






"""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


lowerCAmelCase__    						=			logging.get_logger(__name__)

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

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

lowerCAmelCase__    						=			'▁'


class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Tuple 					=				VOCAB_FILES_NAMES
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				PRETRAINED_VOCAB_FILES_MAP
						SCREAMING_SNAKE_CASE_:					Optional[int] 					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						SCREAMING_SNAKE_CASE_:					Dict 					=				['input_ids', 'attention_mask']







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_="</s>"   ,				lowerCAmelCase_="<unk>"   ,				lowerCAmelCase_="<pad>"   ,				lowerCAmelCase_=1_0_0   ,				lowerCAmelCase_=None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_=True   ,				**lowerCAmelCase_   ,				)		->							None:
										# Add extra_ids to the special token list
										if extra_ids > 0 and additional_special_tokens is None:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    [F"""<extra_id_{i}>""" for i in range(lowerCAmelCase_							)]
										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   :		str       		=    len(set(filter(lambda lowerCAmelCase_							: bool('extra_id' in str(lowerCAmelCase_							)							)   ,				lowerCAmelCase_							)							)							)
														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   :		Union[str, Any]       		=    legacy
										_SCREAMING_SNAKE_CASE   :		Any       		=    {} if sp_model_kwargs is None else sp_model_kwargs

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

										_SCREAMING_SNAKE_CASE   :		int       		=    vocab_file
										_SCREAMING_SNAKE_CASE   :		Any       		=    extra_ids

										_SCREAMING_SNAKE_CASE   :		int       		=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(lowerCAmelCase_							)







						@staticmethod
						def 			A      (    lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							List[Any]:
										if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
														_SCREAMING_SNAKE_CASE   :		Union[str, 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.'   ,				lowerCAmelCase_   ,				)

										return max_model_length







						@property
						def 			A      (    self							)		->							int:
										return self.sp_model.get_piece_size() + self._extra_ids







						def 			A      (    self							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		int       		=    {self.convert_ids_to_tokens(lowerCAmelCase_							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab







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

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







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







						def 			A      (    self							)		->							Tuple:
										return [self._convert_token_to_id(lowerCAmelCase_							) for token in self.get_sentinel_tokens()]







						def 			A      (    self   ,				lowerCAmelCase_							)		->							List[int]:
										if len(lowerCAmelCase_							) > 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   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None							)		->							List[int]:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    [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   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = None							)		->							List[int]:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self._add_eos_if_not_present(lowerCAmelCase_							)
										if token_ids_a is None:
														return token_ids_a
										else:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self._add_eos_if_not_present(lowerCAmelCase_							)
														return token_ids_a + token_ids_a







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







						def __setstate__(    self   ,				lowerCAmelCase_							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    d

										# for backward compatibility
										if not hasattr(self   ,				'sp_model_kwargs'							):
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    {}

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(self.vocab_file							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							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   :		Optional[int]       		=    SPIECE_UNDERLINE + text.replace(lowerCAmelCase_   ,				' '							)
										return super().tokenize(lowerCAmelCase_   ,				**lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							str:
										if not self.legacy:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    text.startswith(lowerCAmelCase_							)
														if is_first:
																		_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    text[1:]

										_SCREAMING_SNAKE_CASE   :		int       		=    self.sp_model.encode(lowerCAmelCase_   ,				out_type=lowerCAmelCase_							)

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







						def 			A      (    self   ,				lowerCAmelCase_							)		->							str:
										if token.startswith('<extra_id_'							):
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    re.match(r'<extra_id_(\d+)>'   ,				lowerCAmelCase_							)
														_SCREAMING_SNAKE_CASE   :		Dict       		=    int(match.group(1							)							)
														return self.vocab_size - num - 1
										return self.sp_model.piece_to_id(lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							List[str]:
										if index < self.sp_model.get_piece_size():
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.sp_model.IdToPiece(lowerCAmelCase_							)
										else:
														_SCREAMING_SNAKE_CASE   :		int       		=    F"""<extra_id_{self.vocab_size - 1 - index}>"""
										return token







						def 			A      (    self   ,				lowerCAmelCase_							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    []
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    ''
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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(lowerCAmelCase_							) + token
																		_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    True
																		_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    []
														else:
																		current_sub_tokens.append(lowerCAmelCase_							)
																		_SCREAMING_SNAKE_CASE   :		Tuple       		=    False
										out_string += self.sp_model.decode(lowerCAmelCase_							)
										return out_string.strip()







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

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

										return (out_vocab_file,)









"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

