


import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_      :			Any		  =      logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_      :			str		  =      {'''vocab_file''': '''spiece.model'''}

SCREAMING_SNAKE_CASE_      :			Optional[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''',
    },
}

SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      {
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

SCREAMING_SNAKE_CASE_      :			Tuple		  =      '''▁'''

class 		snake_case_      (       lowerCamelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		VOCAB_FILES_NAMES
	__UpperCamelCase        =		PRETRAINED_VOCAB_FILES_MAP
	__UpperCamelCase        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__UpperCamelCase        =		["""input_ids""", """token_type_ids"""]




	def __init__(	self       :    Union[str, Any]				,			__lowerCamelCase       :    Any				,			__lowerCamelCase       :    List[Any]=False				,			__lowerCamelCase       :    Dict=True				,			__lowerCamelCase       :    Any=True				,			__lowerCamelCase       :    Union[str, Any]="<unk>"				,			__lowerCamelCase       :    Any="[SEP]"				,			__lowerCamelCase       :    Optional[Any]="<pad>"				,			__lowerCamelCase       :    Any="[CLS]"				,			__lowerCamelCase       :    Tuple="[MASK]"				,			__lowerCamelCase       :    Optional[int] = None				,			**__lowerCamelCase       :    Optional[Any]				,			)					->   Tuple:

			'''simple docstring'''




			__lowercase					=    (
			    AddedToken(__lowerCamelCase				,			lstrip=__lowerCamelCase				,			rstrip=__lowerCamelCase				,			normalized=__lowerCamelCase      )
			    if isinstance(__lowerCamelCase				,			__lowerCamelCase      )
			    else mask_token
			)

			__lowercase					=    {} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    do_lower_case=__lowerCamelCase				,			remove_space=__lowerCamelCase				,			keep_accents=__lowerCamelCase				,			unk_token=__lowerCamelCase				,			sep_token=__lowerCamelCase				,			pad_token=__lowerCamelCase				,			cls_token=__lowerCamelCase				,			mask_token=__lowerCamelCase				,			sp_model_kwargs=self.sp_model_kwargs				,			**__lowerCamelCase				,			)

			__lowercase					=    do_lower_case
			__lowercase					=    remove_space
			__lowercase					=    keep_accents
			__lowercase					=    vocab_file

			__lowercase					=    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
			self.sp_model.Load(__lowerCamelCase      )




	@property
	def       UpperCAmelCase (	self       :    Any      )					->   str:

			'''simple docstring'''




			return len(self.sp_model      )




	def       UpperCAmelCase (	self       :    Any      )					->   Any:

			'''simple docstring'''




			__lowercase					=    {self.convert_ids_to_tokens(__lowerCamelCase      ): i for i in range(self.vocab_size      )}
			vocab.update(self.added_tokens_encoder      )
			return vocab




	def __getstate__(	self       :    List[Any]      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    self.__dict__.copy()
			__lowercase					=    None
			return state




	def __setstate__(	self       :    Dict				,			__lowerCamelCase       :    Any      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    d

			# for backward compatibility
			if not hasattr(self				,			'sp_model_kwargs'      ):
					__lowercase					=    {}

			__lowercase					=    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
			self.sp_model.Load(self.vocab_file      )




	def       UpperCAmelCase (	self       :    List[str]				,			__lowerCamelCase       :    Optional[Any]      )					->   str:

			'''simple docstring'''




			if self.remove_space:
					__lowercase					=    " ".join(inputs.strip().split()      )
			else:
					__lowercase					=    inputs
			__lowercase					=    outputs.replace('``'				,			'\"'      ).replace('\'\''				,			'\"'      )

			if not self.keep_accents:
					__lowercase					=    unicodedata.normalize('NFKD'				,			__lowerCamelCase      )
					__lowercase					=    "".join([c for c in outputs if not unicodedata.combining(__lowerCamelCase      )]      )
			if self.do_lower_case:
					__lowercase					=    outputs.lower()

			return outputs




	def       UpperCAmelCase (	self       :    Dict				,			__lowerCamelCase       :    str      )					->   int:

			'''simple docstring'''




			__lowercase					=    self.preprocess_text(__lowerCamelCase      )
			__lowercase					=    self.sp_model.encode(__lowerCamelCase				,			out_type=__lowerCamelCase      )
			__lowercase					=    []
			for piece in pieces:
					if len(__lowerCamelCase      ) > 1 and piece[-1] == str(','      ) and piece[-2].isdigit():
							__lowercase					=    self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase				,			''      )      )
							if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
									if len(cur_pieces[0]      ) == 1:
											__lowercase					=    cur_pieces[1:]
									else:
											__lowercase					=    cur_pieces[0][1:]
							cur_pieces.append(piece[-1]      )
							new_pieces.extend(__lowerCamelCase      )
					else:
							new_pieces.append(__lowerCamelCase      )

			return new_pieces




	def       UpperCAmelCase (	self       :    List[str]				,			__lowerCamelCase       :    List[Any]      )					->   Tuple:

