





'''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()

__lowerCAmelCase :str     =    logging.get_logger(__name__)


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







def    A				(							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:
										_snake_case       :					str				       =   TOKENIZER_CLASSES
					else:
										_snake_case       :					Dict				       =   {tokenizer_name: getattr(UpperCAmelCase  ,  tokenizer_name + "Fast"				)}

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

					for tokenizer_name in tokenizer_names:
										_snake_case       :					List[Any]				       =   TOKENIZER_CLASSES[tokenizer_name]

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

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

															_snake_case       :					Dict				       =   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__":
							__lowerCAmelCase :Union[str, 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.',
							)
							__lowerCAmelCase :Tuple     =    parser.parse_args()

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






from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__lowerCAmelCase :Union[str, Any]     =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__lowerCAmelCase :Optional[Any]     =    typing.Union[np.floataa, int, float]  # noqa: UP007







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return np.sqrt(np.sum((np.asarray(UpperCAmelCase				) - np.asarray(UpperCAmelCase				)) ** 2				)				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return sum((va - va) ** 2 for va, va in zip(UpperCAmelCase  ,  UpperCAmelCase				)				) ** (1 / 2)


if __name__ == "__main__":







							def    A				(							):
												from timeit import timeit

												print("Without Numpy"				)
												print(
												    timeit(
												        "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)
												print("With Numpy"				)
												print(
												    timeit(
												        "euclidean_distance([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)

							benchmark()