



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

snake_case    :						str  =						logging.get_logger(__name__)


snake_case    :						Union[str, Any]  =						{name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		List[Any]:







						"""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__	:							Dict						    = TOKENIZER_CLASSES
						else:
												A__	:							Tuple						    = {tokenizer_name: getattr(__lowercase							,     tokenizer_name + "Fast"       )}

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

						for tokenizer_name in tokenizer_names:
												A__	:							Optional[int]						    = TOKENIZER_CLASSES[tokenizer_name]

												A__	:							str						    = True
												if checkpoint_name is None:
																		A__	:							str						    = list(tokenizer_class.max_model_input_sizes.keys()       )
												else:
																		A__	:							Union[str, Any]						    = [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__	:							Optional[int]						    = tokenizer_class.from_pretrained(__lowercase							,     force_download=__lowercase       )

																		# 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__	:							Tuple						    = checkpoint.split("/"       )
																								A__	:							int						    = os.path.join(__lowercase							,     __lowercase       )
																		elif add_prefix:
																								A__	:							List[Any]						    = checkpoint
																								A__	:							Optional[Any]						    = dump_path
																		else:
																								A__	:							str						    = None
																								A__	:							int						    = 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__	:							Optional[int]						    = list(tokenizer.pretrained_vocab_files_map.values()       )[0][checkpoint]
																								A__	:							Optional[int]						    = file_path.split(__lowercase       )[-1][0]
																								if next_char == "/":
																														A__	:							Any						    = os.path.join(__lowercase							,     __lowercase       )
																														A__	:							Dict						    = None

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

																		A__	:							List[str]						    = tokenizer.save_pretrained(
																		    __lowercase							,     legacy_format=__lowercase							,     filename_prefix=__lowercase       )
																		logger.info(F'=> File names {file_names}'       )

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


if __name__ == "__main__":
		snake_case    :						Dict  =						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.',
		)
		snake_case    :						Union[str, Any]  =						parser.parse_args()

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




def 	snake_case__       (					)		->		Union[str, Any]:







						"""simple docstring"""

						A__	:							Tuple						    = []
						A__	:							Optional[int]						    = 1

						while len(__lowercase       ) < 1E6:
												constant.append(str(__lowercase       )       )
												i += 1

						A__	:							Any						    = "".join(__lowercase       )

						return (
						    int(constant[0]       )
						    * int(constant[9]       )
						    * int(constant[9_9]       )
						    * int(constant[9_9_9]       )
						    * int(constant[9_9_9_9]       )
						    * int(constant[9_9_9_9_9]       )
						    * int(constant[9_9_9_9_9_9]       )
						)


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