





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[int | float]			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int     ):





						'''simple docstring'''


						if len(_lowerCamelCase     ) == 0:
												raise ValueError("find_max() arg is an empty sequence"     )
						if (
						    left >= len(_lowerCamelCase     )
						    or left < -len(_lowerCamelCase     )
						    or right >= len(_lowerCamelCase     )
						    or right < -len(_lowerCamelCase     )
						):
												raise IndexError("list index out of range"     )
						if left == right:
												return nums[left]
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					(left + right) >> 1  # the middle
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					find_max(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )  # find max in range[left, mid]
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					find_max(_lowerCamelCase			,   mid + 1			,   _lowerCamelCase     )  # find max in range[mid + 1, right]

						return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)