




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_fnet import FNetTokenizer
else:
		__A						:							Optional[Any]							=  None

__A						:							Tuple							=  logging.get_logger(__name__)
__A						:							Any							=  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__A						:							Union[str, Any]							=  {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

__A						:							Optional[Any]							=  {
    """google/fnet-base""": 5_1_2,
    """google/fnet-large""": 5_1_2,
}

__A						:							Optional[Any]							=  """▁"""




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     ['''input_ids''', '''token_type_ids''']
							a__						  =     FNetTokenizer





							def __init__(					self       :	Optional[int]       ,     a       :	str=None       ,     a       :	Tuple=None       ,     a       :	Tuple=False       ,     a       :	Tuple=True       ,     a       :	Optional[Any]=True       ,     a       :	Optional[int]="<unk>"       ,     a       :	Dict="[SEP]"       ,     a       :	Optional[Any]="<pad>"       ,     a       :	Union[str, Any]="[CLS]"       ,     a       :	Optional[int]="[MASK]"       ,     **a       :	Optional[Any]       ,     )			->					Optional[Any]:
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										SCREAMING_SNAKE_CASE           =					(
										    AddedToken(a       ,     lstrip=a       ,     rstrip=a       ,     normalized=a			)
										    if isinstance(a       ,     a			)
										    else mask_token
										)

										super().__init__(
										    a       ,     tokenizer_file=a       ,     do_lower_case=a       ,     remove_space=a       ,     keep_accents=a       ,     unk_token=a       ,     sep_token=a       ,     pad_token=a       ,     cls_token=a       ,     mask_token=a       ,     **a       ,     )

										SCREAMING_SNAKE_CASE           =					do_lower_case
										SCREAMING_SNAKE_CASE           =					remove_space
										SCREAMING_SNAKE_CASE           =					keep_accents
										SCREAMING_SNAKE_CASE           =					vocab_file
										SCREAMING_SNAKE_CASE           =					False if not self.vocab_file else True





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[self.cls_token_id]
										if token_ids_a is None:
													return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[self.cls_token_id]

										if token_ids_a is None:
													return len(cls + token_ids_a + sep			) * [0]
										return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]







							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					Tuple[str]:
										if not os.path.isdir(a			):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
													return
										SCREAMING_SNAKE_CASE           =					os.path.join(
										    a       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

										if os.path.abspath(self.vocab_file			) != os.path.abspath(a			):
													copyfile(self.vocab_file       ,     a			)

										return (out_vocab_file,)












from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__A						:							Dict							=  typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__A						:							Tuple							=  typing.Union[np.floataa, int, float]  # noqa: UP007
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return np.sqrt(np.sum((np.asarray(SCREAMING_SNAKE_CASE   ) - np.asarray(SCREAMING_SNAKE_CASE   )) ** 2   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return sum((va - va) ** 2 for va, va in zip(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )   ) ** (1 / 2)


if __name__ == "__main__":
		def 					lowerCamelCase_    (    ):
					'''simple docstring'''
					from timeit import timeit

					print("""Without Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )
					print("""With Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )




		benchmark()






