




"""simple docstring"""






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
				from ...tokenization_utils_base import TextInput
from ...utils import logging


UpperCamelCase__						=			logging.get_logger(__name__)

UpperCamelCase__						=			{'''vocab_file''': '''spiece.model'''}

UpperCamelCase__						=			{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    }
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCamelCase__						=			{
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}

UpperCamelCase__						=			'''▁'''




class   a__			(       UpperCamelCase_  ):
						snake_case__             = VOCAB_FILES_NAMES
						snake_case__             = PRETRAINED_VOCAB_FILES_MAP
						snake_case__             = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						snake_case__             = ['''input_ids''', '''attention_mask''']





						def __init__(							self      :	List[Any]     ,a__      :	Optional[Any]     ,a__      :	List[str]="</s>"     ,a__      :	Optional[int]="<unk>"     ,a__      :	Optional[int]="<pad>"     ,a__      :	Optional[Any]=100     ,a__      :	List[str]=None     ,a__      :	Optional[Dict[str, Any]] = None     ,a__      :	Optional[Any]=True     ,**a__      :	List[str]     ,)						->       None:




											"""simple docstring"""







											if extra_ids > 0 and additional_special_tokens is None:
																_lowerCAmelCase:List[Any]									=  [F'<extra_id_{i}>' for i in range(a__)]
											elif extra_ids > 0 and additional_special_tokens is not None:
																# Check that we have the right number of extra_id special tokens
																_lowerCAmelCase:List[Any]									=  len(set(filter(lambda a__: bool('''extra_id''' in str(a__))     ,a__)))
																if extra_tokens != extra_ids:
																					raise ValueError(
																					    F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
																					    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																					    ''' tokens''')
											if legacy:
																logger.warning_once(
																    F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
																    ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''')

											_lowerCAmelCase:int									=  legacy
											_lowerCAmelCase:List[str]									=  {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    eos_token=a__     ,unk_token=a__     ,pad_token=a__     ,extra_ids=a__     ,additional_special_tokens=a__     ,sp_model_kwargs=self.sp_model_kwargs     ,legacy=a__     ,**a__     ,)

											_lowerCAmelCase:List[str]									=  vocab_file
											_lowerCAmelCase:Any									=  extra_ids

											_lowerCAmelCase:Optional[int]									=  spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(a__)





						@staticmethod
						def       __UpperCamelCase     (							a__      :	int     ,a__      :	int     ,a__      :	List[str])						->       List[Any]:




											"""simple docstring"""







											if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																_lowerCAmelCase:Optional[int]									=  TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
																if init_max_model_length is not None and init_max_model_length != max_model_length:
																					return init_max_model_length
																elif init_max_model_length is None:
																					warnings.warn(
																					    '''This tokenizer was incorrectly instantiated with a model max length of'''
																					    F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
																					    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																					    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																					    F' {pretrained_model_name_or_path} automatically truncating your input to'
																					    F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
																					    F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
																					    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																					    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''     ,a__     ,)

											return max_model_length





						@property
						def       __UpperCamelCase     (							self      :	Dict)						->       List[str]:




											"""simple docstring"""







											return self.sp_model.get_piece_size() + self._extra_ids





						def       __UpperCamelCase     (							self      :	Dict)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  {self.convert_ids_to_tokens(a__): i for i in range(self.vocab_size)}
											vocab.update(self.added_tokens_encoder)
											return vocab





						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	List[int]     ,a__      :	Optional[List[int]] = None     ,a__      :	bool = False)						->       List[int]:




											"""simple docstring"""







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

											# normal case: some special tokens
											if token_ids_a is None:
																return ([0] * len(a__)) + [1]
											return ([0] * len(a__)) + [1] + ([0] * len(a__)) + [1]





						def       __UpperCamelCase     (							self      :	List[str])						->       Optional[int]:




											"""simple docstring"""







											return list(
											    set(filter(lambda a__: bool(re.search(R'''<extra_id_\d+>'''     ,a__)) is not None     ,self.additional_special_tokens)))





						def       __UpperCamelCase     (							self      :	List[Any])						->       int:




											"""simple docstring"""







											return [self._convert_token_to_id(a__) for token in self.get_sentinel_tokens()]





						def       __UpperCamelCase     (							self      :	int     ,a__      :	List[int])						->       List[int]:




											"""simple docstring"""







											if len(a__) > 0 and token_ids[-1] == self.eos_token_id:
																warnings.warn(
																    F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
																    ''' eos tokens being added.''')
																return token_ids
											else:
																return token_ids + [self.eos_token_id]





						def       __UpperCamelCase     (							self      :	int     ,a__      :	List[int]     ,a__      :	Optional[List[int]] = None)						->       List[int]:




											"""simple docstring"""







											_lowerCAmelCase:str									=  [self.eos_token_id]

											if token_ids_a is None:
																return len(token_ids_a + eos) * [0]
											return len(token_ids_a + eos + token_ids_a + eos) * [0]





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	List[int]     ,a__      :	Optional[List[int]] = None)						->       List[int]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self._add_eos_if_not_present(a__)
											if token_ids_a is None:
																return token_ids_a
											else:
																_lowerCAmelCase:int									=  self._add_eos_if_not_present(a__)
																return token_ids_a + token_ids_a





						def __getstate__(							self      :	Optional[Any])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  self.__dict__.copy()
											_lowerCAmelCase:Dict									=  None
											return state





						def __setstate__(							self      :	Optional[Any]     ,a__      :	Tuple)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  d

