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

lowercase  :					Optional[Any]													=       {'vocab_file': 'spiece.model'}

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

lowercase  :					Optional[int]													=       '▁'




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     VOCAB_FILES_NAMES
	_A								=     PRETRAINED_VOCAB_FILES_MAP
	_A								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=     ['input_ids', 'attention_mask']







	def __init__(					self		:List[Any]     ,   a		:int     ,   a		:int="</s>"     ,   a		:List[str]="<unk>"     ,   a		:List[Any]="<pad>"     ,   a		:List[str]=1_0_0     ,   a		:Tuple=None     ,   a		:Optional[Dict[str, Any]] = None     ,   a		:str=True     ,   **a		:int     ,   )		-> None:
								# Add extra_ids to the special token list
								if extra_ids > 0 and additional_special_tokens is None:
															__UpperCamelCase	:   Optional[int]			   =					[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
															__UpperCamelCase	:   Union[str, 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"						)

								__UpperCamelCase	:   Union[str, Any]			   =					legacy
								__UpperCamelCase	:   Dict			   =					{} 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     ,   )

								__UpperCamelCase	:   List[str]			   =					vocab_file
								__UpperCamelCase	:   str			   =					extra_ids

								__UpperCamelCase	:   Dict			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(a						)







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







	def    _lowerCamelCase				(					self		:Dict						)		-> List[str]:
								__UpperCamelCase	:   Tuple			   =					{self.convert_ids_to_tokens(a						): i for i in range(self.vocab_size						)}
								vocab.update(self.added_tokens_encoder						)
								return vocab







	def    _lowerCamelCase				(					self		:Tuple     ,   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    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Union[str, Any]:
								return list(
								    set(filter(lambda a						: bool(re.search(r"<extra_id_\d+>"     ,   a						)						) is not None     ,   self.additional_special_tokens						)						)						)







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







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







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







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

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

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







	def    _lowerCamelCase				(					self		:Dict     ,   a		:"TextInput"     ,   **a		:int						)		-> 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:
															__UpperCamelCase	:   int			   =					SPIECE_UNDERLINE + text.replace(a     ,   " "						)
								return super().tokenize(a     ,   **a						)







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

								__UpperCamelCase	:   Optional[int]			   =					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						):
															__UpperCamelCase	:   Optional[Any]			   =					([tokens[0][1:]] if len(tokens[0]						) > 1 else []) + tokens[1:]
								return tokens







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







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







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







	def    _lowerCamelCase				(					self		:Optional[int]     ,   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
								__UpperCamelCase	:   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:
																						__UpperCamelCase	:   List[str]			   =					self.sp_model.serialized_model_proto()
																						fi.write(a						)

								return (out_vocab_file,)

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowercase  :					Optional[Any]													=       TypeVar('T')




class  lowerCamelCase__							(				Generic[T]):




	'''simple docstring'''




	_A								=     42  # Cache store of keys
	_A								=     42  # References of the keys in cache
	_A								=     1_0  # Maximum capacity of cache







	def __init__(					self		:Optional[Any]     ,   a		:int						)		-> None:
								__UpperCamelCase	:   Union[str, Any]			   =					deque()
								__UpperCamelCase	:   str			   =					set()
								if not n:
															__UpperCamelCase	:   Union[str, Any]			   =					sys.maxsize
								elif n < 0:
															raise ValueError("n should be an integer greater than 0."						)
								else:
															__UpperCamelCase	:   Any			   =					n







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:T						)		-> None:
								if x not in self.key_reference:
															if len(self.dq_store						) == LRUCache._MAX_CAPACITY:
																						__UpperCamelCase	:   int			   =					self.dq_store.pop()
																						self.key_reference.remove(a						)
								else:
															self.dq_store.remove(a						)

								self.dq_store.appendleft(a						)
								self.key_reference.add(a						)







	def    _lowerCamelCase				(					self		:Any						)		-> None:
								for k in self.dq_store:
															print(a						)







	def __repr__(					self		:Tuple						)		-> str:
								return f'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store						)}'


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowercase  :					LRUCache[str | int]													=       LRUCache(4)
		lru_cache.refer('A')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('A')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"