an integer greater than 0.'''  )
    else:
      _UpperCamelCase       			=n


  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					T  )     ->						None:
    if x not in self.key_reference:
      if len(self.dq_store  ) == LRUCache._MAX_CAPACITY:
        _UpperCamelCase       			=self.dq_store.pop()
        self.key_reference.remove(UpperCamelCase__  )
    else:
      self.dq_store.remove(UpperCamelCase__  )

    self.dq_store.appendleft(UpperCamelCase__  )
    self.key_reference.add(UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						None:
    for k in self.dq_store:
      print(UpperCamelCase__  )




  def __repr__(				self    :					str  )     ->						str:
    return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store  )}'''


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      __lowerCamelCase						: LRUCache[str | int]		=   LRUCache(4)
      lru_cache.refer('A')
      lru_cache.refer(2)
      lru_cache.refer(3)
      lru_cache.refer('A')
      lru_cache.refer(4)
      lru_cache.refer(5)
      lru_cache.display()

      print(lru_cache)
      assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"






