




import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_						=logging.get_logger(__name__)

UpperCAmelCase_						={"""vocab_file""": """sentencepiece.model"""}

UpperCAmelCase_						={
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

UpperCAmelCase_						={
    """google/rembert""": 256,
}


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Optional[int]										=VOCAB_FILES_NAMES
				__a							: Union[str, Any]										=PRETRAINED_VOCAB_FILES_MAP
				__a							: Optional[int]										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=False       , UpperCAmelCase_=True       , UpperCAmelCase_=True       , UpperCAmelCase_="[CLS]"       , UpperCAmelCase_="[SEP]"       , UpperCAmelCase_="[UNK]"       , UpperCAmelCase_="[SEP]"       , UpperCAmelCase_="[PAD]"       , UpperCAmelCase_="[CLS]"       , UpperCAmelCase_="[MASK]"       , **UpperCAmelCase_       , ):
											super().__init__(
											    do_lower_case=UpperCAmelCase_       , remove_space=UpperCAmelCase_       , keep_accents=UpperCAmelCase_       , bos_token=UpperCAmelCase_       , eos_token=UpperCAmelCase_       , unk_token=UpperCAmelCase_       , sep_token=UpperCAmelCase_       , pad_token=UpperCAmelCase_       , cls_token=UpperCAmelCase_       , mask_token=UpperCAmelCase_       , **UpperCAmelCase_       , )

											lowerCAmelCase   =		do_lower_case
											lowerCAmelCase   =		remove_space
											lowerCAmelCase   =		keep_accents
											lowerCAmelCase   =		vocab_file

											lowerCAmelCase   =		spm.SentencePieceProcessor()
											self.sp_model.Load(UpperCAmelCase_						)



				@property
				def       __snake_case		( self						):
											return len(self.sp_model						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		{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						):
											lowerCAmelCase   =		self.__dict__.copy()
											lowerCAmelCase   =		None
											return state



				def __setstate__( self       , UpperCAmelCase_						):
											lowerCAmelCase   =		d
											lowerCAmelCase   =		spm.SentencePieceProcessor()
											self.sp_model.Load(self.vocab_file						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=False						):
											lowerCAmelCase   =		self.sp_model.EncodeAsPieces(UpperCAmelCase_						)
											return pieces



				def       __snake_case		( self       , UpperCAmelCase_						):
											return self.sp_model.PieceToId(UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_						):
											return self.sp_model.IdToPiece(UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_						):
											lowerCAmelCase   =		self.sp_model.decode_pieces(UpperCAmelCase_						)
											return out_string



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		[self.sep_token_id]
											lowerCAmelCase   =		[self.cls_token_id]
											if token_ids_a is None:
																		return cls + token_ids_a + sep
											return cls + token_ids_a + sep + token_ids_a + sep



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = False						):

											if already_has_special_tokens:
																		if token_ids_a is not None:
																									raise ValueError(
																									    '''You should not supply a second sequence if the provided sequence of '''
																									    '''ids is already formatted with special tokens for the model.'''						)
																		return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

											if token_ids_a is not None:
																		return [1] + ([0] * len(UpperCAmelCase_						)) + [1] + ([0] * len(UpperCAmelCase_						)) + [1]
											return [1] + ([0] * len(UpperCAmelCase_						)) + [1]



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		[self.sep_token_id]
											lowerCAmelCase   =		[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]






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											if not os.path.isdir(UpperCAmelCase_						):
																		logger.error('''Vocabulary path ({}) should be a directory'''.format(UpperCAmelCase_						)						)
																		return
											lowerCAmelCase   =		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_						):
																		copyfile(self.vocab_file       , UpperCAmelCase_						)

											return (out_vocab_file,)









from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_						=logging.get_logger(__name__)


class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Optional[Any]										="""maskformer-swin"""

				__a							: Optional[int]										={
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}



				def __init__( self       , UpperCAmelCase_=2_24       , UpperCAmelCase_=4       , UpperCAmelCase_=3       , UpperCAmelCase_=96       , UpperCAmelCase_=[2, 2, 6, 2]       , UpperCAmelCase_=[3, 6, 12, 24]       , UpperCAmelCase_=7       , UpperCAmelCase_=4.0       , UpperCAmelCase_=True       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.1       , UpperCAmelCase_="gelu"       , UpperCAmelCase_=False       , UpperCAmelCase_=0.02       , UpperCAmelCase_=1E-5       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , **UpperCAmelCase_       , ):
											super().__init__(**UpperCAmelCase_						)

											lowerCAmelCase   =		image_size
											lowerCAmelCase   =		patch_size
											lowerCAmelCase   =		num_channels
											lowerCAmelCase   =		embed_dim
											lowerCAmelCase   =		depths
											lowerCAmelCase   =		len(UpperCAmelCase_						)
											lowerCAmelCase   =		num_heads
											lowerCAmelCase   =		window_size
											lowerCAmelCase   =		mlp_ratio
											lowerCAmelCase   =		qkv_bias
											lowerCAmelCase   =		hidden_dropout_prob
											lowerCAmelCase   =		attention_probs_dropout_prob
											lowerCAmelCase   =		drop_path_rate
											lowerCAmelCase   =		hidden_act
											lowerCAmelCase   =		use_absolute_embeddings
											lowerCAmelCase   =		layer_norm_eps
											lowerCAmelCase   =		initializer_range
											# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
											# this indicates the channel dimension after the last stage of the model
											lowerCAmelCase   =		int(embed_dim * 2 ** (len(UpperCAmelCase_						) - 1)						)
											lowerCAmelCase   =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       , len(UpperCAmelCase_						) + 1						)]
											lowerCAmelCase   ,     lowerCAmelCase   =		get_aligned_output_features_output_indices(
											    out_features=UpperCAmelCase_       , out_indices=UpperCAmelCase_       , stage_names=self.stage_names						)



