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

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





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Union[str, Any]           =   2 if text == """✅""" or text == """❌""" else len(lowerCamelCase							)
 lowerCamelCase	:			str           =   (width - text_length) // 2
 lowerCamelCase	:			List[str]           =   width - text_length - left_indent
 return " " * left_indent + text + " " * right_indent





def 					_a      (		):
 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	:			List[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.
 lowerCamelCase	:			Any           =   collections.defaultdict(lowerCamelCase							)
 lowerCamelCase	:			Optional[Any]           =   collections.defaultdict(lowerCamelCase							)
 lowerCamelCase	:			str           =   collections.defaultdict(lowerCamelCase							)
 lowerCamelCase	:			str           =   collections.defaultdict(lowerCamelCase							)
 lowerCamelCase	:			str           =   collections.defaultdict(lowerCamelCase							)

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

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

 # Build the table per se
 lowerCamelCase	:			Any           =   """|""" + """|""".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	:			Union[str, Any]           =   {True: """✅""", False: """❌"""}
 for name in model_names:
  lowerCamelCase	:			str           =   model_name_to_prefix[name]
  lowerCamelCase	:			Union[str, 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(lowerCamelCase,			lowerCamelCase							) for l, w in zip(lowerCamelCase,			lowerCamelCase							)]							) + "|\n"
 return table





def 					_a      (		lowerCamelCase=False							):
 lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase	:			int           =   _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	:			int           =   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)






def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 lowerCamelCase	:			Optional[Any]           =   float()
 try:
  lowerCamelCase	:			Tuple           =   mass * acceleration
 except Exception:
  return -0.0
 return force


if __name__ == "__main__":
      import doctest

      # run doctest
      doctest.testmod()

      # demo
      _lowerCamelCase           =12.5
      _lowerCamelCase           =1_0
      _lowerCamelCase           =newtons_second_law_of_motion(mass, acceleration)
      print("""The force is """, force, """N""")




