



'''simple docstring'''

import argparse
import collections
import importlib.util
import os
import re


# 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		    =     '''src/diffusers'''
__lowerCAmelCase		    =     '''docs/source/en'''
__lowerCAmelCase		    =     '''.'''



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)    ->   str:
  with open(lowerCAmelCase_		,    'r'		,    encoding='utf-8'		,    newline='\n'						) as f:
    _a  :   Any				 =							f.readlines()
  # Find the start prompt.
  _a  :   Any				 =							0
  while not lines[start_index].startswith(lowerCAmelCase_						):
    start_index += 1
  start_index += 1

  _a  :   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 |
__lowerCAmelCase		    =     '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase		    =     re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__lowerCAmelCase		    =     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		    =     re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the diffusers module imported is the one in the repo.
__lowerCAmelCase		    =     importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(TRANSFORMERS_PATH, '''__init__.py'''),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
__lowerCAmelCase		    =     spec.loader.load_module()



def   __lowerCamelCase  (   lowerCAmelCase_						)    ->   str:
  _a  :   List[Any]				 =							re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'		,    lowerCAmelCase_						)
  return [m.group(0						) for m in matches]



def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   str:
  _a  :   Optional[int]				 =							2 if text == '✅' or text == '❌' else len(lowerCAmelCase_						)
  _a  :   int				 =							(width - text_length) // 2
  _a  :   Optional[int]				 =							width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent



def   __lowerCamelCase  (   )    ->   Any:
  _a  :   Dict				 =							diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  _a  :   Dict				 =							{
      name: config_mapping_names[code]
      for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
      if code in config_mapping_names
  }
  _a  :   Tuple				 =							{name: config.replace('ConfigMixin'		,    ''						) 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(lowerCAmelCase_						)
  _a  :   int				 =							collections.defaultdict(lowerCAmelCase_						)
  _a  :   List[Any]				 =							collections.defaultdict(lowerCAmelCase_						)
  _a  :   Optional[int]				 =							collections.defaultdict(lowerCAmelCase_						)
  _a  :   Dict				 =							collections.defaultdict(lowerCAmelCase_						)

  # Let's lookup through all diffusers object (once).
  for attr_name in dir(lowerCAmelCase_						):
    _a  :   Dict				 =							None
    if attr_name.endswith('Tokenizer'						):
      _a  :   List[Any]				 =							slow_tokenizers
      _a  :   Tuple				 =							attr_name[:-9]
    elif attr_name.endswith('TokenizerFast'						):
      _a  :   Any				 =							fast_tokenizers
      _a  :   int				 =							attr_name[:-13]
    elif _re_tf_models.match(lowerCAmelCase_						) is not None:
      _a  :   int				 =							tf_models
      _a  :   List[Any]				 =							_re_tf_models.match(lowerCAmelCase_						).groups()[0]
    elif _re_flax_models.match(lowerCAmelCase_						) is not None:
      _a  :   int				 =							flax_models
      _a  :   Optional[Any]				 =							_re_flax_models.match(lowerCAmelCase_						).groups()[0]
    elif _re_pt_models.match(lowerCAmelCase_						) is not None:
      _a  :   Optional[int]				 =							pt_models
      _a  :   Tuple				 =							_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():
          _a  :   str				 =							True
          break
        # Try again after removing the last word in the name
        _a  :   Tuple				 =							''.join(camel_case_split(lowerCAmelCase_						)[:-1]						)

    # Let's build that table!
  _a  :   List[Any]				 =							list(model_name_to_config.keys()						)
  model_names.sort(key=str.lower						)
  _a  :   int				 =							['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  :   Union[str, Any]				 =							[len(lowerCAmelCase_						) + 2 for c in columns]
  _a  :   Optional[int]				 =							max([len(lowerCAmelCase_						) for name in model_names]						) + 2

  # Build the table per se
  _a  :   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"

  _a  :   int				 =							{True: '✅', False: '❌'}
  for name in model_names:
    _a  :   Optional[Any]				 =							model_name_to_prefix[name]
    _a  :   Tuple				 =							[
        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   __lowerCamelCase  (   lowerCAmelCase_=False						)    ->   Optional[int]:
  _a  :   List[str]				 =							_find_text_in_file(
      filename=os.path.join(lowerCAmelCase_		,    'index.mdx'						)		,    start_prompt='<!--This table is updated automatically from the auto modules'		,    end_prompt='<!-- End table-->'		,    )
  _a  :   Union[str, Any]				 =							get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(lowerCAmelCase_		,    'index.mdx'						)		,    '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.mdx` has not been updated. Run `make fix-copies` to fix this.'						)


if __name__ == "__main__":
       __lowerCAmelCase		    =     argparse.ArgumentParser()
       parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
       __lowerCAmelCase		    =     parser.parse_args()

       check_model_table(args.fix_and_overwrite)









'''simple docstring'''

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      SqueezeBertTokenizer
							lowerCAmelCase :					int            =      SqueezeBertTokenizerFast
							lowerCAmelCase :					List[Any]            =      True







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Tuple	):
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							@slow
							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[int]				 =							SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'	)

									_a  :   int				 =							tokenizer.encode('sequence builders'			,add_special_tokens=_UpperCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.encode('multi-sequence build'			,add_special_tokens=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
									_a  :   Dict				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,_UpperCAmelCase	)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]




