
"""simple docstring"""

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


_A						=			'▁'

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

_A						=			{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

_A						=			{
    'google/pegasus-xsum': 5_1_2,
}


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     VOCAB_FILES_NAMES

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




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_="<pad>"		,			UpperCAmelCase_="</s>"		,			UpperCAmelCase_="<unk>"		,			UpperCAmelCase_="<mask_2>"		,			UpperCAmelCase_="<mask_1>"		,			UpperCAmelCase_=None		,			UpperCAmelCase_=103		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							None:
			lowerCamelCase					:      Union[str, Any]          =   offset
			if additional_special_tokens is not None:
					if not isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
							raise TypeError(
							    F"""additional_special_tokens should be of type {type(UpperCAmelCase_ )}, but is"""
							    F""" {type(UpperCAmelCase_ )}""" )

					lowerCamelCase					:      Tuple          =   (
					    ([mask_token_sent] + additional_special_tokens)
					    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
					    else additional_special_tokens
					)
					# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
					additional_special_tokens_extended += [
					    F"""<unk_{i}>""" for i in range(len(UpperCAmelCase_ )		,			self.offset - 1 )
					]

					if len(set(UpperCAmelCase_ ) ) != len(UpperCAmelCase_ ):
							raise ValueError(
							    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
							    F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
					lowerCamelCase					:      Optional[Any]          =   additional_special_tokens_extended
			else:
					lowerCamelCase					:      Union[str, Any]          =   [mask_token_sent] if mask_token_sent is not None else []
					additional_special_tokens += [F"""<unk_{i}>""" for i in range(2		,			self.offset )]

			lowerCamelCase					:      int          =   {} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    eos_token=UpperCAmelCase_		,			unk_token=UpperCAmelCase_		,			mask_token=UpperCAmelCase_		,			pad_token=UpperCAmelCase_		,			mask_token_sent=UpperCAmelCase_		,			offset=UpperCAmelCase_		,			additional_special_tokens=UpperCAmelCase_		,			sp_model_kwargs=self.sp_model_kwargs		,			**UpperCAmelCase_		,			)
			lowerCamelCase					:      str          =   mask_token_sent
			lowerCamelCase					:      Tuple          =   vocab_file
			lowerCamelCase					:      Dict          =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
			self.sp_model.Load(UpperCAmelCase_ )

			# add special tokens to encoder dict
			lowerCamelCase					:      Dict[int, str]          =   {
			    0: self.pad_token,
			    1: self.eos_token,
			}

			if self.mask_token_sent is not None:
					self.encoder.update(
					    {
					        2: self.mask_token_sent,
					        3: self.mask_token,
					    } )

			if self.offset > 0:
					# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
					# mask_token_sent is already added to list -> so start at 1
					self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1		,			self.offset - 1 )} )

			lowerCamelCase					:      Dict[str, int]          =   {v: k for k, v in self.encoder.items()}




	@property
	def       _UpperCamelCase   (			self )		->							int:
			return len(self.sp_model ) + self.offset




	def       _UpperCamelCase   (			self )		->							Dict[str, int]:
			lowerCamelCase					:      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 )		->							Tuple:
			lowerCamelCase					:      List[str]          =   self.__dict__.copy()
			lowerCamelCase					:      Optional[Any]          =   None
			return state




	def __setstate__(			self		,			UpperCAmelCase_ )		->							int:
			lowerCamelCase					:      Union[str, Any]          =   d

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

			lowerCamelCase					:      Optional[int]          =   spm.SentencePieceProcessor(**self.sp_model_kwargs )
			self.sp_model.Load(self.vocab_file )




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




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							int:
			if token in self.decoder:
					return self.decoder[token]
			elif token in self.added_tokens_decoder:
					return self.added_tokens_decoder[token]
			lowerCamelCase					:      Dict          =   self.sp_model.piece_to_id(UpperCAmelCase_ )
			return sp_id + self.offset




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							str:
			if index in self.encoder:
					return self.encoder[index]
			elif index in self.added_tokens_encoder:
					return self.added_tokens_encoder[index]
			else:
					lowerCamelCase					:      str          =   self.sp_model.IdToPiece(index - self.offset )
			return token




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
			lowerCamelCase					:      int          =   []
			lowerCamelCase					:      int          =   ''
			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
							lowerCamelCase					:      List[Any]          =   []
					else:
							current_sub_tokens.append(UpperCAmelCase_ )
			out_string += self.sp_model.decode(UpperCAmelCase_ )
			return out_string.strip()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=False )		->							Optional[Any]:
			return 1




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Any:
			lowerCamelCase					:      List[Any]          =   set(self.all_special_ids )  # call it once instead of inside list comp
			all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

			return [1 if x in all_special_ids else 0 for x in seq]




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = False )		->							List[int]:
			if already_has_special_tokens:
					return self._special_token_mask(UpperCAmelCase_ )
			elif token_ids_a is None:
					return self._special_token_mask(UpperCAmelCase_ ) + [1]
			else:
					return self._special_token_mask(token_ids_a + token_ids_a ) + [1]




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							List[int]:
			if token_ids_a is None:
					return token_ids_a + [self.eos_token_id]
			# We don't expect to process pairs, but leave the pair logic for API consistency
			return token_ids_a + token_ids_a + [self.eos_token_id]




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = None )		->							Tuple[str]:
			if not os.path.isdir(UpperCAmelCase_ ):
					logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
					return
			lowerCamelCase					:      List[Any]          =   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:
							lowerCamelCase					:      Any          =   self.sp_model.serialized_model_proto()
							fi.write(UpperCAmelCase_ )

			return (out_vocab_file,)








"""simple docstring"""

import copy

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


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'encoder-decoder'
	lowercase_				=     True




	def __init__(			self		,			**UpperCAmelCase_ )		->							str:
			super().__init__(**UpperCAmelCase_ )
			assert (
			    "encoder" in kwargs and "decoder" in kwargs
			), "Config has to be initialized with encoder and decoder config"
			lowerCamelCase					:      List[Any]          =   kwargs.pop('encoder' )
			lowerCamelCase					:      Optional[int]          =   encoder_config.pop('model_type' )
			lowerCamelCase					:      str          =   kwargs.pop('decoder' )
			lowerCamelCase					:      Dict          =   decoder_config.pop('model_type' )

			from ..auto.configuration_auto import AutoConfig

			lowerCamelCase					:      int          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   True




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							PretrainedConfig:
			logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
			lowerCamelCase					:      str          =   True
			lowerCamelCase					:      Optional[Any]          =   True

			return cls(encoder=encoder_config.to_dict()		,			decoder=decoder_config.to_dict()		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   copy.deepcopy(self.__dict__ )
			lowerCamelCase					:      Union[str, Any]          =   self.encoder.to_dict()
			lowerCamelCase					:      List[Any]          =   self.decoder.to_dict()
			lowerCamelCase					:      Tuple          =   self.__class__.model_type
			return output






