

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
_a			=       '''src/diffusers'''
_a			=       '''docs/source/en'''
_a			=       '''.'''







def _a			(	SCREAMING_SNAKE_CASE		:      Tuple   , SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      Optional[int]		)   ->   Optional[Any]:

 """simple docstring"""






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

 __lowerCAmelCase:       List[str]            =				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 |
_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 diffusers module imported is the one in the repo.
_a			=       importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(TRANSFORMERS_PATH, '''__init__.py'''),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
_a			=       spec.loader.load_module()







def _a			(	SCREAMING_SNAKE_CASE		:      Dict		)   ->   int:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				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		:      Optional[Any]   , SCREAMING_SNAKE_CASE		:      str		)   ->   str:

 """simple docstring"""






 __lowerCAmelCase:       Any            =				2 if text == '✅' or text == '❌' else len(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Dict            =				(width - text_length) // 2
 __lowerCAmelCase:       Optional[int]            =				width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent







def _a			(	)   ->   int:

 """simple docstring"""






 __lowerCAmelCase:       Tuple            =				diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 __lowerCAmelCase:       Union[str, Any]            =				{
     name: config_mapping_names[code]
     for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
     if code in config_mapping_names
 }
 __lowerCAmelCase:       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.
 __lowerCAmelCase:       List[str]            =				collections.defaultdict(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Dict            =				collections.defaultdict(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Union[str, Any]            =				collections.defaultdict(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				collections.defaultdict(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       List[str]            =				collections.defaultdict(SCREAMING_SNAKE_CASE		)

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

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

 # Build the table per se
 __lowerCAmelCase:       Optional[Any]            =				'|' + '|'.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"

 __lowerCAmelCase:       Dict            =				{True: '✅', False: '❌'}
 for name in model_names:
  __lowerCAmelCase:       Optional[Any]            =				model_name_to_prefix[name]
  __lowerCAmelCase:       List[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(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		:      List[Any]=False		)   ->   Tuple:

 """simple docstring"""






 __lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase:       List[str]            =				_find_text_in_file(
     filename=os.path.join(SCREAMING_SNAKE_CASE   , 'index.mdx'		)   , start_prompt='<!--This table is updated automatically from the auto modules'   , end_prompt='<!-- End table-->'   , )
 __lowerCAmelCase:       List[str]            =				get_model_table_from_auto_modules()

 if current_table != new_table:
  if overwrite:
   with open(os.path.join(SCREAMING_SNAKE_CASE   , '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__":
  _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)








import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig






class 							A_			(		snake_case__ ):
     _lowercase      :					Any       =     CustomConfig


     def __init__(							self      :		List[Any]      ,  UpperCAmelCase      :		Optional[Any] )							->     int:
      super().__init__(UpperCAmelCase )
      __lowerCAmelCase:       Tuple            =				torch.nn.Linear(config.hidden_size      ,  config.hidden_size )


     def   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Tuple )							->     Union[str, Any]:
      return self.linear(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		str      ,  UpperCAmelCase      :		str )							->     Optional[int]:
      pass







class 							A_			(		snake_case__ ):
     _lowercase      :					str       =     NoSuperInitConfig


     def __init__(							self      :		List[Any]      ,  UpperCAmelCase      :		Union[str, Any] )							->     int:
      super().__init__(UpperCAmelCase )
      __lowerCAmelCase:       int            =				torch.nn.Linear(config.attribute      ,  config.attribute )


     def   UpperCAmelCase  (							self      :		Tuple      ,  UpperCAmelCase      :		Tuple )							->     Tuple:
      return self.linear(UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Optional[Any]      ,  UpperCAmelCase      :		Any )							->     str:
      pass