											# for backward compatibility
											if not hasattr(self     ,'''sp_model_kwargs'''):
																_lowerCAmelCase:Optional[Any]									=  {}

											_lowerCAmelCase:Optional[int]									=  spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(self.vocab_file)





						def       __UpperCamelCase     (							self      :	List[str]     ,a__      :	"TextInput"     ,**a__      :	Any)						->       List[str]:




											"""simple docstring"""







											if not self.legacy:
																_lowerCAmelCase:Optional[Any]									=  SPIECE_UNDERLINE + text.replace(a__     ,''' ''')
											return super().tokenize(a__     ,**a__)





						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	List[str]     ,**a__      :	Tuple)						->       Tuple:




											"""simple docstring"""







											if not self.legacy:
																_lowerCAmelCase:str									=  text.startswith(a__)
																if is_first:
																					_lowerCAmelCase:int									=  text[1:]

											_lowerCAmelCase:Union[str, Any]									=  self.sp_model.encode(a__     ,out_type=a__)

											if not self.legacy and not is_first and not text.startswith(''' ''') and tokens[0].startswith(a__):
																_lowerCAmelCase:List[str]									=  ([tokens[0][1:]] if len(tokens[0]) > 1 else []) + tokens[1:]
											return tokens





						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	str)						->       str:




											"""simple docstring"""







											if token.startswith('''<extra_id_'''):
																_lowerCAmelCase:str									=  re.match(R'''<extra_id_(\d+)>'''     ,a__)
																_lowerCAmelCase:List[str]									=  int(match.group(1))
																return self.vocab_size - num - 1
											return self.sp_model.piece_to_id(a__)





						def       __UpperCamelCase     (							self      :	Optional[Any]     ,a__      :	Dict)						->       Optional[int]:




											"""simple docstring"""







											if index < self.sp_model.get_piece_size():
																_lowerCAmelCase:Any									=  self.sp_model.IdToPiece(a__)
											else:
																_lowerCAmelCase:Optional[Any]									=  F'<extra_id_{self.vocab_size - 1 - index}>'
											return token





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	int)						->       List[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=  []
											_lowerCAmelCase:Tuple									=  ''''''
											_lowerCAmelCase:Union[str, Any]									=  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(a__) + token
																					_lowerCAmelCase:Tuple									=  True
																					_lowerCAmelCase:Optional[int]									=  []
																else:
																					current_sub_tokens.append(a__)
																					_lowerCAmelCase:Any									=  False
											out_string += self.sp_model.decode(a__)
											return out_string.strip()







						def       __UpperCamelCase     (							self      :	List[str]     ,a__      :	str     ,a__      :	Optional[str] = None)						->       Tuple[str]:




											"""simple docstring"""







											if not os.path.isdir(a__):
																logger.error(F'Vocabulary path ({save_directory}) should be a directory')
																return
											_lowerCAmelCase:Optional[int]									=  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__) and os.path.isfile(self.vocab_file):
																copyfile(self.vocab_file     ,a__)
											elif not os.path.isfile(self.vocab_file):
																with open(a__     ,'''wb''') as fi:
																					_lowerCAmelCase:Optional[Any]									=  self.sp_model.serialized_model_proto()
																					fi.write(a__)

											return (out_vocab_file,)












"""simple docstring"""






def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					str    ):
					assert x is not None
					assert y is not None

					_lowerCAmelCase:Optional[Any]									=  len(snake_case    )
					_lowerCAmelCase:Tuple									=  len(snake_case    )

					# declaring the array for storing the dp values
					_lowerCAmelCase:Optional[Any]									=  [[0] * (n + 1) for _ in range(m + 1    )]  # noqa: E741

					for i in range(1      ,       m + 1    ):
										for j in range(1      ,       n + 1    ):
															_lowerCAmelCase:Optional[int]									=  1 if x[i - 1] == y[j - 1] else 0

															_lowerCAmelCase:int									=  max(l[i - 1][j]      ,       l[i][j - 1]      ,       l[i - 1][j - 1] + match    )

					_lowerCAmelCase:List[Any]									=  ''''''
					_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  m, n
					while i > 0 and j > 0:
										_lowerCAmelCase:Any									=  1 if x[i - 1] == y[j - 1] else 0

										if l[i][j] == l[i - 1][j - 1] + match:
															if match == 1:
																				_lowerCAmelCase:Any									=  x[i - 1] + seq
															i -= 1
															j -= 1
										elif l[i][j] == l[i - 1][j]:
															i -= 1
										else:
															j -= 1

					return l[m][n], seq


if __name__ == "__main__":
				UpperCamelCase__						=			'''AGGTAB'''
				UpperCamelCase__						=			'''GXTXAYB'''
				UpperCamelCase__						=			4
				UpperCamelCase__						=			'''GTAB'''

				UpperCamelCase__						,							UpperCamelCase__						=			longest_common_subsequence(a, b)
				print('''len =''', ln, ''', sub-sequence =''', subseq)
				import doctest

				doctest.testmod()






