
import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_SCREAMING_SNAKE_CASE    :      int          =			logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE    :      Any          =			{name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]							,						__UpperCamelCase :						List[Any]							,						__UpperCamelCase :						Optional[int]							,						__UpperCamelCase :						Union[str, Any]       ) ->       List[str]:

				"""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__  :	Union[str, Any]         =							TOKENIZER_CLASSES
				else:
								A__  :	Dict         =							{tokenizer_name: getattr(__UpperCamelCase							,						tokenizer_name + '''Fast'''       )}

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

				for tokenizer_name in tokenizer_names:
								A__  :	Union[str, Any]         =							TOKENIZER_CLASSES[tokenizer_name]

								A__  :	int         =							True
								if checkpoint_name is None:
												A__  :	Tuple         =							list(tokenizer_class.max_model_input_sizes.keys()       )
								else:
												A__  :	List[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(__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__  :	Union[str, Any]         =							checkpoint.split('''/'''       )
																A__  :	Any         =							os.path.join(__UpperCamelCase							,						__UpperCamelCase       )
												elif add_prefix:
																A__  :	Tuple         =							checkpoint
																A__  :	Union[str, Any]         =							dump_path
												else:
																A__  :	str         =							None
																A__  :	Any         =							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__  :	int         =							list(tokenizer.pretrained_vocab_files_map.values()       )[0][checkpoint]
																A__  :	Union[str, Any]         =							file_path.split(__UpperCamelCase       )[-1][0]
																if next_char == "/":
																				A__  :	Union[str, Any]         =							os.path.join(__UpperCamelCase							,						__UpperCamelCase       )
																				A__  :	str         =							None

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

												A__  :	Optional[Any]         =							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__":
					_SCREAMING_SNAKE_CASE    :      str          =			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.',
					)
					_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			parser.parse_args()

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

import numpy as np

_SCREAMING_SNAKE_CASE    :      Any          =			[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]







class  UpperCamelCase__		:







	'''simple docstring'''





	def __init__(			self		):
					A__  :	List[Any]         =							np.array(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__       ,							A__  :	Any         =							np.where(letter == self.SQUARE		)
					A__  :	int         =							np.concatenate([indexa + 1, indexa + 1]		)
					return indexes





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Union[str, Any]         =							self.SQUARE[indexa - 1, indexa - 1]
					return letter





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	List[str]         =							message.lower()
					A__  :	str         =							message.replace(''' '''				,       ''''''		)
					A__  :	Union[str, Any]         =							message.replace('''j'''				,       '''i'''		)

					A__  :	List[Any]         =							np.empty((2, len(UpperCamelCase__		))		)
					for letter_index in range(len(UpperCamelCase__		)		):
									A__  :	Any         =							self.letter_to_numbers(message[letter_index]		)

									A__  :	Optional[Any]         =							numbers[0]
									A__  :	List[str]         =							numbers[1]

					A__  :	List[str]         =							first_step.reshape(2 * len(UpperCamelCase__		)		)
					A__  :	List[Any]         =							''''''
					for numbers_index in range(len(UpperCamelCase__		)		):
									A__  :	Dict         =							int(second_step[numbers_index * 2]		)
									A__  :	List[str]         =							int(second_step[(numbers_index * 2) + 1]		)
									A__  :	Dict         =							self.numbers_to_letter(UpperCamelCase__				,       UpperCamelCase__		)
									A__  :	Tuple         =							encoded_message + letter

					return encoded_message





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	str         =							message.lower()
					message.replace(''' '''				,       ''''''		)
					A__  :	List[Any]         =							np.empty(2 * len(UpperCamelCase__		)		)
					for letter_index in range(len(UpperCamelCase__		)		):
									A__  :	List[str]         =							self.letter_to_numbers(message[letter_index]		)
									A__  :	Dict         =							numbers[0]
									A__  :	int         =							numbers[1]

					A__  :	Optional[Any]         =							first_step.reshape((2, len(UpperCamelCase__		))		)
					A__  :	int         =							''''''
					for numbers_index in range(len(UpperCamelCase__		)		):
									A__  :	Tuple         =							int(second_step[0, numbers_index]		)
									A__  :	Dict         =							int(second_step[1, numbers_index]		)
									A__  :	List[str]         =							self.numbers_to_letter(UpperCamelCase__				,       UpperCamelCase__		)
									A__  :	Tuple         =							decoded_message + letter

					return decoded_message