



"""simple docstring"""






import os
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


__snake_case							:   List[str]      =    logging.get_logger(__name__)

__snake_case							:   Optional[Any]      =    {'vocab_file': 'sentencepiece.bpe.model'}

__snake_case							:   List[str]      =    {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

__snake_case							:   Optional[int]      =    {
    'camembert-base': 512,
}

__snake_case							:   Any      =    '▁'



class 			A__   (  __SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE          =	PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE          =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE          =	['input_ids', 'attention_mask']

	def __init__(     self:		int       ,			_SCREAMING_SNAKE_CASE:		str       ,			_SCREAMING_SNAKE_CASE:		List[str]="<s>"       ,			_SCREAMING_SNAKE_CASE:		int="</s>"       ,			_SCREAMING_SNAKE_CASE:		List[str]="</s>"       ,			_SCREAMING_SNAKE_CASE:		Any="<s>"       ,			_SCREAMING_SNAKE_CASE:		str="<unk>"       ,			_SCREAMING_SNAKE_CASE:		List[str]="<pad>"       ,			_SCREAMING_SNAKE_CASE:		Dict="<mask>"       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=["<s>NOTUSED", "</s>NOTUSED"]       ,			_SCREAMING_SNAKE_CASE:		Optional[Dict[str, Any]] = None       ,			**_SCREAMING_SNAKE_CASE:		Dict       ,			)				->       None:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       AddedToken(_SCREAMING_SNAKE_CASE       ,			lstrip=_SCREAMING_SNAKE_CASE       ,			rstrip=_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE) else mask_token

				__lowerCAmelCase      :  Any												=       {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=_SCREAMING_SNAKE_CASE       ,			eos_token=_SCREAMING_SNAKE_CASE       ,			unk_token=_SCREAMING_SNAKE_CASE       ,			sep_token=_SCREAMING_SNAKE_CASE       ,			cls_token=_SCREAMING_SNAKE_CASE       ,			pad_token=_SCREAMING_SNAKE_CASE       ,			mask_token=_SCREAMING_SNAKE_CASE       ,			additional_special_tokens=_SCREAMING_SNAKE_CASE       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**_SCREAMING_SNAKE_CASE       ,			)
				__lowerCAmelCase      :  Optional[Any]												=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(str(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase      :  Optional[Any]												=       vocab_file
				# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
				# sentencepiece vocabulary (this is the case for <s> and </s>
				__lowerCAmelCase      :  List[str]												=       {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
				__lowerCAmelCase      :  Union[str, Any]												=       len(self.fairseq_tokens_to_ids)
				__lowerCAmelCase      :  Tuple												=       len(self.sp_model) + len(self.fairseq_tokens_to_ids)
				__lowerCAmelCase      :  Dict												=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			_SCREAMING_SNAKE_CASE:		List[int]       ,			_SCREAMING_SNAKE_CASE:		Optional[List[int]] = None)				->       List[int]:

				"""simple docstring"""

				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__lowerCAmelCase      :  List[Any]												=       [self.cls_token_id]
				__lowerCAmelCase      :  Dict												=       [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str]       ,			_SCREAMING_SNAKE_CASE:		List[int]       ,			_SCREAMING_SNAKE_CASE:		Optional[List[int]] = None       ,			_SCREAMING_SNAKE_CASE:		bool = False)				->       List[int]:

				"""simple docstring"""

				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=_SCREAMING_SNAKE_CASE       ,			token_ids_a=_SCREAMING_SNAKE_CASE       ,			already_has_special_tokens=_SCREAMING_SNAKE_CASE)

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		List[int]       ,			_SCREAMING_SNAKE_CASE:		Optional[List[int]] = None)				->       List[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       [self.sep_token_id]
				__lowerCAmelCase      :  Optional[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 + sep + token_ids_a + sep) * [0]

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       List[Any]:

				"""simple docstring"""

				return len(self.fairseq_tokens_to_ids) + len(self.sp_model)

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

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
				vocab.update(self.added_tokens_encoder)
				return vocab

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_SCREAMING_SNAKE_CASE:		str)				->       List[str]:

				"""simple docstring"""

				return self.sp_model.encode(_SCREAMING_SNAKE_CASE       ,			out_type=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		Optional[Any])				->       int:

				"""simple docstring"""

				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				elif self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE) == 0:
							# Convert sentence piece unk token to fairseq unk token index
							return self.unk_token_id
				return self.fairseq_offset + self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple       ,			_SCREAMING_SNAKE_CASE:		Tuple)				->       Dict:

