

"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase   :	List[str]												=						logging.get_logger(__name__)

_lowercase   :	Any												=						{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Union[str, Any]					=					"pegasus"
	a__		: Optional[int]					=					["past_key_values"]
	a__		: str					=					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



	def __init__(  self     :       Optional[int]			,    _lowercase     :       Tuple=5_02_65			,    _lowercase     :       Dict=10_24			,    _lowercase     :       int=12			,    _lowercase     :       Any=40_96			,    _lowercase     :       Tuple=16			,    _lowercase     :       List[str]=12			,    _lowercase     :       List[Any]=40_96			,    _lowercase     :       List[str]=16			,    _lowercase     :       Any=0.0			,    _lowercase     :       int=0.0			,    _lowercase     :       str=True			,    _lowercase     :       Union[str, Any]=True			,    _lowercase     :       Tuple="gelu"			,    _lowercase     :       Any=10_24			,    _lowercase     :       Optional[int]=0.1			,    _lowercase     :       Tuple=0.0			,    _lowercase     :       Tuple=0.0			,    _lowercase     :       Union[str, Any]=0.02			,    _lowercase     :       Tuple=0			,    _lowercase     :       str=False			,    _lowercase     :       Tuple=0			,    _lowercase     :       Any=1			,    _lowercase     :       List[Any]=1			,    **_lowercase     :       Dict			,    ):
		__UpperCAmelCase      		= vocab_size
		__UpperCAmelCase      		= max_position_embeddings
		__UpperCAmelCase      		= d_model
		__UpperCAmelCase      		= encoder_ffn_dim
		__UpperCAmelCase      		= encoder_layers
		__UpperCAmelCase      		= encoder_attention_heads
		__UpperCAmelCase      		= decoder_ffn_dim
		__UpperCAmelCase      		= decoder_layers
		__UpperCAmelCase      		= decoder_attention_heads
		__UpperCAmelCase      		= dropout
		__UpperCAmelCase      		= attention_dropout
		__UpperCAmelCase      		= activation_dropout
		__UpperCAmelCase      		= activation_function
		__UpperCAmelCase      		= init_std
		__UpperCAmelCase      		= encoder_layerdrop
		__UpperCAmelCase      		= decoder_layerdrop
		__UpperCAmelCase      		= use_cache
		__UpperCAmelCase      		= encoder_layers
		__UpperCAmelCase      		= scale_embedding  # scale factor will be sqrt(d_model) if True
		super().__init__(
		    pad_token_id=_lowercase			,    eos_token_id=_lowercase			,    is_encoder_decoder=_lowercase			,    decoder_start_token_id=_lowercase			,    forced_eos_token_id=_lowercase			,    **_lowercase			,    )



	@property
	def        a  (  self     :       Any						):
		return self.encoder_attention_heads



	@property
	def        a  (  self     :       List[Any]						):
		return self.d_model

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	"▁"

SCREAMING_SNAKE_CASE		:     Dict         =	{"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE		:     List[Any]         =	{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

SCREAMING_SNAKE_CASE		:     Any         =	{
    "google/reformer-crime-and-punishment": 524288,
}

class        UpperCamelCase						(				__a				):
						a__		:Optional[Any]      =			VOCAB_FILES_NAMES
						a__		:List[Any]      =			PRETRAINED_VOCAB_FILES_MAP
						a__		:Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__		:List[Any]      =			['''input_ids''', '''attention_mask''']





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase="</s>"			,     __UpperCamelCase="<unk>"			,     __UpperCamelCase=[]			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				None:
												UpperCamelCase_		:							List[str]  =		{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=__UpperCamelCase			,     unk_token=__UpperCamelCase			,     additional_special_tokens=__UpperCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **__UpperCamelCase			,     )

												UpperCamelCase_		:							int  =		vocab_file
												UpperCamelCase_		:							str  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Dict:
												return self.sp_model.get_piece_size()





						def        A_							(self						)				->				Dict[str, int]:
												UpperCamelCase_		:							str  =		{self.convert_ids_to_tokens(__UpperCamelCase						): i for i in range(self.vocab_size						)}
												vocab.update(self.added_tokens_encoder						)
												return vocab





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





						def __setstate__(self			,     __UpperCamelCase						)				->				List[Any]:
												UpperCamelCase_		:							List[Any]  =		d

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

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





						def        A_							(self			,     __UpperCamelCase						)				->				List[str]:
												return self.sp_model.encode(__UpperCamelCase			,     out_type=__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Union[str, Any]:
												return self.sp_model.piece_to_id(__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Dict:
												if index < self.sp_model.get_piece_size():
																		UpperCamelCase_		:							Union[str, Any]  =		self.sp_model.IdToPiece(__UpperCamelCase						)
												return token





						def        A_							(self			,     __UpperCamelCase						)				->				int:
												UpperCamelCase_		:							Dict  =		[]
												UpperCamelCase_		:							Union[str, Any]  =		""""""
												for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																								out_string += self.sp_model.decode(__UpperCamelCase						) + token
																								UpperCamelCase_		:							Optional[Any]  =		[]
																		else:
																								current_sub_tokens.append(__UpperCamelCase						)
												out_string += self.sp_model.decode(__UpperCamelCase						)
												return out_string.strip()





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple[str]:
												if not os.path.isdir(__UpperCamelCase						):
																		logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
																		return
												UpperCamelCase_		:							Tuple  =		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:
																								UpperCamelCase_		:							int  =		self.sp_model.serialized_model_proto()
																								fi.write(__UpperCamelCase						)

												return (out_vocab_file,)






