



'''simple docstring'''






from __future__ import annotations

lowerCAmelCase:      Any		 =		[]
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  for i in range(len(_A       )       ):
    if board[row][i] == 1:
      return False
  for i in range(len(_A       )       ):
    if board[i][column] == 1:
      return False
  for i, j in zip(range(_A				,    -1				,    -1       )				,    range(_A				,    -1				,    -1       )       ):
    if board[i][j] == 1:
      return False
  for i, j in zip(range(_A				,    -1				,    -1       )				,    range(_A				,    len(_A       )       )       ):
    if board[i][j] == 1:
      return False
  return True
def    lowerCamelCase__ (		_A				,    _A       ):
  if row >= len(_A       ):
    solution.append(_A       )
    printboard(_A       )
    print()
    return True
  for i in range(len(_A       )       ):
    if is_safe(_A				,    _A				,    _A       ):
      a		:      Optional[Any]       =  1
      solve(_A				,    row + 1       )
      a		:      str       =  0
  return False
def    lowerCamelCase__ (		_A       ):
  for i in range(len(_A       )       ):
    for j in range(len(_A       )       ):
      if board[i][j] == 1:
        print('Q'				,    end=' '       )
      else:
        print('.'				,    end=' '       )
    print()


# n=int(input("The no. of queens"))
lowerCAmelCase:      int		 =		8
lowerCAmelCase:      Optional[int]		 =		[[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print('The total no. of solutions are :', len(solution))




'''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:      Tuple		 =		'src/transformers'
lowerCAmelCase:      Union[str, Any]		 =		'docs/source/en'
lowerCAmelCase:      Dict		 =		'.'
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  with open(_A				,    'r'				,    encoding='utf-8'				,    newline='\n'       ) as f:
    a		:      Optional[Any]       =  f.readlines()
  # Find the start prompt.
  a		:      Dict       =  0
  while not lines[start_index].startswith(_A       ):
    start_index += 1
  start_index += 1

  a		:      Optional[Any]       =  start_index
  while not lines[end_index].startswith(_A       ):
    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:      List[str]		 =		'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase:      Dict		 =		re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase:      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    lowerCamelCase__ (		_A       ):
  a		:      Tuple       =  re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'				,    _A       )
  return [m.group(0       ) for m in matches]
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      List[Any]       =  2 if text == '✅' or text == '❌' else len(_A       )
  a		:      Optional[int]       =  (width - text_length) // 2
  a		:      List[Any]       =  width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent
def    lowerCamelCase__ (		):
  a		:      Union[str, Any]       =  transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  a		:      Union[str, Any]       =  {
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  a		:      Tuple       =  {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		:      int       =  collections.defaultdict(_A       )
  a		:      List[Any]       =  collections.defaultdict(_A       )
  a		:      List[Any]       =  collections.defaultdict(_A       )
  a		:      Union[str, Any]       =  collections.defaultdict(_A       )
  a		:      int       =  collections.defaultdict(_A       )

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

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

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

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

  a		:      str       =  {True: '✅', False: '❌'}
  for name in model_names:
    a		:      Any       =  model_name_to_prefix[name]
    a		:      Optional[Any]       =  [
        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(_A				,    _A       ) for l, w in zip(_A				,    _A       )]       ) + "|\n"
  return table
def    lowerCamelCase__ (		_A=False       ):
  a     ,			a     ,			a     ,			a		:      Tuple       =  _find_text_in_file(
      filename=os.path.join(_A				,    'index.md'       )				,    start_prompt='<!--This table is updated automatically from the auto modules'				,    end_prompt='<!-- End table-->'				,    )
  a		:      Optional[int]       =  get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(_A				,    '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:      Dict		 =		argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     lowerCAmelCase:      Any		 =		parser.parse_args()

     check_model_table(args.fix_and_overwrite)