






import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# 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/transformers"
_lowerCAmelCase         =							"docs/source/en"
_lowerCAmelCase         =							"."






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

	A_		:						Union[str, 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 |
_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 transformers module imported is the one in the repo.
_lowerCAmelCase         =							direct_transformers_import(TRANSFORMERS_PATH)






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						int	    =   re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""   ,   snake_case__					)
	return [m.group(0					) for m in matches]






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						Dict	    =   2 if text == """✅""" or text == """❌""" else len(snake_case__					)
	A_		:						int	    =   (width - text_length) // 2
	A_		:						Dict	    =   width - text_length - left_indent
	return " " * left_indent + text + " " * right_indent






def 			__UpperCamelCase	(		):
	A_		:						Any	    =   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	A_		:						List[Any]	    =   {
	    name: config_maping_names[code]
	    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
	    if code in config_maping_names
	}
	A_		:						Dict	    =   {name: config.replace("""Config"""   ,   """"""					) 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_		:						Optional[int]	    =   collections.defaultdict(snake_case__					)
	A_		:						int	    =   collections.defaultdict(snake_case__					)
	A_		:						int	    =   collections.defaultdict(snake_case__					)
	A_		:						List[Any]	    =   collections.defaultdict(snake_case__					)
	A_		:						Tuple	    =   collections.defaultdict(snake_case__					)

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

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

	# Build the table per se
	A_		:						Optional[int]	    =   """|""" + """|""".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"

	A_		:						str	    =   {True: """✅""", False: """❌"""}
	for name in model_names:
		A_		:						Optional[int]	    =   model_name_to_prefix[name]
		A_		:						Tuple	    =   [
		    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 			__UpperCamelCase	(		snake_case__=False					):
	A_		:						List[str]	    =   _find_text_in_file(
	    filename=os.path.join(snake_case__   ,   """index.md"""					)   ,   start_prompt="""<!--This table is updated automatically from the auto modules"""   ,   end_prompt="""<!-- End table-->"""   ,   )
	A_		:						List[Any]	    =   get_model_table_from_auto_modules()

	if current_table != new_table:
		if overwrite:
			with open(os.path.join(snake_case__   ,   """index.md"""					)   ,   """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.md` 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)









"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   prime_factors(snake_case__					)
	if is_square_free(snake_case__					):
		return -1 if len(snake_case__					) % 2 else 1
	return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()

