


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 : str          =    "src/diffusers"
SCREAMING_SNAKE_CASE : Dict          =    "docs/source/en"
SCREAMING_SNAKE_CASE : Optional[Any]          =    "."





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[int]	,	SCREAMING_SNAKE_CASE_				: Any	,	SCREAMING_SNAKE_CASE_				: List[Any]      ):






							"""simple docstring"""



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

							a_       :   Any			=	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 |
SCREAMING_SNAKE_CASE : Optional[int]          =    "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE : Any          =    re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
SCREAMING_SNAKE_CASE : Dict          =    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 : Dict          =    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 : List[str]          =    importlib.util.spec_from_file_location(
    "diffusers",
    os.path.join(TRANSFORMERS_PATH, "__init__.py"),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
SCREAMING_SNAKE_CASE : Tuple          =    spec.loader.load_module()





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: str      ):






							"""simple docstring"""



							a_       :   Optional[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 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: int	,	SCREAMING_SNAKE_CASE_				: Optional[Any]      ):






							"""simple docstring"""



							a_       :   Dict			=	2 if text == """✅""" or text == """❌""" else len(SCREAMING_SNAKE_CASE_      )
							a_       :   Any			=	(width - text_length) // 2
							a_       :   Any			=	width - text_length - left_indent
							return " " * left_indent + text + " " * right_indent





def 						_lowerCamelCase		(							):






							"""simple docstring"""



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

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

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

							# Build the table per se
							a_       :   Dict			=	"""|""" + """|""".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"

							a_       :   Union[str, Any]			=	{True: """✅""", False: """❌"""}
							for name in model_names:
														a_       :   int			=	model_name_to_prefix[name]
														a_       :   str			=	[
														    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 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Tuple=False      ):






							"""simple docstring"""



							a_		,				a_		,				a_		,				a_       :   Dict			=	_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-->"""	,	)
							a_       :   Optional[Any]			=	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__":
	SCREAMING_SNAKE_CASE : Optional[int]          =    argparse.ArgumentParser()
	parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
	SCREAMING_SNAKE_CASE : Optional[Any]          =    parser.parse_args()

	check_model_table(args.fix_and_overwrite)






from __future__ import annotations





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list      ):






							"""simple docstring"""



							a_       :   Tuple			=	sorted(SCREAMING_SNAKE_CASE_      )
							a_       :   Tuple			=	len(SCREAMING_SNAKE_CASE_      )
							a_       :   Tuple			=	length >> 1
							return (
							    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
							    if length % 2 == 0
							    else sorted_list[mid_index]
							)





def 						_lowerCamelCase		(							):






							"""simple docstring"""



							import doctest

							doctest.testmod()


if __name__ == "__main__":
	main()


