
"""simple docstring"""





import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__magic_name__       =			logging.get_logger(__name__)


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



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		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:
				__SCREAMING_SNAKE_CASE            = TOKENIZER_CLASSES
		else:
				__SCREAMING_SNAKE_CASE            = {tokenizer_name: getattr(UpperCamelCase_  ,				tokenizer_name + """Fast"""				)}

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

		for tokenizer_name in tokenizer_names:
				__SCREAMING_SNAKE_CASE            = TOKENIZER_CLASSES[tokenizer_name]

				__SCREAMING_SNAKE_CASE            = True
				if checkpoint_name is None:
						__SCREAMING_SNAKE_CASE            = list(tokenizer_class.max_model_input_sizes.keys()				)
				else:
						__SCREAMING_SNAKE_CASE            = [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
						__SCREAMING_SNAKE_CASE            = 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:
								__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = checkpoint.split("""/"""				)
								__SCREAMING_SNAKE_CASE            = os.path.join(UpperCamelCase_  ,				UpperCamelCase_				)
						elif add_prefix:
								__SCREAMING_SNAKE_CASE            = checkpoint
								__SCREAMING_SNAKE_CASE            = dump_path
						else:
								__SCREAMING_SNAKE_CASE            = None
								__SCREAMING_SNAKE_CASE            = 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]:
								__SCREAMING_SNAKE_CASE            = list(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
								__SCREAMING_SNAKE_CASE            = file_path.split(UpperCamelCase_				)[-1][0]
								if next_char == "/":
										__SCREAMING_SNAKE_CASE            = os.path.join(UpperCamelCase_  ,				UpperCamelCase_				)
										__SCREAMING_SNAKE_CASE            = None

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

						__SCREAMING_SNAKE_CASE            = 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__":
							__magic_name__       =			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.",
							)
							__magic_name__       =			parser.parse_args()

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






"""simple docstring"""





from __future__ import annotations

from fractions import Fraction



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		return (
		    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
		)



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = 11
		__SCREAMING_SNAKE_CASE            = int("""1""" + """0""" * digit_len				)
		for num in range(UpperCamelCase_  ,				UpperCamelCase_				):
				while den <= 99:
						if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
								if is_digit_cancelling(UpperCamelCase_  ,				UpperCamelCase_				):
										solutions.append(f"{num}/{den}"				)
						den += 1
				num += 1
				__SCREAMING_SNAKE_CASE            = 10
		return solutions



def 						_lowerCAmelCase				(				UpperCamelCase_ = 2				):
		__SCREAMING_SNAKE_CASE            = 1.0
		for fraction in fraction_list(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = Fraction(UpperCamelCase_				)
				result *= frac.denominator / frac.numerator
		return int(UpperCamelCase_				)


if __name__ == "__main__":
							print(solution())




