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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase		:  Optional[Any]     					=       logging.get_logger(__name__)

lowercase		:  List[str]     					=       {"""vocab_file""": """spiece.model"""}

lowercase		:  List[str]     					=       {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

lowercase		:  List[Any]     					=       {
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}






class 					a__     ( __a ):
				_A       					=							VOCAB_FILES_NAMES
				_A       					=							PRETRAINED_VOCAB_FILES_MAP
				_A       					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_A       					=							["""input_ids""", """attention_mask"""]
				_A       					=							[]






				def __init__(    self       :						List[str]  ,							A_       :						Tuple  ,							A_       :						Dict="<unk>"  ,							A_       :						List[str]="<s>"  ,							A_       :						Optional[int]="</s>"  ,							A_       :						List[Any]="<pad>"  ,							A_       :						str="[SEP]"  ,							A_       :						Any="[MASK]"  ,							A_       :						Optional[Any]="[CLS]"  ,							A_       :						Union[str, Any] = None  ,							**A_       :						Dict  ,							)       ->      None:

										"""simple docstring"""
										lowerCamelCase_:       Tuple					     = AddedToken(_lowerCamelCase  ,							lstrip=_lowerCamelCase  ,							rstrip=_lowerCamelCase							) if isinstance(_lowerCamelCase  ,							_lowerCamelCase							) else bos_token
										lowerCamelCase_:       Any					     = AddedToken(_lowerCamelCase  ,							lstrip=_lowerCamelCase  ,							rstrip=_lowerCamelCase							) if isinstance(_lowerCamelCase  ,							_lowerCamelCase							) else eos_token
										lowerCamelCase_:       List[str]					     = AddedToken(_lowerCamelCase  ,							lstrip=_lowerCamelCase  ,							rstrip=_lowerCamelCase							) if isinstance(_lowerCamelCase  ,							_lowerCamelCase							) else unk_token
										lowerCamelCase_:       Any					     = AddedToken(_lowerCamelCase  ,							lstrip=_lowerCamelCase  ,							rstrip=_lowerCamelCase							) if isinstance(_lowerCamelCase  ,							_lowerCamelCase							) else pad_token
										lowerCamelCase_:       str					     = AddedToken(_lowerCamelCase  ,							lstrip=_lowerCamelCase  ,							rstrip=_lowerCamelCase							) if isinstance(_lowerCamelCase  ,							_lowerCamelCase							) else cls_token
										lowerCamelCase_:       int					     = AddedToken(_lowerCamelCase  ,							lstrip=_lowerCamelCase  ,							rstrip=_lowerCamelCase							) if isinstance(_lowerCamelCase  ,							_lowerCamelCase							) else sep_token

										# Mask token behave like a normal word, i.e. include the space before it
										lowerCamelCase_:       List[Any]					     = AddedToken(_lowerCamelCase  ,							lstrip=_lowerCamelCase  ,							rstrip=_lowerCamelCase							) if isinstance(_lowerCamelCase  ,							_lowerCamelCase							) else mask_token

										lowerCamelCase_:       Optional[int]					     = {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=_lowerCamelCase  ,							eos_token=_lowerCamelCase  ,							unk_token=_lowerCamelCase  ,							pad_token=_lowerCamelCase  ,							sep_token=_lowerCamelCase  ,							mask_token=_lowerCamelCase  ,							cls_token=_lowerCamelCase  ,							sp_model_kwargs=self.sp_model_kwargs  ,							**_lowerCamelCase  ,							)

										lowerCamelCase_:       Dict					     = vocab_file

										lowerCamelCase_:       List[str]					     = spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(_lowerCamelCase							)






				@property
				def   lowerCAmelCase (    self       :						Dict							)       ->      Any:

										"""simple docstring"""
										return self.sp_model.get_piece_size()






				def   lowerCAmelCase (    self       :						Any							)       ->      List[Any]:

										"""simple docstring"""
										lowerCamelCase_:       int					     = {self.convert_ids_to_tokens(_lowerCamelCase							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab






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

										"""simple docstring"""
										lowerCamelCase_:       Tuple					     = self.__dict__.copy()
										lowerCamelCase_:       str					     = None
										return state






				def __setstate__(    self       :						str  ,							A_       :						Any							)       ->      str:

										"""simple docstring"""
										lowerCamelCase_:       List[Any]					     = d

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

										lowerCamelCase_:       List[str]					     = spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(self.vocab_file							)






				def   lowerCAmelCase (    self       :						Dict  ,							A_       :						Dict							)       ->      List[str]:

										"""simple docstring"""
										return self.sp_model.encode(_lowerCamelCase  ,							out_type=_lowerCamelCase							)






				def   lowerCAmelCase (    self       :						Tuple  ,							A_       :						Union[str, Any]							)       ->      Any:

										"""simple docstring"""
										return self.sp_model.piece_to_id(_lowerCamelCase							)






				def   lowerCAmelCase (    self       :						Union[str, Any]  ,							A_       :						Optional[Any]							)       ->      Dict:

