






"""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


lowercase    			=       logging.get_logger(__name__)

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

lowercase    			=       {
    '''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
lowercase    			=       {
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

lowercase    			=       '''▁'''

class 						A_							(      __UpperCAmelCase  ):
				UpperCAmelCase__        =				VOCAB_FILES_NAMES
				UpperCAmelCase__        =				PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase__        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase__        =				["""input_ids""", """attention_mask"""]







				def __init__(							self							:						Optional[int]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Union[str, Any]="</s>"						,    __lowerCamelCase							:						str="<unk>"						,    __lowerCamelCase							:						int="<pad>"						,    __lowerCamelCase							:						List[Any]=1_0_0						,    __lowerCamelCase							:						List[str]=None						,    __lowerCamelCase							:						List[Any] = None						,    __lowerCamelCase							:						Any=True						,    **__lowerCamelCase							:						Tuple						,    )				->					List[str]:
										# Add extra_ids to the special token list
										if extra_ids > 0 and additional_special_tokens is None:
																__magic_name__            =     [f'''<extra_id_{i}>''' for i in range(UpperCAmelCase_	)]
										elif extra_ids > 0 and additional_special_tokens is not None:
																# Check that we have the right number of extra_id special tokens
																__magic_name__            =     len(set(filter(lambda __lowerCamelCase	: bool("extra_id" in str(UpperCAmelCase_	)	)						,    UpperCAmelCase_	)	)	)
																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"	)

										__magic_name__            =     legacy
										__magic_name__            =     {} if sp_model_kwargs is None else sp_model_kwargs

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

										__magic_name__            =     vocab_file
										__magic_name__            =     extra_ids

										__magic_name__            =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.Load(UpperCAmelCase_	)







				@staticmethod
				def     _snake_case					(							__lowerCamelCase							:						int						,    __lowerCamelCase							:						Optional[Any]						,    __lowerCamelCase							:						List[Any]	)				->					Tuple:
										if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																__magic_name__            =     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."						,    UpperCAmelCase_						,    )

										return max_model_length







				@property
				def     _snake_case					(							self							:						Optional[Any]	)				->					Optional[int]:
										return self.sp_model.get_piece_size() + self._extra_ids







				def     _snake_case					(							self							:						Dict	)				->					List[str]:
										__magic_name__            =     {self.convert_ids_to_tokens(UpperCAmelCase_	): i for i in range(self.vocab_size	)}
										vocab.update(self.added_tokens_encoder	)
										return vocab







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Any = None						,    __lowerCamelCase							:						Dict = False	)				->					int:
										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=UpperCAmelCase_						,    token_ids_a=UpperCAmelCase_						,    already_has_special_tokens=UpperCAmelCase_	)

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







				def     _snake_case					(							self							:						Any	)				->					List[Any]:
										return list(
										    set(filter(lambda __lowerCamelCase	: bool(re.search(R"<extra_id_\d+>"						,    UpperCAmelCase_	)	) is not None						,    self.additional_special_tokens	)	)	)







				def     _snake_case					(							self							:						int	)				->					Any:
										return [self._convert_token_to_id(UpperCAmelCase_	) for token in self.get_sentinel_tokens()]







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						List[Any]	)				->					Dict:
										if len(UpperCAmelCase_	) > 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     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Any = None	)				->					Any:
										__magic_name__            =     [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     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Optional[Any] = None	)				->					Optional[Any]:
										__magic_name__            =     self._add_eos_if_not_present(UpperCAmelCase_	)
										if token_ids_a is None:
																return token_ids_a
										else:
																__magic_name__            =     self._add_eos_if_not_present(UpperCAmelCase_	)
																return token_ids_a + token_ids_a







				def __getstate__(							self							:						Tuple	)				->					Tuple:
										__magic_name__            =     self.__dict__.copy()
										__magic_name__            =     None
										return state







				def __setstate__(							self							:						Tuple						,    __lowerCamelCase							:						Optional[Any]	)				->					Any:
										__magic_name__            =     d

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

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







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						Tuple						,    **__lowerCamelCase							:						Any	)				->					List[Any]:
										# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
										# the beginning of the text
										if not self.legacy:
																__magic_name__            =     SPIECE_UNDERLINE + text.replace(UpperCAmelCase_						,    " "	)
										return super().tokenize(UpperCAmelCase_						,    **UpperCAmelCase_	)







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						Union[str, Any]						,    **__lowerCamelCase							:						Union[str, Any]	)				->					Tuple:
										if not self.legacy:
																__magic_name__            =     text.startswith(UpperCAmelCase_	)
																if is_first:
																						__magic_name__            =     text[1:]

										__magic_name__            =     self.sp_model.encode(UpperCAmelCase_						,    out_type=UpperCAmelCase_	)

										if not self.legacy and not is_first and not text.startswith(" "	) and tokens[0].startswith(UpperCAmelCase_	):
																__magic_name__            =     ([tokens[0][1:]] if len(tokens[0]	) > 1 else []) + tokens[1:]
										return tokens







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Dict	)				->					List[Any]:
										if token.startswith("<extra_id_"	):
																__magic_name__            =     re.match(R"<extra_id_(\d+)>"						,    UpperCAmelCase_	)
																__magic_name__            =     int(match.group(1	)	)
																return self.vocab_size - num - 1
										return self.sp_model.piece_to_id(UpperCAmelCase_	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						str	)				->					str:
										if index < self.sp_model.get_piece_size():
																__magic_name__            =     self.sp_model.IdToPiece(UpperCAmelCase_	)
										else:
																__magic_name__            =     f'''<extra_id_{self.vocab_size - 1 - index}>'''
										return token







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						List[str]	)				->					Any:
										__magic_name__            =     []
										__magic_name__            =     ""
										__magic_name__            =     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(UpperCAmelCase_	) + token
																						__magic_name__            =     True
																						__magic_name__            =     []
																else:
																						current_sub_tokens.append(UpperCAmelCase_	)
																						__magic_name__            =     False
										out_string += self.sp_model.decode(UpperCAmelCase_	)
										return out_string.strip()







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						Union[str, Any] = None	)				->					Union[str, Any]:
										if not os.path.isdir(UpperCAmelCase_	):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
																return
										__magic_name__            =     os.path.join(
										    UpperCAmelCase_						,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

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

										return (out_vocab_file,)











"""simple docstring"""

from __future__ import annotations

from random import choice






def _lowerCAmelCase	(  __lowerCamelCase:Optional[int]   ):

						'''simple docstring'''






						return choice(__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:list[int] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     random_pivot(__lowerCamelCase   )

						# partition based on pivot
						# linear time
						__magic_name__            =     [e for e in lst if e < pivot]
						__magic_name__            =     [e for e in lst if e > pivot]

						# if we get lucky, pivot might be the element we want.
						# we can easily see this:
						# small (elements smaller than k)
						# + pivot (kth element)
						# + big (elements larger than k)
						if len(__lowerCamelCase   ) == k - 1:
												return pivot
						# pivot is in elements bigger than k
						elif len(__lowerCamelCase   ) < k - 1:
												return kth_number(__lowerCamelCase ,						k - len(__lowerCamelCase   ) - 1   )
						# pivot is in elements smaller than k
						else:
												return kth_number(__lowerCamelCase ,						__lowerCamelCase   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



