


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 	_a							(      UpperCamelCase__      ):
							_lowercase     :      Optional[int]  				=			VOCAB_FILES_NAMES
							_lowercase     :      Optional[int]  				=			PRETRAINED_VOCAB_FILES_MAP
							_lowercase     :      Optional[int]  				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowercase     :      Optional[Any]  				=			['''input_ids''', '''attention_mask''']

							def __init__(							self:     Optional[Any]   ,						UpperCamelCase_:     List[Any]   ,						UpperCamelCase_:     Any="</s>"   ,						UpperCamelCase_:     Union[str, Any]="<unk>"   ,						UpperCamelCase_:     List[Any]="<pad>"   ,						UpperCamelCase_:     Optional[int]=100   ,						UpperCamelCase_:     Dict=None   ,						UpperCamelCase_:     Optional[Dict[str, Any]] = None   ,						UpperCamelCase_:     List[str]=True   ,						**UpperCamelCase_:     int   ,						)      ->       None:





									"""simple docstring"""







									if extra_ids > 0 and additional_special_tokens is None:
											lowercase__						=				[f'<extra_id_{i}>' for i in range(UpperCamelCase_							)]
									elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra_id special tokens
											lowercase__						=				len(set(filter(lambda UpperCamelCase_							: bool('''extra_id''' in str(UpperCamelCase_							)							)   ,						UpperCamelCase_							)							)							)
											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'''							)

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

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

									lowercase__						=				vocab_file
									lowercase__						=				extra_ids

									lowercase__						=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(UpperCamelCase_							)

							@staticmethod
							def 				lowerCamelCase_			(							UpperCamelCase_:     int   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Dict							)      ->       Tuple:





									"""simple docstring"""







									if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
											lowercase__						=				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.'''   ,						UpperCamelCase_   ,						)

									return max_model_length

							@property
							def 				lowerCamelCase_			(							self:     Tuple							)      ->       Any:





									"""simple docstring"""







									return self.sp_model.get_piece_size() + self._extra_ids

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				{self.convert_ids_to_tokens(UpperCamelCase_							): i for i in range(self.vocab_size							)}
									vocab.update(self.added_tokens_encoder							)
									return vocab

							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     List[int]   ,						UpperCamelCase_:     Optional[List[int]] = None   ,						UpperCamelCase_:     bool = False							)      ->       List[int]:





									"""simple docstring"""







									if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=UpperCamelCase_   ,						token_ids_a=UpperCamelCase_   ,						already_has_special_tokens=UpperCamelCase_							)

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

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Tuple:





									"""simple docstring"""







									return list(
									    set(filter(lambda UpperCamelCase_							: bool(re.search(r'''<extra_id_\d+>'''   ,						UpperCamelCase_							)							) is not None   ,						self.additional_special_tokens							)							)							)

							def 				lowerCamelCase_			(							self:     str							)      ->       int:





									"""simple docstring"""







									return [self._convert_token_to_id(UpperCamelCase_							) for token in self.get_sentinel_tokens()]

							def 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     List[int]							)      ->       List[int]:





									"""simple docstring"""







									if len(UpperCamelCase_							) > 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:     Tuple   ,						UpperCamelCase_:     List[int]   ,						UpperCamelCase_:     Optional[List[int]] = None							)      ->       List[int]:





									"""simple docstring"""







									lowercase__						=				[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:     Tuple   ,						UpperCamelCase_:     List[int]   ,						UpperCamelCase_:     Optional[List[int]] = None							)      ->       List[int]:





									"""simple docstring"""







									lowercase__						=				self._add_eos_if_not_present(UpperCamelCase_							)
									if token_ids_a is None:
											return token_ids_a
									else:
											lowercase__						=				self._add_eos_if_not_present(UpperCamelCase_							)
											return token_ids_a + token_ids_a

							def __getstate__(							self:     int							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				self.__dict__.copy()
									lowercase__						=				None
									return state

							def __setstate__(							self:     List[Any]   ,						UpperCamelCase_:     Union[str, Any]							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				d

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

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

							def 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     "TextInput"   ,						**UpperCamelCase_:     int							)      ->       List[str]:





									"""simple docstring"""







									if not self.legacy:
											lowercase__						=				SPIECE_UNDERLINE + text.replace(UpperCamelCase_   ,						''' '''							)
									return super().tokenize(UpperCamelCase_   ,						**UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     List[Any]   ,						**UpperCamelCase_:     Dict							)      ->       str:





									"""simple docstring"""







									if not self.legacy:
											lowercase__						=				text.startswith(UpperCamelCase_							)
											if is_first:
													lowercase__						=				text[1:]

									lowercase__						=				self.sp_model.encode(UpperCamelCase_   ,						out_type=UpperCamelCase_							)

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

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     Union[str, Any]							)      ->       List[Any]:





									"""simple docstring"""







									if token.startswith('''<extra_id_'''							):
											lowercase__						=				re.match(r'''<extra_id_(\d+)>'''   ,						UpperCamelCase_							)
											lowercase__						=				int(match.group(1							)							)
											return self.vocab_size - num - 1
									return self.sp_model.piece_to_id(UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     Tuple   ,						UpperCamelCase_:     Tuple							)      ->       Union[str, Any]:





									"""simple docstring"""







									if index < self.sp_model.get_piece_size():
											lowercase__						=				self.sp_model.IdToPiece(UpperCamelCase_							)
									else:
											lowercase__						=				f'<extra_id_{self.vocab_size - 1 - index}>'
									return token

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Union[str, Any]							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				[]
									lowercase__						=				''''''
									lowercase__						=				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(UpperCamelCase_							) + token
													lowercase__						=				True
													lowercase__						=				[]
											else:
													current_sub_tokens.append(UpperCamelCase_							)
													lowercase__						=				False
									out_string += self.sp_model.decode(UpperCamelCase_							)
									return out_string.strip()


							def 				lowerCamelCase_			(							self:     List[Any]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Optional[str] = None							)      ->       Tuple[str]:





									"""simple docstring"""







									if not os.path.isdir(UpperCamelCase_							):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'							)
											return
									lowercase__						=				os.path.join(
									    UpperCamelCase_   ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

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

									return (out_vocab_file,)






import logging

from transformers import PretrainedConfig


lowerCAmelCase	=							logging.getLogger(__name__)


lowerCAmelCase	=							{
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[Any]  				=			'''bertabs'''

							def __init__(							self:     List[str]   ,						UpperCamelCase_:     Dict=30_522   ,						UpperCamelCase_:     Union[str, Any]=512   ,						UpperCamelCase_:     Optional[int]=6   ,						UpperCamelCase_:     int=512   ,						UpperCamelCase_:     Optional[int]=8   ,						UpperCamelCase_:     List[Any]=512   ,						UpperCamelCase_:     Tuple=0.2   ,						UpperCamelCase_:     List[Any]=6   ,						UpperCamelCase_:     Tuple=768   ,						UpperCamelCase_:     List[Any]=8   ,						UpperCamelCase_:     Union[str, Any]=2_048   ,						UpperCamelCase_:     str=0.2   ,						**UpperCamelCase_:     Any   ,						)      ->       List[str]:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)

									lowercase__						=				vocab_size
									lowercase__						=				max_pos

									lowercase__						=				enc_layers
									lowercase__						=				enc_hidden_size
									lowercase__						=				enc_heads
									lowercase__						=				enc_ff_size
									lowercase__						=				enc_dropout

									lowercase__						=				dec_layers
									lowercase__						=				dec_hidden_size
									lowercase__						=				dec_heads
									lowercase__						=				dec_ff_size
									lowercase__						=				dec_dropout


