




'''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_						:      Optional[Any]       =						logging.get_logger(__name__)

UpperCamelCase_						:      List[str]       =						{'''vocab_file''': '''spiece.model'''}

UpperCamelCase_						:      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
UpperCamelCase_						:      Union[str, Any]       =						{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

UpperCamelCase_						:      Tuple       =						'''▁'''

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_							:   List[str]       =						["""input_ids""", """attention_mask"""]






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE="</s>" ,_SCREAMING_SNAKE_CASE="<unk>" ,_SCREAMING_SNAKE_CASE="<pad>" ,_SCREAMING_SNAKE_CASE=100 ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE=True ,**_SCREAMING_SNAKE_CASE ,)							->      None:
          # Add extra_ids to the special token list
          if extra_ids > 0 and additional_special_tokens is None:
               _snake_case							=				[f"""<extra_id_{i}>""" for i in range(_SCREAMING_SNAKE_CASE   )]
          elif extra_ids > 0 and additional_special_tokens is not None:
               # Check that we have the right number of extra_id special tokens
               _snake_case							=				len(set(filter(lambda _SCREAMING_SNAKE_CASE   : bool("extra_id" in str(_SCREAMING_SNAKE_CASE   )   ) ,_SCREAMING_SNAKE_CASE   )   )   )
               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"   )

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

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

          _snake_case							=				vocab_file
          _snake_case							=				extra_ids

          _snake_case							=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
          self.sp_model.Load(_SCREAMING_SNAKE_CASE   )






     @staticmethod
     def     _lowercase     (			_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      int:
          if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
               _snake_case							=				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." ,_SCREAMING_SNAKE_CASE ,)

          return max_model_length






     @property
     def     _lowercase     (			self   )							->      Any:
          return self.sp_model.get_piece_size() + self._extra_ids






     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE   ): i for i in range(self.vocab_size   )}
          vocab.update(self.added_tokens_encoder   )
          return vocab






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False   )							->      List[int]:
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_SCREAMING_SNAKE_CASE ,token_ids_a=_SCREAMING_SNAKE_CASE ,already_has_special_tokens=_SCREAMING_SNAKE_CASE   )

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






     def     _lowercase     (			self   )							->      Dict:
          return list(
              set(filter(lambda _SCREAMING_SNAKE_CASE   : bool(re.search(r"<extra_id_\d+>" ,_SCREAMING_SNAKE_CASE   )   ) is not None ,self.additional_special_tokens   )   )   )






     def     _lowercase     (			self   )							->      Any:
          return [self._convert_token_to_id(_SCREAMING_SNAKE_CASE   ) for token in self.get_sentinel_tokens()]






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[int]:
          if len(_SCREAMING_SNAKE_CASE   ) > 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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[int]:
          _snake_case							=				[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     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[int]:
          _snake_case							=				self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE   )
          if token_ids_a is None:
               return token_ids_a
          else:
               _snake_case							=				self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE   )
               return token_ids_a + token_ids_a






     def __getstate__(			self   )							->      Any:
          _snake_case							=				self.__dict__.copy()
          _snake_case							=				None
          return state






     def __setstate__(			self ,_SCREAMING_SNAKE_CASE   )							->      Any:
          _snake_case							=				d

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

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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      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:
               _snake_case							=				SPIECE_UNDERLINE + text.replace(_SCREAMING_SNAKE_CASE ," "   )
          return super().tokenize(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )							->      Optional[int]:
          if not self.legacy:
               _snake_case							=				text.startswith(_SCREAMING_SNAKE_CASE   )
               if is_first:
                    _snake_case							=				text[1:]

          _snake_case							=				self.sp_model.encode(_SCREAMING_SNAKE_CASE ,out_type=_SCREAMING_SNAKE_CASE   )

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






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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Optional[int]:
          if index < self.sp_model.get_piece_size():
               _snake_case							=				self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE   )
          else:
               _snake_case							=				f"""<extra_id_{self.vocab_size - 1 - index}>"""
          return token






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          _snake_case							=				[]
          _snake_case							=				""
          _snake_case							=				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(_SCREAMING_SNAKE_CASE   ) + token
                    _snake_case							=				True
                    _snake_case							=				[]
               else:
                    current_sub_tokens.append(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				False
          out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE   )
          return out_string.strip()






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      Tuple[str]:
          if not os.path.isdir(_SCREAMING_SNAKE_CASE   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
          _snake_case							=				os.path.join(
              _SCREAMING_SNAKE_CASE ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

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

          return (out_vocab_file,)








