
"""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


_a														=		logging.get_logger(__name__)

_a														=		{"""vocab_file""": """spiece.model"""}

_a														=		{
    """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"""
        ),
    }
}

_a														=		{
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				VOCAB_FILES_NAMES
		lowercase__      =				PRETRAINED_VOCAB_FILES_MAP
		lowercase__      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase__      =				['input_ids', 'attention_mask']
		lowercase__      =				[]



		def __init__( self    ,		__a    ,		__a="<unk>"    ,		__a="<s>"    ,		__a="</s>"    ,		__a="<pad>"    ,		__a="[SEP]"    ,		__a="[MASK]"    ,		__a="[CLS]"    ,		__a = None    ,		**__a    ,		)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  AddedToken(__a    ,		lstrip=__a    ,		rstrip=__a) if isinstance(__a    ,		__a) else bos_token
							_UpperCamelCase										=  AddedToken(__a    ,		lstrip=__a    ,		rstrip=__a) if isinstance(__a    ,		__a) else eos_token
							_UpperCamelCase										=  AddedToken(__a    ,		lstrip=__a    ,		rstrip=__a) if isinstance(__a    ,		__a) else unk_token
							_UpperCamelCase										=  AddedToken(__a    ,		lstrip=__a    ,		rstrip=__a) if isinstance(__a    ,		__a) else pad_token
							_UpperCamelCase										=  AddedToken(__a    ,		lstrip=__a    ,		rstrip=__a) if isinstance(__a    ,		__a) else cls_token
							_UpperCamelCase										=  AddedToken(__a    ,		lstrip=__a    ,		rstrip=__a) if isinstance(__a    ,		__a) else sep_token

							# Mask token behave like a normal word, i.e. include the space before it
							_UpperCamelCase										=  AddedToken(__a    ,		lstrip=__a    ,		rstrip=__a) if isinstance(__a    ,		__a) else mask_token

							_UpperCamelCase										=  {} if sp_model_kwargs is None else sp_model_kwargs

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

							_UpperCamelCase										=  vocab_file

							_UpperCamelCase										=  spm.SentencePieceProcessor(**self.sp_model_kwargs)
							self.sp_model.Load(__a)



		@property
		def 						UpperCAmelCase							( self)	->	Tuple:






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



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  {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)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  self.__dict__.copy()
							_UpperCamelCase										=  None
							return state



		def __setstate__( self    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase										=  d

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

							_UpperCamelCase										=  spm.SentencePieceProcessor(**self.sp_model_kwargs)
							self.sp_model.Load(self.vocab_file)



		def 						UpperCAmelCase							( self    ,		__a)	->	List[str]:






							'''simple docstring'''
							return self.sp_model.encode(__a    ,		out_type=__a)



		def 						UpperCAmelCase							( self    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							return self.sp_model.piece_to_id(__a)



		def 						UpperCAmelCase							( self    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.sp_model.IdToPiece(__a)
							return token



		def 						UpperCAmelCase							( self    ,		__a)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  []
							_UpperCamelCase										=  ''''''
							_UpperCamelCase										=  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(__a) + token
																	_UpperCamelCase										=  True
																	_UpperCamelCase										=  []
												else:
																	current_sub_tokens.append(__a)
																	_UpperCamelCase										=  False
							out_string += self.sp_model.decode(__a)
							return out_string.strip()



		def 						UpperCAmelCase							( self    ,		__a    ,		__a = False    ,		__a = None    ,		__a = True    ,		**__a    ,		)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  kwargs.pop('''use_source_tokenizer'''    ,		__a)

							_UpperCamelCase										=  self.convert_ids_to_tokens(__a    ,		skip_special_tokens=__a)

							# 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
							_UpperCamelCase										=  []
							_UpperCamelCase										=  []
							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(__a))
																						_UpperCamelCase										=  []
																	sub_texts.append(__a)
												else:
																	current_sub_text.append(__a)
							if current_sub_text:
												sub_texts.append(self.convert_tokens_to_string(__a))

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

							_UpperCamelCase										=  (
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
												_UpperCamelCase										=  self.clean_up_tokenization(__a)
												return clean_text
							else:
												return text



		def 						UpperCAmelCase							( self    ,		__a    ,		__a = None)	->	Tuple[str]:






							'''simple docstring'''
							if not os.path.isdir(__a):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
												return
							_UpperCamelCase										=  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:
																	_UpperCamelCase										=  self.sp_model.serialized_model_proto()
																	fi.write(__a)

							return (out_vocab_file,)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a = None)	->	List[int]:






							'''simple docstring'''
							if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_UpperCamelCase										=  [self.cls_token_id]
							_UpperCamelCase										=  [self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep



		def 						UpperCAmelCase							( self    ,		__a    ,		__a = None    ,		__a = False)	->	List[int]:






							'''simple docstring'''
							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__a    ,		token_ids_a=__a    ,		already_has_special_tokens=__a)

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




		def 						UpperCAmelCase							( self    ,		__a    ,		__a = None)	->	List[int]:






							'''simple docstring'''
							_UpperCamelCase										=  [self.sep_token_id]
							_UpperCamelCase										=  [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"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'levit'



		def __init__( self    ,		__a=2_24    ,		__a=3    ,		__a=3    ,		__a=2    ,		__a=1    ,		__a=16    ,		__a=[1_28, 2_56, 3_84]    ,		__a=[4, 8, 12]    ,		__a=[4, 4, 4]    ,		__a=[16, 16, 16]    ,		__a=0    ,		__a=[2, 2, 2]    ,		__a=[2, 2, 2]    ,		__a=0.02    ,		**__a    ,		)	->	List[str]:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase										=  image_size
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  kernel_size
							_UpperCamelCase										=  stride
							_UpperCamelCase										=  padding
							_UpperCamelCase										=  hidden_sizes
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  depths
							_UpperCamelCase										=  key_dim
							_UpperCamelCase										=  drop_path_rate
							_UpperCamelCase										=  patch_size
							_UpperCamelCase										=  attention_ratio
							_UpperCamelCase										=  mlp_ratio
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  [
							    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
							    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
							]






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				version.parse('1.11'     )



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ])




		@property
		def 						UpperCAmelCase							( self)	->	float:






							'''simple docstring'''
							return 1e-4






