



'''simple docstring'''

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


__lowerCAmelCase		     =  logging.get_logger(__name__)

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

__lowerCAmelCase		     =  {
    '''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'''
        ),
    }
}

__lowerCAmelCase		     =  {
    '''google/bigbird-roberta-base''': 4_096,
    '''google/bigbird-roberta-large''': 4_096,
    '''google/bigbird-base-trivia-itc''': 4_096,
}

class       __magic_name__		(       _UpperCamelCase  ):
							lowerCAmelCase			:       Tuple  							=   VOCAB_FILES_NAMES
							lowerCAmelCase			:       Any  							=   PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase			:       Optional[int]  							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase			:       Union[str, Any]  							=   ['input_ids', 'attention_mask']
							lowerCAmelCase			:       List[int]  							=   []







							def __init__(				self   :			Optional[Any]      ,_UpperCAmelCase   :			Optional[int]      ,_UpperCAmelCase   :			Any="<unk>"      ,_UpperCAmelCase   :			Optional[int]="<s>"      ,_UpperCAmelCase   :			Optional[int]="</s>"      ,_UpperCAmelCase   :			List[Any]="<pad>"      ,_UpperCAmelCase   :			List[str]="[SEP]"      ,_UpperCAmelCase   :			List[Any]="[MASK]"      ,_UpperCAmelCase   :			List[Any]="[CLS]"      ,_UpperCAmelCase   :			Optional[Dict[str, Any]] = None      ,**_UpperCAmelCase   :			Dict      ,):
									_a	: Dict								=				AddedToken(_UpperCAmelCase      ,lstrip=_UpperCAmelCase      ,rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase      ,_UpperCAmelCase						) else bos_token
									_a	: int								=				AddedToken(_UpperCAmelCase      ,lstrip=_UpperCAmelCase      ,rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase      ,_UpperCAmelCase						) else eos_token
									_a	: str								=				AddedToken(_UpperCAmelCase      ,lstrip=_UpperCAmelCase      ,rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase      ,_UpperCAmelCase						) else unk_token
									_a	: List[Any]								=				AddedToken(_UpperCAmelCase      ,lstrip=_UpperCAmelCase      ,rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase      ,_UpperCAmelCase						) else pad_token
									_a	: Tuple								=				AddedToken(_UpperCAmelCase      ,lstrip=_UpperCAmelCase      ,rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase      ,_UpperCAmelCase						) else cls_token
									_a	: Tuple								=				AddedToken(_UpperCAmelCase      ,lstrip=_UpperCAmelCase      ,rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase      ,_UpperCAmelCase						) else sep_token

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

									_a	: Dict								=				{} if sp_model_kwargs is None else sp_model_kwargs

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

									_a	: Dict								=				vocab_file

									_a	: Any								=				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
									self.sp_model.Load(_UpperCAmelCase						)







							@property
							def 				__lowercase     (				self   :			List[Any]						):
									return self.sp_model.get_piece_size()







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: 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						):
									_a	: Optional[int]								=				self.__dict__.copy()
									_a	: List[Any]								=				None
									return state







							def __setstate__(				self   :			Any      ,_UpperCAmelCase   :			Union[str, Any]						):
									_a	: List[Any]								=				d

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

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







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			str						):
									return self.sp_model.encode(_UpperCAmelCase      ,out_type=_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Dict      ,_UpperCAmelCase   :			Tuple						):
									return self.sp_model.piece_to_id(_UpperCAmelCase						)







							def 				__lowercase     (				self   :			List[str]      ,_UpperCAmelCase   :			List[Any]						):
									_a	: str								=				self.sp_model.IdToPiece(_UpperCAmelCase						)
									return token







							def 				__lowercase     (				self   :			List[str]      ,_UpperCAmelCase   :			Optional[int]						):
									_a	: Optional[Any]								=				[]
									_a	: Tuple								=				''
									_a	: str								=				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
													_a	: Tuple								=				True
													_a	: Any								=				[]
											else:
													current_sub_tokens.append(_UpperCAmelCase						)
													_a	: Union[str, Any]								=				False
									out_string += self.sp_model.decode(_UpperCAmelCase						)
									return out_string.strip()







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			List[int]      ,_UpperCAmelCase   :			bool = False      ,_UpperCAmelCase   :			bool = None      ,_UpperCAmelCase   :			bool = True      ,**_UpperCAmelCase   :			Optional[int]      ,):
									_a	: Optional[Any]								=				kwargs.pop('use_source_tokenizer'      ,_UpperCAmelCase						)

									_a	: Optional[Any]								=				self.convert_ids_to_tokens(_UpperCAmelCase      ,skip_special_tokens=_UpperCAmelCase						)

									# 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
									_a	: str								=				[]
									_a	: int								=				[]
									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(_UpperCAmelCase						)						)
															_a	: Optional[int]								=				[]
													sub_texts.append(_UpperCAmelCase						)
											else:
													current_sub_text.append(_UpperCAmelCase						)
									if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(_UpperCAmelCase						)						)

									# Mimic the behavior of the Rust tokenizer:
									# No space before [MASK] and [SEP]
									if spaces_between_special_tokens:
											_a	: Union[str, Any]								=				re.sub(R' (\[(MASK|SEP)\])'      ,R'\1'      ,' '.join(_UpperCAmelCase						)						)
									else:
											_a	: List[str]								=				''.join(_UpperCAmelCase						)

									_a	: Union[str, Any]								=				(
									    clean_up_tokenization_spaces
									    if clean_up_tokenization_spaces is not None
									    else self.clean_up_tokenization_spaces
									)
									if clean_up_tokenization_spaces:
											_a	: List[Any]								=				self.clean_up_tokenization(_UpperCAmelCase						)
											return clean_text
									else:
											return text







							def 				__lowercase     (				self   :			Any      ,_UpperCAmelCase   :			str      ,_UpperCAmelCase   :			Optional[str] = None						):
									if not os.path.isdir(_UpperCAmelCase						):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
											return
									_a	: int								=				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:
													_a	: Optional[Any]								=				self.sp_model.serialized_model_proto()
													fi.write(_UpperCAmelCase						)

									return (out_vocab_file,)







							def 				__lowercase     (				self   :			Tuple      ,_UpperCAmelCase   :			List[int]      ,_UpperCAmelCase   :			Optional[List[int]] = None						):
									if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_a	: List[str]								=				[self.cls_token_id]
									_a	: Union[str, Any]								=				[self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep







							def 				__lowercase     (				self   :			Optional[int]      ,_UpperCAmelCase   :			List[int]      ,_UpperCAmelCase   :			Optional[List[int]] = None      ,_UpperCAmelCase   :			bool = False						):
									if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=_UpperCAmelCase      ,token_ids_a=_UpperCAmelCase      ,already_has_special_tokens=_UpperCAmelCase						)

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







							def 				__lowercase     (				self   :			Tuple      ,_UpperCAmelCase   :			List[int]      ,_UpperCAmelCase   :			Optional[List[int]] = None						):
									_a	: List[Any]								=				[self.sep_token_id]
									_a	: List[Any]								=				[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'''

import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


__lowerCAmelCase		     =  logging.get_logger(__name__)

class       __magic_name__		(       _UpperCamelCase  ):







							def __init__(				self   :			Tuple      ,*_UpperCAmelCase   :			str      ,**_UpperCAmelCase   :			int						):
									warnings.warn(
									    'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use DeiTImageProcessor instead.'      ,_UpperCAmelCase      ,)
									super().__init__(*_UpperCAmelCase      ,**_UpperCAmelCase						)




