

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_			:					Any    			=     '▁'

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

A_			:					Union[str, Any]    			=     {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

A_			:					Dict    			=     {
    'google/pegasus-xsum': 512,
}


A_			:					List[Any]    			=     logging.get_logger(__name__)



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  List[Any]    				=			VOCAB_FILES_NAMES

					UpperCAmelCase__:  Any    				=			VOCAB_FILES_NAMES
					UpperCAmelCase__:  List[Any]    				=			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:  Dict    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:  Dict    				=			['''input_ids''', '''attention_mask''']






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

							A__				:       Optional[int]	  =						(
							    ([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(A__     )       ,			self.offset - 1     )
							]

							if len(set(A__     )     ) != len(A__     ):
								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}."""     )
							A__				:       List[str]	  =						additional_special_tokens_extended
						else:
							A__				:       Optional[int]	  =						[mask_token_sent] if mask_token_sent is not None else []
							additional_special_tokens += [F"""<unk_{i}>""" for i in range(2       ,			self.offset     )]

						A__				:       Optional[Any]	  =						{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    eos_token=A__       ,			unk_token=A__       ,			mask_token=A__       ,			pad_token=A__       ,			mask_token_sent=A__       ,			offset=A__       ,			additional_special_tokens=A__       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**A__       ,			)
						A__				:       Any	  =						mask_token_sent
						A__				:       int	  =						vocab_file
						A__				:       List[Any]	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(A__     )

						# add special tokens to encoder dict
						A__				:       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     )}     )

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






					@property
					def     __A     (					self     ):
						return len(self.sp_model     ) + self.offset






					def     __A     (					self     ):
						A__				:       Tuple	  =						{self.convert_ids_to_tokens(A__     ): i for i in range(self.vocab_size     )}
						vocab.update(self.added_tokens_encoder     )
						return vocab






					def __getstate__(					self     ):
						A__				:       Any	  =						self.__dict__.copy()
						A__				:       Union[str, Any]	  =						None
						return state






					def __setstate__(					self       ,			A__     ):
						A__				:       Union[str, Any]	  =						d

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

						A__				:       Union[str, Any]	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(self.vocab_file     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.encode(A__       ,			out_type=A__     )






					def     __A     (					self       ,			A__     ):
						if token in self.decoder:
							return self.decoder[token]
						elif token in self.added_tokens_decoder:
							return self.added_tokens_decoder[token]
						A__				:       str	  =						self.sp_model.piece_to_id(A__     )
						return sp_id + self.offset






					def     __A     (					self       ,			A__     ):
						if index in self.encoder:
							return self.encoder[index]
						elif index in self.added_tokens_encoder:
							return self.added_tokens_encoder[index]
						else:
							A__				:       List[Any]	  =						self.sp_model.IdToPiece(index - self.offset     )
						return token






					def     __A     (					self       ,			A__     ):
						A__				:       int	  =						[]
						A__				:       Optional[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(A__     ) + token
								A__				:       Dict	  =						[]
							else:
								current_sub_tokens.append(A__     )
						out_string += self.sp_model.decode(A__     )
						return out_string.strip()






					def     __A     (					self       ,			A__=False     ):
						return 1






					def     __A     (					self       ,			A__     ):
						A__				:       List[str]	  =						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     __A     (					self       ,			A__       ,			A__ = None       ,			A__ = False     ):
						if already_has_special_tokens:
							return self._special_token_mask(A__     )
						elif token_ids_a is None:
							return self._special_token_mask(A__     ) + [1]
						else:
							return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]






					def     __A     (					self       ,			A__       ,			A__=None     ):
						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     __A     (					self       ,			A__       ,			A__ = None     ):
						if not os.path.isdir(A__     ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
							return
						A__				:       Any	  =						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:
								A__				:       str	  =						self.sp_model.serialized_model_proto()
								fi.write(A__     )

						return (out_vocab_file,)






A_			:					Union[str, Any]    			=     '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A_			:					str    			=     [{'type': 'code', 'content': INSTALL_CONTENT}]
A_			:					Any    			=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



