
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				:Optional[int]									=							logging.get_logger(__name__)


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


def 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_		)						->      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       =					TOKENIZER_CLASSES
				else:
								__A       =					{tokenizer_name: getattr(a_						,  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(a_						,  force_download=a_		)

												# 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(a_						,  a_		)
												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(a_		)[-1][0]
																if next_char == "/":
																				__A       =					os.path.join(a_						,  a_		)
																				__A       =					None

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

												__A       =					tokenizer.save_pretrained(
												    a_						,  legacy_format=a_						,  filename_prefix=a_		)
												logger.info(F'''=> File names {file_names}'''		)

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE				:List[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.',
					)
					SCREAMING_SNAKE_CASE				:Dict									=							parser.parse_args()

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


import os

# Precomputes a list of the 100 first triangular numbers
__a         =   [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]




def 							a		(				):


				'''simple docstring'''
				lowercase_	    =   os.path.dirname(os.path.realpath(snake_case__   )   )
				lowercase_	    =   os.path.join(snake_case__		,  '''words.txt'''   )

				lowercase_	    =   ''''''
				with open(snake_case__   ) as f:
								lowercase_	    =   f.readline()

				lowercase_	    =   [word.strip('''"'''   ) for word in words.strip('''\r\n'''   ).split(''','''   )]
				lowercase_	    =   [
				    word
				    for word in [sum(ord(snake_case__   ) - 64 for x in word   ) for word in words]
				    if word in TRIANGULAR_NUMBERS
				]
				return len(snake_case__   )


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