				"""simple docstring"""

				if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset)

	def 							_SCREAMING_SNAKE_CASE   (     self:		str       ,			_SCREAMING_SNAKE_CASE:		Dict)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       []
				__lowerCAmelCase      :  List[str]												=       ""
				__lowerCAmelCase      :  Tuple												=       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(_SCREAMING_SNAKE_CASE) + token
										__lowerCAmelCase      :  List[Any]												=       True
										__lowerCAmelCase      :  Union[str, Any]												=       []
							else:
										current_sub_tokens.append(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase      :  Optional[int]												=       False
				out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE)
				return out_string.strip()

	def __getstate__(     self:		int)				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.__dict__.copy()
				__lowerCAmelCase      :  Union[str, Any]												=       None
				return state

	def __setstate__(     self:		Any       ,			_SCREAMING_SNAKE_CASE:		Dict)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       d

				# for backward compatibility
				if not hasattr(self       ,			"sp_model_kwargs"):
							__lowerCAmelCase      :  Optional[Any]												=       {}

				__lowerCAmelCase      :  Tuple												=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(self.vocab_file)







	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		str       ,			_SCREAMING_SNAKE_CASE:		Optional[str] = None)				->       Tuple[str]:

				"""simple docstring"""

				if not os.path.isdir(_SCREAMING_SNAKE_CASE):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
							return
				__lowerCAmelCase      :  Union[str, Any]												=       os.path.join(
				    _SCREAMING_SNAKE_CASE       ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

				return (out_vocab_file,)




"""simple docstring"""






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


__snake_case							:   Optional[int]      =    logging.get_logger(__name__)



class 			A__   (  __SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'maskformer-swin'

	SCREAMING_SNAKE_CASE          =	{
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}

	def __init__(     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		int=224       ,			_SCREAMING_SNAKE_CASE:		Tuple=4       ,			_SCREAMING_SNAKE_CASE:		int=3       ,			_SCREAMING_SNAKE_CASE:		List[Any]=96       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=[2, 2, 6, 2]       ,			_SCREAMING_SNAKE_CASE:		Any=[3, 6, 12, 24]       ,			_SCREAMING_SNAKE_CASE:		List[str]=7       ,			_SCREAMING_SNAKE_CASE:		List[str]=4.0       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=True       ,			_SCREAMING_SNAKE_CASE:		Tuple=0.0       ,			_SCREAMING_SNAKE_CASE:		Any=0.0       ,			_SCREAMING_SNAKE_CASE:		Any=0.1       ,			_SCREAMING_SNAKE_CASE:		str="gelu"       ,			_SCREAMING_SNAKE_CASE:		Tuple=False       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=0.02       ,			_SCREAMING_SNAKE_CASE:		str=1e-5       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=None       ,			_SCREAMING_SNAKE_CASE:		str=None       ,			**_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			)				->       List[str]:

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  int												=       image_size
				__lowerCAmelCase      :  Any												=       patch_size
				__lowerCAmelCase      :  Tuple												=       num_channels
				__lowerCAmelCase      :  Any												=       embed_dim
				__lowerCAmelCase      :  Any												=       depths
				__lowerCAmelCase      :  Dict												=       len(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       num_heads
				__lowerCAmelCase      :  Tuple												=       window_size
				__lowerCAmelCase      :  Dict												=       mlp_ratio
				__lowerCAmelCase      :  Any												=       qkv_bias
				__lowerCAmelCase      :  Union[str, Any]												=       hidden_dropout_prob
				__lowerCAmelCase      :  int												=       attention_probs_dropout_prob
				__lowerCAmelCase      :  Tuple												=       drop_path_rate
				__lowerCAmelCase      :  int												=       hidden_act
				__lowerCAmelCase      :  Optional[int]												=       use_absolute_embeddings
				__lowerCAmelCase      :  List[str]												=       layer_norm_eps
				__lowerCAmelCase      :  Any												=       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      :  Optional[Any]												=       int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE) - 1))
				__lowerCAmelCase      :  Any												=       ["stem"] + [F"""stage{idx}""" for idx in range(1       ,			len(_SCREAMING_SNAKE_CASE) + 1)]
				__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       get_aligned_output_features_output_indices(
				    out_features=_SCREAMING_SNAKE_CASE       ,			out_indices=_SCREAMING_SNAKE_CASE       ,			stage_names=self.stage_names)