




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

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


if is_sentencepiece_available():
    from .tokenization_reformer import ReformerTokenizer
else:
    SCREAMING_SNAKE_CASE__    :    Any								=					None


SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__    :    int								=					"▁"

SCREAMING_SNAKE_CASE__    :    List[str]								=					{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__    :    str								=					{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

SCREAMING_SNAKE_CASE__    :    str								=					{
    "google/reformer-crime-and-punishment": 52_42_88,
}



class 			a_					(    a__    ):
 A						       =   VOCAB_FILES_NAMES
 A						       =   PRETRAINED_VOCAB_FILES_MAP
 A						       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A						       =   ["input_ids", "attention_mask"]
 A						       =   ReformerTokenizer





 def __init__(       self			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE="</s>"			,    SCREAMING_SNAKE_CASE="<unk>"			,    SCREAMING_SNAKE_CASE=[]			,    **SCREAMING_SNAKE_CASE			,    )     ->				int:




    """simple docstring"""



    super().__init__(
        SCREAMING_SNAKE_CASE_			,    tokenizer_file=SCREAMING_SNAKE_CASE_			,    eos_token=SCREAMING_SNAKE_CASE_			,    unk_token=SCREAMING_SNAKE_CASE_			,    additional_special_tokens=SCREAMING_SNAKE_CASE_			,    **SCREAMING_SNAKE_CASE_			,    )

    SCREAMING_SNAKE_CASE_			=	vocab_file
    SCREAMING_SNAKE_CASE_			=	False if not self.vocab_file else True





 def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				Tuple[str]:




    """simple docstring"""



    if not self.can_save_slow_tokenizer:
       raise ValueError(
           'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
           'tokenizer.'			)

    if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
       logger.error(f'Vocabulary path ({save_directory}) should be a directory'			)
       return
    SCREAMING_SNAKE_CASE_			=	os.path.join(
        SCREAMING_SNAKE_CASE_			,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

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

    return (out_vocab_file,)






from __future__ import annotations

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       None:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	m - 1, 0
			while i >= 0 and j < n:
						if key == mat[i][j]:
									print(F'Key {key} found at row- {i + 1} column- {j + 1}'   )
									return
						if key < mat[i][j]:
									i -= 1
						else:
									j += 1
			print(F'Key {key} not found'   )

def lowercase						(     ) ->       None:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
			SCREAMING_SNAKE_CASE_			=	int(input('Enter the element to be searched:'   )   )
			print(SCREAMING_SNAKE_CASE   )
			search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE					,						len(SCREAMING_SNAKE_CASE   )					,						len(mat[0]   )					,						SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()