			'''simple docstring'''




			return self.sp_model.PieceToId(__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Tuple				,			__lowerCamelCase       :    Optional[Any]      )					->   List[str]:

			'''simple docstring'''




			return self.sp_model.IdToPiece(__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    Optional[int]      )					->   int:

			'''simple docstring'''




			__lowercase					=    []
			__lowercase					=    ""
			__lowercase					=    False
			for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
							if not prev_is_special:
									out_string += " "
							out_string += self.sp_model.decode(__lowerCamelCase      ) + token
							__lowercase					=    True
							__lowercase					=    []
					else:
							current_sub_tokens.append(__lowerCamelCase      )
							__lowercase					=    False
			out_string += self.sp_model.decode(__lowerCamelCase      )
			return out_string.strip()




	def       UpperCAmelCase (	self       :    Optional[int]				,			__lowerCamelCase       :    List[Any]				,			__lowerCamelCase       :    str = False				,			__lowerCamelCase       :    int = None				,			__lowerCamelCase       :    Optional[int] = True				,			**__lowerCamelCase       :    Tuple				,			)					->   Tuple:

			'''simple docstring'''




			__lowercase					=    kwargs.pop('use_source_tokenizer'				,			__lowerCamelCase      )

			__lowercase					=    self.convert_ids_to_tokens(__lowerCamelCase				,			skip_special_tokens=__lowerCamelCase      )

			# To avoid mixing byte-level and unicode for byte-level BPT
			# we need to build string separately for added tokens and byte-level tokens
			# cf. https://github.com/huggingface/transformers/issues/1133
			__lowercase					=    []
			__lowercase					=    []
			for token in filtered_tokens:
					if skip_special_tokens and token in self.all_special_ids:
							continue
					if token in self.added_tokens_encoder:
							if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase      )      )
									__lowercase					=    []
							sub_texts.append(__lowerCamelCase      )
					else:
							current_sub_text.append(__lowerCamelCase      )
			if current_sub_text:
					sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase      )      )

			# Mimic the behavior of the Rust tokenizer:
			# No space after <unk>
			if spaces_between_special_tokens:
					__lowercase					=    re.sub(R'(<unk>) '				,			R'\1'				,			' '.join(__lowerCamelCase      )      )
			else:
					__lowercase					=    "".join(__lowerCamelCase      )

			__lowercase					=    (
			    clean_up_tokenization_spaces
			    if clean_up_tokenization_spaces is not None
			    else self.clean_up_tokenization_spaces
			)
			if clean_up_tokenization_spaces:
					__lowercase					=    self.clean_up_tokenization(__lowerCamelCase      )
					return clean_text
			else:
					return text




	def       UpperCAmelCase (	self       :    Tuple				,			__lowerCamelCase       :    Optional[Any]				,			__lowerCamelCase       :    Union[str, Any] = None      )					->   str:

			'''simple docstring'''




			__lowercase					=    [self.sep_token_id]
			__lowercase					=    [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       :    Optional[int]				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    List[str] = None				,			__lowerCamelCase       :    List[str] = False      )					->   Union[str, Any]:

			'''simple docstring'''




			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=__lowerCamelCase				,			token_ids_a=__lowerCamelCase				,			already_has_special_tokens=__lowerCamelCase      )

			if token_ids_a is not None:
					return [1] + ([0] * len(__lowerCamelCase      )) + [1] + ([0] * len(__lowerCamelCase      )) + [1]
			return [1] + ([0] * len(__lowerCamelCase      )) + [1]




	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    List[str]				,			__lowerCamelCase       :    Dict = None      )					->   Optional[Any]:

			'''simple docstring'''




			__lowercase					=    [self.sep_token_id]
			__lowercase					=    [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       :    int				,			__lowerCamelCase       :    Union[str, Any]				,			__lowerCamelCase       :    List[Any] = None      )					->   Optional[int]:

			'''simple docstring'''




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

			if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCamelCase      ) and os.path.isfile(self.vocab_file      ):
					copyfile(self.vocab_file				,			__lowerCamelCase      )
			elif not os.path.isfile(self.vocab_file      ):
					with open(__lowerCamelCase				,			'wb'      ) as fi:
							__lowercase					=    self.sp_model.serialized_model_proto()
							fi.write(__lowerCamelCase      )

			return (out_vocab_file,)











from __future__ import annotations



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						None:
		__lowercase	,			__lowercase					=    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    SCREAMING_SNAKE_CASE							(			)		->						None:
		__lowercase					=    [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
		__lowercase					=    int(input('Enter the element to be searched:'       )       )
		print(snake_case       )
		search_in_a_sorted_matrix(snake_case	,      len(snake_case       )	,      len(mat[0]       )	,      snake_case       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




