
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
lowercase_											=	"""src/diffusers"""
lowercase_											=	"""docs/source/en"""
lowercase_											=	"""."""


def     a__			(    snake_case   ,	snake_case   ,	snake_case    ):





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

  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						start_index
  while not lines[end_index].startswith(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 |
lowercase_											=	"""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
lowercase_											=	re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowercase_											=	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.
lowercase_											=	re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


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


def     a__			(    snake_case    ):





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


def     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						2 if text == '''✅''' or text == '''❌''' else len(snake_case    )
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						(width - text_length) // 2
  __SCREAMING_SNAKE_CASE     :					Tuple			=						width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent


def     a__			(    ):





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

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

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

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

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

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


def     a__			(    snake_case=False    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[str]			=						_find_text_in_file(
      filename=os.path.join(snake_case   ,	'''index.mdx'''    )   ,	start_prompt='''<!--This table is updated automatically from the auto modules'''   ,	end_prompt='''<!-- End table-->'''   ,	)
  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(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__":
   lowercase_											=	argparse.ArgumentParser()
   parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
   lowercase_											=	parser.parse_args()

   check_model_table(args.fix_and_overwrite)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_											=	{
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""LEDTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	[
       """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LEDForConditionalGeneration""",
       """LEDForQuestionAnswering""",
       """LEDForSequenceClassification""",
       """LEDModel""",
       """LEDPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase_											=	["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
   from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
   from .tokenization_led import LEDTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_led_fast import LEDTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_led import (
          LED_PRETRAINED_MODEL_ARCHIVE_LIST,
          LEDForConditionalGeneration,
          LEDForQuestionAnswering,
          LEDForSequenceClassification,
          LEDModel,
          LEDPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
   import sys

   lowercase_											=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