'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

UpperCamelCase_						:      int       =						TypeVar('''T''')
UpperCamelCase_						:      Dict       =						TypeVar('''U''')

class        _a    (						Generic[T, U]							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Dict:
          _snake_case							=				key
          _snake_case							=				val
          _snake_case							=				None
          _snake_case							=				None






     def __repr__(			self   )							->      str:
          return (
              f"""Node: key: {self.key}, val: {self.val}, """
              f"""has next: {bool(self.next   )}, has prev: {bool(self.prev   )}"""
          )







class        _a    (						Generic[T, U]							):






     def __init__(			self   )							->      None:
          _snake_case							=				DoubleLinkedListNode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				DoubleLinkedListNode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							, _snake_case							=				self.rear, self.head






     def __repr__(			self   )							->      str:
          _snake_case							=				["DoubleLinkedList"]
          _snake_case							=				self.head
          while node.next is not None:
               rep.append(str(_SCREAMING_SNAKE_CASE   )   )
               _snake_case							=				node.next
          rep.append(str(self.rear   )   )
          return ",\n    ".join(_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      None:
          _snake_case							=				self.rear.prev

          # All nodes other than self.head are guaranteed to have non-None previous
          assert previous is not None

          _snake_case							=				node
          _snake_case							=				previous
          _snake_case							=				node
          _snake_case							=				self.rear






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      DoubleLinkedListNode[T, U] | None:

          if node.prev is None or node.next is None:
               return None

          _snake_case							=				node.next
          _snake_case							=				node.prev
          _snake_case							=				None
          _snake_case							=				None
          return node







class        _a    (						Generic[T, U]							):
     SCREAMING_SNAKE_CASE_							:   dict[Callable[[T], U], LRUCache[T, U]]       =						{}






     def __init__(			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          _snake_case							=				DoubleLinkedList()
          _snake_case							=				capacity
          _snake_case							=				0
          _snake_case							=				0
          _snake_case							=				0
          _snake_case							=				{}






     def __repr__(			self   )							->      str:
          return (
              f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
              f"""capacity={self.capacity}, current size={self.num_keys})"""
          )






     def __contains__(			self ,_SCREAMING_SNAKE_CASE   )							->      bool:
          return key in self.cache






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      U | None:
          # Note: pythonic interface would throw KeyError rather than return None

          if key in self.cache:
               self.hits += 1
               _snake_case							=				self.cache[key]
               _snake_case							=				self.list.remove(self.cache[key]   )
               assert node == value_node

               # node is guaranteed not None because it is in self.cache
               assert node is not None
               self.list.add(_SCREAMING_SNAKE_CASE   )
               return node.val
          self.miss += 1
          return None






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      None:

          if key not in self.cache:
               if self.num_keys >= self.capacity:
                    # delete first node (oldest) when over capacity
                    _snake_case							=				self.list.head.next

                    # guaranteed to have a non-None first node when num_keys > 0
                    # explain to type checker via assertions
                    assert first_node is not None
                    assert first_node.key is not None
                    assert (
                        self.list.remove(_SCREAMING_SNAKE_CASE   ) is not None
                    )  # node guaranteed to be in list assert node.key is not None

                    del self.cache[first_node.key]
                    self.num_keys -= 1
               _snake_case							=				DoubleLinkedListNode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.list.add(self.cache[key]   )
               self.num_keys += 1

          else:
               # bump node to the end of the list, update value
               _snake_case							=				self.list.remove(self.cache[key]   )
               assert node is not None  # node guaranteed to be in list
               _snake_case							=				value
               self.list.add(_SCREAMING_SNAKE_CASE   )






     @classmethod
     def     _lowercase     (			cls ,_SCREAMING_SNAKE_CASE = 128   )							->      Callable[[Callable[[T], U]], Callable[..., U]]:

          def cache_decorator_inner(_SCREAMING_SNAKE_CASE   ) -> Callable[..., U]:
               def cache_decorator_wrapper(*_SCREAMING_SNAKE_CASE   ) -> U:
                    if func not in cls.decorator_function_to_instance_map:
                         _snake_case							=				LRUCache(_SCREAMING_SNAKE_CASE   )

                    _snake_case							=				cls.decorator_function_to_instance_map[func].get(args[0]   )
                    if result is None:
                         _snake_case							=				func(*_SCREAMING_SNAKE_CASE   )
                         cls.decorator_function_to_instance_map[func].put(args[0] ,_SCREAMING_SNAKE_CASE   )
                    return result

               def cache_info() -> LRUCache[T, U]:
                    return cls.decorator_function_to_instance_map[func]

               setattr(_SCREAMING_SNAKE_CASE ,"cache_info" ,_SCREAMING_SNAKE_CASE   )  # noqa: B010

               return cache_decorator_wrapper

          return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()


