




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


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

UpperCamelCase__ :   Optional[int]						=							{'''vocab_file''': '''spiece.model'''}

UpperCamelCase__ :   Any						=							{
    '''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'''
        ),
    }
}

UpperCamelCase__ :   List[str]						=							{
    '''google/bigbird-roberta-base''': 40_96,
    '''google/bigbird-roberta-large''': 40_96,
    '''google/bigbird-base-trivia-itc''': 40_96,
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       Optional[int]						=     VOCAB_FILES_NAMES
				__a     :       Dict						=     PRETRAINED_VOCAB_FILES_MAP
				__a     :       Dict						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a     :       str						=     ["input_ids", "attention_mask"]
				__a     :       List[int]						=     []






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

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

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      {} if sp_model_kwargs is None else sp_model_kwargs

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

							SCREAMING_SNAKE_CASE_   : List[str]					  =      vocab_file

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(snake_case__ )






				@property
				def 		snake_case	(			self ):
							return self.sp_model.get_piece_size()






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      {self.convert_ids_to_tokens(snake_case__ ): i for i in range(self.vocab_size )}
							vocab.update(self.added_tokens_encoder )
							return vocab






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






				def __setstate__(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      d

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

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






				def 		snake_case	(			self      ,snake_case__ ):
							return self.sp_model.encode(snake_case__      ,out_type=snake_case__ )






				def 		snake_case	(			self      ,snake_case__ ):
							return self.sp_model.piece_to_id(snake_case__ )






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.sp_model.IdToPiece(snake_case__ )
							return token






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : str					  =      []
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      ''
							SCREAMING_SNAKE_CASE_   : int					  =      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(snake_case__ ) + token
													SCREAMING_SNAKE_CASE_   : int					  =      True
													SCREAMING_SNAKE_CASE_   : int					  =      []
										else:
													current_sub_tokens.append(snake_case__ )
													SCREAMING_SNAKE_CASE_   : List[str]					  =      False
							out_string += self.sp_model.decode(snake_case__ )
							return out_string.strip()






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = False      ,snake_case__ = None      ,snake_case__ = True      ,**snake_case__      ,):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      kwargs.pop('use_source_tokenizer'      ,snake_case__ )

							SCREAMING_SNAKE_CASE_   : Dict					  =      self.convert_ids_to_tokens(snake_case__      ,skip_special_tokens=snake_case__ )

							# 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
							SCREAMING_SNAKE_CASE_   : str					  =      []
							SCREAMING_SNAKE_CASE_   : Optional[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(snake_case__ ) )
																SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      []
													sub_texts.append(snake_case__ )
										else:
													current_sub_text.append(snake_case__ )
							if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(snake_case__ ) )

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

							SCREAMING_SNAKE_CASE_   : int					  =      (
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
										SCREAMING_SNAKE_CASE_   : Dict					  =      self.clean_up_tokenization(snake_case__ )
										return clean_text
							else:
										return text






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							if not os.path.isdir(snake_case__ ):
										logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
										return
							SCREAMING_SNAKE_CASE_   : str					  =      os.path.join(
							    snake_case__      ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

							if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ) and os.path.isfile(self.vocab_file ):
										copyfile(self.vocab_file      ,snake_case__ )
							elif not os.path.isfile(self.vocab_file ):
										with open(snake_case__      ,'wb' ) as fi:
													SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.sp_model.serialized_model_proto()
													fi.write(snake_case__ )

							return (out_vocab_file,)






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [self.cls_token_id]
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      [self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None      ,snake_case__ = False ):
							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=snake_case__      ,token_ids_a=snake_case__      ,already_has_special_tokens=snake_case__ )

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






				def 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : str					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : 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]









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


UpperCamelCase__ :   Any						=							logging.get_logger(__name__)

UpperCamelCase__ :   Any						=							{
    '''Visual-Attention-Network/van-base''': (
        '''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
    ),
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       List[Any]						=     "van"






				def __init__(			self      ,snake_case__=224      ,snake_case__=3      ,snake_case__=[7, 3, 3, 3]      ,snake_case__=[4, 2, 2, 2]      ,snake_case__=[64, 128, 320, 512]      ,snake_case__=[3, 3, 12, 3]      ,snake_case__=[8, 8, 4, 4]      ,snake_case__="gelu"      ,snake_case__=0.02      ,snake_case__=1E-6      ,snake_case__=1E-2      ,snake_case__=0.0      ,snake_case__=0.0      ,**snake_case__      ,):
							super().__init__(**snake_case__ )
							SCREAMING_SNAKE_CASE_   : Tuple					  =      image_size
							SCREAMING_SNAKE_CASE_   : List[str]					  =      num_channels
							SCREAMING_SNAKE_CASE_   : Dict					  =      patch_sizes
							SCREAMING_SNAKE_CASE_   : str					  =      strides
							SCREAMING_SNAKE_CASE_   : str					  =      hidden_sizes
							SCREAMING_SNAKE_CASE_   : str					  =      depths
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      mlp_ratios
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      hidden_act
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      layer_norm_eps
							SCREAMING_SNAKE_CASE_   : int					  =      layer_scale_init_value
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      drop_path_rate
							SCREAMING_SNAKE_CASE_   : List[str]					  =      dropout_rate



