




'''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
__SCREAMING_SNAKE_CASE    :   Tuple       ="""src/diffusers"""
__SCREAMING_SNAKE_CASE    :   Any       ="""docs/source/en"""
__SCREAMING_SNAKE_CASE    :   Dict       ="""."""

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[Any]     ,				lowerCamelCase:       Tuple     ,				lowerCamelCase:       Any						)					->			Dict:


      '''simple docstring'''




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

      lowerCamelCase__        =				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 |
__SCREAMING_SNAKE_CASE    :   Dict       ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
__SCREAMING_SNAKE_CASE    :   List[str]       =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__SCREAMING_SNAKE_CASE    :   Tuple       =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.
__SCREAMING_SNAKE_CASE    :   List[str]       =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[Any]						)					->			Optional[Any]:


      '''simple docstring'''




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

def 			lowerCAmelCase_			(  lowerCamelCase:       List[Any]     ,				lowerCamelCase:       int						)					->			Dict:


      '''simple docstring'''




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

def 			lowerCAmelCase_			(  )					->			Tuple:


      '''simple docstring'''




      lowerCamelCase__        =				diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      lowerCamelCase__        =				{
          name: config_mapping_names[code]
          for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
          if code in config_mapping_names
      }
      lowerCamelCase__        =				{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__        =				collections.defaultdict(lowerCamelCase						)
      lowerCamelCase__        =				collections.defaultdict(lowerCamelCase						)
      lowerCamelCase__        =				collections.defaultdict(lowerCamelCase						)
      lowerCamelCase__        =				collections.defaultdict(lowerCamelCase						)
      lowerCamelCase__        =				collections.defaultdict(lowerCamelCase						)

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

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

      # Build the table per se
      lowerCamelCase__        =				'|' + '|'.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"

      lowerCamelCase__        =				{True: '✅', False: '❌'}
      for name in model_names:
            lowerCamelCase__        =				model_name_to_prefix[name]
            lowerCamelCase__        =				[
                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:       Any=False						)					->			List[str]:


      '''simple docstring'''




      lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				_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-->'     ,				)
      lowerCamelCase__        =				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__":
      __SCREAMING_SNAKE_CASE    :   int       =argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      __SCREAMING_SNAKE_CASE    :   Tuple       =parser.parse_args()

      check_model_table(args.fix_and_overwrite)





from ..utils import DummyObject, requires_backends






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   Dict	,  *A__  :   Optional[int]	,  **A__  :   Dict			)				->       Union[str, Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[int]	,  *A__  :   Dict	,  **A__  :   List[str]			)				->       Union[str, Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[Any]	,  *A__  :   Dict	,  **A__  :   List[Any]			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   List[str]	,  *A__  :   str	,  **A__  :   int			)				->       List[Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   str	,  *A__  :   Any	,  **A__  :   Dict			)				->       Optional[Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   List[str]	,  *A__  :   Optional[Any]	,  **A__  :   int			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)