										"""simple docstring"""
										lowerCamelCase_:       Tuple					     = self.sp_model.IdToPiece(_lowerCamelCase							)
										return token






				def   lowerCAmelCase (    self       :						List[Any]  ,							A_       :						Optional[Any]							)       ->      str:

										"""simple docstring"""
										lowerCamelCase_:       List[str]					     = []
										lowerCamelCase_:       List[str]					     = """"""
										lowerCamelCase_:       Union[str, 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
																						lowerCamelCase_:       Union[str, Any]					     = True
																						lowerCamelCase_:       Optional[Any]					     = []
																else:
																						current_sub_tokens.append(_lowerCamelCase							)
																						lowerCamelCase_:       str					     = False
										out_string += self.sp_model.decode(_lowerCamelCase							)
										return out_string.strip()






				def   lowerCAmelCase (    self       :						Any  ,							A_       :						List[str]  ,							A_       :						Any = False  ,							A_       :						Union[str, Any] = None  ,							A_       :						Dict = True  ,							**A_       :						Dict  ,							)       ->      str:

										"""simple docstring"""
										lowerCamelCase_:       Union[str, Any]					     = kwargs.pop("""use_source_tokenizer"""  ,							_lowerCamelCase							)

										lowerCamelCase_:       Union[str, Any]					     = self.convert_ids_to_tokens(_lowerCamelCase  ,							skip_special_tokens=_lowerCamelCase							)

										# To avoid mixing byte-level and unicode for byte-level BPT
										# we need to build string separately for added tokens and byte-level tokens
										# cf. https://github.com/huggingface/transformers/issues/1133
										lowerCamelCase_:       List[str]					     = []
										lowerCamelCase_:       Union[str, Any]					     = []
										for token in filtered_tokens:
																if skip_special_tokens and token in self.all_special_ids:
																						continue
																if token in self.added_tokens_encoder:
																						if current_sub_text:
																												sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase							)							)
																												lowerCamelCase_:       Optional[int]					     = []
																						sub_texts.append(_lowerCamelCase							)
																else:
																						current_sub_text.append(_lowerCamelCase							)
										if current_sub_text:
																sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase							)							)

										# Mimic the behavior of the Rust tokenizer:
										# No space before [MASK] and [SEP]
										if spaces_between_special_tokens:
																lowerCamelCase_:       str					     = re.sub(r""" (\[(MASK|SEP)\])"""  ,							r"""\1"""  ,							""" """.join(_lowerCamelCase							)							)
										else:
																lowerCamelCase_:       Tuple					     = """""".join(_lowerCamelCase							)

										lowerCamelCase_:       Optional[int]					     = (
										    clean_up_tokenization_spaces
										    if clean_up_tokenization_spaces is not None
										    else self.clean_up_tokenization_spaces
										)
										if clean_up_tokenization_spaces:
																lowerCamelCase_:       int					     = self.clean_up_tokenization(_lowerCamelCase							)
																return clean_text
										else:
																return text






				def   lowerCAmelCase (    self       :						Dict  ,							A_       :						Union[str, Any]  ,							A_       :						str = None							)       ->      Tuple[str]:

										"""simple docstring"""
										if not os.path.isdir(_lowerCamelCase							):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
																return
										lowerCamelCase_:       int					     = 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:
																						lowerCamelCase_:       Union[str, Any]					     = self.sp_model.serialized_model_proto()
																						fi.write(_lowerCamelCase							)

										return (out_vocab_file,)






				def   lowerCAmelCase (    self       :						Union[str, Any]  ,							A_       :						Optional[Any]  ,							A_       :						Optional[Any] = None							)       ->      List[int]:

										"""simple docstring"""
										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										lowerCamelCase_:       Optional[int]					     = [self.cls_token_id]
										lowerCamelCase_:       List[Any]					     = [self.sep_token_id]
										return cls + token_ids_a + sep + token_ids_a + sep






				def   lowerCAmelCase (    self       :						Tuple  ,							A_       :						Any  ,							A_       :						int = None  ,							A_       :						int = False							)       ->      List[int]:

										"""simple docstring"""
										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_lowerCamelCase  ,							token_ids_a=_lowerCamelCase  ,							already_has_special_tokens=_lowerCamelCase							)

										if token_ids_a is None:
																return [1] + ([0] * len(_lowerCamelCase							)) + [1]
										return [1] + ([0] * len(_lowerCamelCase							)) + [1] + ([0] * len(_lowerCamelCase							)) + [1]






				def   lowerCAmelCase (    self       :						Any  ,							A_       :						Union[str, Any]  ,							A_       :						Any = None							)       ->      List[int]:

										"""simple docstring"""
										lowerCamelCase_:       List[Any]					     = [self.sep_token_id]
										lowerCamelCase_:       Optional[int]					     = [self.cls_token_id]
										if token_ids_a is None:
																return len(cls + token_ids_a + sep							) * [0]
										return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]






"""simple docstring"""






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

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





