





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       :Dict       =  logging.get_logger(__name__)

__lowercase       :List[Any]       =  {"vocab_file": "spiece.model"}

__lowercase       :Union[str, Any]       =  {
    "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       :List[Any]       =  {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}

__lowercase       :List[str]       =  "▁"



class    _a     (				lowercase__							):






							"""simple docstring"""

							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				:     Tuple							,	a				:     int							,	a				:     Optional[Any]="</s>"							,	a				:     Optional[Any]="<unk>"							,	a				:     Tuple="<pad>"							,	a				:     int=1_00							,	a				:     Tuple=None							,	a				:     Optional[Dict[str, Any]] = None							,	a				:     List[Any]=True							,	**a				:     Dict							,	)	->None:
													# Add extra_ids to the special token list
													if extra_ids > 0 and additional_special_tokens is None:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					[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
																			SCREAMING_SNAKE_CASE__     :				Dict   =					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"    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					legacy
													SCREAMING_SNAKE_CASE__     :				Any   =					{} 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							,	)

													SCREAMING_SNAKE_CASE__     :				Dict   =					vocab_file
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					extra_ids

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					spm.SentencePieceProcessor(**self.sp_model_kwargs    )
													self.sp_model.Load(a    )

							@staticmethod
							def      A_  (      a				:     Any							,	a				:     List[Any]							,	a				:     Dict    )	->str:
													if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																			SCREAMING_SNAKE_CASE__     :				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      A_  (      self				:     List[str]    )	->int:
													return self.sp_model.get_piece_size() + self._extra_ids

							def      A_  (      self				:     str    )	->int:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					{self.convert_ids_to_tokens(a    ): i for i in range(self.vocab_size    )}
													vocab.update(self.added_tokens_encoder    )
													return vocab

							def      A_  (      self				:     int							,	a				:     List[int]							,	a				:     Optional[List[int]] = None							,	a				:     bool = False    )	->List[int]:
													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      A_  (      self				:     Optional[int]    )	->List[Any]:
													return list(
													    set(filter(lambda a    : bool(re.search(r"<extra_id_\d+>"							,	a    )    ) is not None							,	self.additional_special_tokens    )    )    )

							def      A_  (      self				:     Union[str, Any]    )	->Dict:
													return [self._convert_token_to_id(a    ) for token in self.get_sentinel_tokens()]

							def      A_  (      self				:     str							,	a				:     List[int]    )	->List[int]:
													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      A_  (      self				:     str							,	a				:     List[int]							,	a				:     Optional[List[int]] = None    )	->List[int]:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					[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      A_  (      self				:     str							,	a				:     List[int]							,	a				:     Optional[List[int]] = None    )	->List[int]:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self._add_eos_if_not_present(a    )
													if token_ids_a is None:
																			return token_ids_a
													else:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self._add_eos_if_not_present(a    )
																			return token_ids_a + token_ids_a

							def __getstate__(      self				:     List[Any]    )	->str:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.__dict__.copy()
													SCREAMING_SNAKE_CASE__     :				str   =					None
													return state

							def __setstate__(      self				:     Any							,	a				:     List[str]    )	->Tuple:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					d

													# for backward compatibility
													if not hasattr(self							,	"sp_model_kwargs"    ):
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					{}

													SCREAMING_SNAKE_CASE__     :				Dict   =					spm.SentencePieceProcessor(**self.sp_model_kwargs    )
													self.sp_model.Load(self.vocab_file    )

							def      A_  (      self				:     Dict							,	a				:     "TextInput"							,	**a				:     Dict    )	->List[str]:
													# 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:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					SPIECE_UNDERLINE + text.replace(a							,	" "    )
													return super().tokenize(a							,	**a    )

							def      A_  (      self				:     List[str]							,	a				:     List[Any]							,	**a				:     Union[str, Any]    )	->List[str]:
													if not self.legacy:
																			SCREAMING_SNAKE_CASE__     :				int   =					text.startswith(a    )
																			if is_first:
																									SCREAMING_SNAKE_CASE__     :				int   =					text[1:]

													SCREAMING_SNAKE_CASE__     :				Dict   =					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    ):
																			SCREAMING_SNAKE_CASE__     :				Dict   =					([tokens[0][1:]] if len(tokens[0]    ) > 1 else []) + tokens[1:]
													return tokens

							def      A_  (      self				:     int							,	a				:     List[Any]    )	->Optional[Any]:
													if token.startswith("<extra_id_"    ):
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					re.match(r"<extra_id_(\d+)>"							,	a    )
																			SCREAMING_SNAKE_CASE__     :				Dict   =					int(match.group(1    )    )
																			return self.vocab_size - num - 1
													return self.sp_model.piece_to_id(a    )

							def      A_  (      self				:     Optional[int]							,	a				:     Union[str, Any]    )	->List[str]:
													if index < self.sp_model.get_piece_size():
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.sp_model.IdToPiece(a    )
													else:
																			SCREAMING_SNAKE_CASE__     :				int   =					f"""<extra_id_{self.vocab_size - 1 - index}>"""
													return token

							def      A_  (      self				:     Any							,	a				:     Union[str, Any]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					[]
													SCREAMING_SNAKE_CASE__     :				Dict   =					""
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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
																									SCREAMING_SNAKE_CASE__     :				int   =					True
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
																			else:
																									current_sub_tokens.append(a    )
																									SCREAMING_SNAKE_CASE__     :				List[str]   =					False
													out_string += self.sp_model.decode(a    )
													return out_string.strip()






							def      A_  (      self				:     Any							,	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__     :				str   =					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:
																									SCREAMING_SNAKE_CASE__     :				Any   =					self.sp_model.serialized_model_proto()
																									fi.write(a    )

													return (out_vocab_file,)






from __future__ import annotations







def       UpperCAmelCase							(     _lowerCamelCase      :  list			,   _lowerCamelCase      :  int | None = None			,   _lowerCamelCase      :  int | None = None     ):





						'''simple docstring'''


						if start is None:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					0

						if end is None:
												SCREAMING_SNAKE_CASE__     :				Any   =					len(_lowerCamelCase     ) - 1

						if start >= end:
												return

						SCREAMING_SNAKE_CASE__     :				List[str]   =					(start + end) // 2

						slowsort(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )
						slowsort(_lowerCamelCase			,   mid + 1			,   _lowerCamelCase     )

						if sequence[end] < sequence[mid]:
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					sequence[mid], sequence[end]

						slowsort(_lowerCamelCase			,   _lowerCamelCase			,   end - 1     )


if __name__ == "__main__":
				from doctest import testmod

				testmod()