


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

				_A         =	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    a__       ( __lowercase      )		->		Optional[Any]:
				_A         =	re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"   ,	__lowercase      )
				return [m.group(0      ) for m in matches]






def    a__       ( __lowercase   ,	__lowercase      )		->		int:
				_A         =	2 if text == "✅" or text == "❌" else len(__lowercase      )
				_A         =	(width - text_length) // 2
				_A         =	width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent






def    a__       ( )		->		List[str]:
				_A         =	diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				_A         =	{
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				_A         =	{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         =	collections.defaultdict(__lowercase      )
				_A         =	collections.defaultdict(__lowercase      )
				_A         =	collections.defaultdict(__lowercase      )
				_A         =	collections.defaultdict(__lowercase      )
				_A         =	collections.defaultdict(__lowercase      )

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

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

				# Build the table per se
				_A         =	"|" + "|".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"

				_A         =	{True: "✅", False: "❌"}
				for name in model_names:
								_A         =	model_name_to_prefix[name]
								_A         =	[
								    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    a__       ( __lowercase=False      )		->		Tuple:
				_A     ,						_A     ,						_A     ,						_A         =	_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-->"   ,	)
				_A         =	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    a__       ( __lowercase = 20      )		->		int:

				try:
								_A         =	int(__lowercase      )
				except (TypeError, ValueError):
								raise TypeError("Parameter n must be int or castable to int."      )
				if n <= 0:
								raise ValueError("Parameter n must be greater than or equal to one."      )
				_A         =	0
				while 1:
								i += n * (n - 1)
								_A         =	0
								for j in range(2   ,	__lowercase      ):
												if i % j != 0:
																_A         =	1
																break
								if nfound == 0:
												if i == 0:
																_A         =	1
												return i
				return None


if __name__ == "__main__":
		print(f'''{solution() = }''')