




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



def 					UpperCamelCase__       (						lowercase__					:       Optional[int]    ,       lowercase__					:       Union[str, Any]    ,       lowercase__					:       Optional[int]				):
		with open(lowercase__    ,       "r"    ,       encoding="utf-8"    ,       newline="\n"				) as f:
				snake_case						:  Tuple        =			f.readlines()
		# Find the start prompt.
		snake_case						:  Any        =			0
		while not lines[start_index].startswith(lowercase__				):
				start_index += 1
		start_index += 1

		snake_case						:  List[str]        =			start_index
		while not lines[end_index].startswith(lowercase__				):
				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 					UpperCamelCase__       (						lowercase__					:       str				):
		snake_case						:  Optional[Any]        =			re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,       lowercase__				)
		return [m.group(0				) for m in matches]



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



def 					UpperCamelCase__       (						):
		snake_case						:  int        =			diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		snake_case						:  Dict        =			{
		    name: config_mapping_names[code]
		    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
		    if code in config_mapping_names
		}
		snake_case						:  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.
		snake_case						:  Dict        =			collections.defaultdict(lowercase__				)
		snake_case						:  List[Any]        =			collections.defaultdict(lowercase__				)
		snake_case						:  List[str]        =			collections.defaultdict(lowercase__				)
		snake_case						:  int        =			collections.defaultdict(lowercase__				)
		snake_case						:  Optional[Any]        =			collections.defaultdict(lowercase__				)

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

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

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

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

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



def 					UpperCamelCase__       (						lowercase__					:       Union[str, Any]=False				):
		snake_case     ,			snake_case     ,			snake_case     ,			snake_case						:  Optional[int]        =			_find_text_in_file(
		    filename=os.path.join(lowercase__    ,       "index.mdx"				)    ,       start_prompt="<!--This table is updated automatically from the auto modules"    ,       end_prompt="<!-- End table-->"    ,       )
		snake_case						:  Union[str, Any]        =			get_model_table_from_auto_modules()

		if current_table != new_table:
				if overwrite:
						with open(os.path.join(lowercase__    ,       "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)







"""simple docstring"""

def 					UpperCamelCase__       (						lowercase__					:       int				):
		if not isinstance(lowercase__    ,       lowercase__				):
				raise ValueError("double_factorial() only accepts integral values"				)
		if n < 0:
				raise ValueError("double_factorial() not defined for negative values"				)
		return 1 if n <= 1 else n * double_factorial(n - 2				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

