


import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_lowercase       :      List[Any]	=	logging.get_logger(__name__)


_lowercase       :      Tuple	=	{name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     int							,					UpperCamelCase__:     Tuple						) ->   int:





	"""simple docstring"""







	if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
		raise ValueError(f'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()						)}.'						)

	if tokenizer_name is None:
		A								=				TOKENIZER_CLASSES
	else:
		A								=				{tokenizer_name: getattr(UpperCamelCase__							,					tokenizer_name + """Fast"""						)}

	logger.info(f'Loading tokenizer classes: {tokenizer_names}'						)

	for tokenizer_name in tokenizer_names:
		A								=				TOKENIZER_CLASSES[tokenizer_name]

		A								=				True
		if checkpoint_name is None:
			A								=				list(tokenizer_class.max_model_input_sizes.keys()						)
		else:
			A								=				[checkpoint_name]

		logger.info(f'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'						)

		for checkpoint in checkpoint_names:
			logger.info(f'Loading {tokenizer_class.__class__.__name__} {checkpoint}'						)

			# Load tokenizer
			A								=				tokenizer_class.from_pretrained(UpperCamelCase__							,					force_download=UpperCamelCase__						)

			# Save fast tokenizer
			logger.info(f'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'						)

			# For organization names we create sub-directories
			if "/" in checkpoint:
				A					,			A								=				checkpoint.split("""/"""						)
				A								=				os.path.join(UpperCamelCase__							,					UpperCamelCase__						)
			elif add_prefix:
				A								=				checkpoint
				A								=				dump_path
			else:
				A								=				None
				A								=				dump_path

			logger.info(f'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'						)

			if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()						)[0]:
				A								=				list(tokenizer.pretrained_vocab_files_map.values()						)[0][checkpoint]
				A								=				file_path.split(UpperCamelCase__						)[-1][0]
				if next_char == "/":
					A								=				os.path.join(UpperCamelCase__							,					UpperCamelCase__						)
					A								=				None

				logger.info(f'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'						)

			A								=				tokenizer.save_pretrained(
			    UpperCamelCase__							,					legacy_format=UpperCamelCase__							,					filename_prefix=UpperCamelCase__						)
			logger.info(f'=> File names {file_names}'						)

			for file_name in file_names:
				if not file_name.endswith("""tokenizer.json"""						):
					os.remove(UpperCamelCase__						)
					logger.info(f'=> removing {file_name}'						)


if __name__ == "__main__":
				_lowercase       :      Optional[Any]	=	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
				)
				parser.add_argument(
				    "--tokenizer_name",
				    default=None,
				    type=str,
				    help=(
				        f'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
				        "download and convert all the checkpoints from AWS."
				    ),
				)
				parser.add_argument(
				    "--checkpoint_name",
				    default=None,
				    type=str,
				    help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
				)
				parser.add_argument(
				    "--force_download",
				    action="store_true",
				    help="Re-download checkpoints.",
				)
				_lowercase       :      Dict	=	parser.parse_args()

				convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)







def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   list[int]:





	"""simple docstring"""







	if num <= 0:
		raise ValueError("""Input must be a positive integer"""						)

	A								=				[True] * (num + 1)

	A								=				2
	while p * p <= num:
		if primes[p]:
			for i in range(p * p							,					num + 1							,					UpperCamelCase__						):
				A								=				False
		p += 1

	return [prime for prime in range(2							,					num + 1						) if primes[prime]]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowercase       :      Tuple	=	int(input("Enter a positive integer: ").strip())
				print(prime_sieve_eratosthenes(user_num))



