



'''simple docstring'''




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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_camembert import CamembertTokenizer
else:
		UpperCamelCase__ :			Tuple												=				None


UpperCamelCase__ :			Optional[Any]												=				logging.get_logger(__name__)

UpperCamelCase__ :			Any												=				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__ :			Tuple												=				{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

UpperCamelCase__ :			Optional[Any]												=				{
    "camembert-base": 5_12,
}

UpperCamelCase__ :			Optional[Any]												=				"▁"






class 		_UpperCamelCase						(							__lowerCAmelCase					):


			'''simple docstring'''

			_A   :	int    					=	VOCAB_FILES_NAMES
			_A   :	Union[str, Any]    					=	PRETRAINED_VOCAB_FILES_MAP
			_A   :	Optional[int]    					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A   :	List[str]    					=	["input_ids", "attention_mask"]
			_A   :	Dict    					=	CamembertTokenizer

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	AddedToken(lowerCamelCase__							,  lstrip=lowerCamelCase__							,  rstrip=lowerCamelCase__		) if isinstance(lowerCamelCase__							,  lowerCamelCase__		) else mask_token

					super().__init__(
					    lowerCamelCase__							,  tokenizer_file=lowerCamelCase__							,  bos_token=lowerCamelCase__							,  eos_token=lowerCamelCase__							,  sep_token=lowerCamelCase__							,  cls_token=lowerCamelCase__							,  unk_token=lowerCamelCase__							,  pad_token=lowerCamelCase__							,  mask_token=lowerCamelCase__							,  additional_special_tokens=lowerCamelCase__							,  **lowerCamelCase__							,  )

					__SCREAMING_SNAKE_CASE :	Tuple					 =	vocab_file
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	False if not self.vocab_file else True

			def UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				List[int]							,  lowerCAmelCase__  :				Optional[List[int]] = None		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				List[int]							,  lowerCAmelCase__  :				Optional[List[int]] = None		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	[self.sep_token_id]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	[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]



			def UpperCamelCase__      (							self  :				Optional[int]							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Optional[str] = None		):





					"""simple docstring"""






					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
							    """tokenizer."""		)

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

					if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase__		):
							copyfile(self.vocab_file							,  lowerCamelCase__		)

					return (out_vocab_file,)




'''simple docstring'''




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


UpperCamelCase__ :			Optional[int]												=				{
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

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






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[Any]    					=	'''maskformer'''
			_A   :	Optional[int]    					=	{'''hidden_size''': '''mask_feature_size'''}
			_A   :	int    					=	['''resnet''', '''swin''']
			_A   :	Any    					=	['''detr''']

			def __init__(							self  :				Any							,  lowerCAmelCase__  :				int = 2_5_6							,  lowerCAmelCase__  :				int = 2_5_6							,  lowerCAmelCase__  :				float = 0.1							,  lowerCAmelCase__  :				bool = False							,  lowerCAmelCase__  :				Optional[Dict] = None							,  lowerCAmelCase__  :				Optional[Dict] = None							,  lowerCAmelCase__  :				float = 0.02							,  lowerCAmelCase__  :				float = 1.0							,  lowerCAmelCase__  :				float = 1.0							,  lowerCAmelCase__  :				float = 1.0							,  lowerCAmelCase__  :				float = 20.0							,  lowerCAmelCase__  :				Optional[bool] = None							,  **lowerCAmelCase__  :				Any							,  ):





					"""simple docstring"""






					if backbone_config is None:
							# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
							__SCREAMING_SNAKE_CASE :	Any					 =	SwinConfig(
							    image_size=3_8_4							,  in_channels=3							,  patch_size=4							,  embed_dim=1_2_8							,  depths=[2, 2, 1_8, 2]							,  num_heads=[4, 8, 1_6, 3_2]							,  window_size=1_2							,  drop_path_rate=0.3							,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]							,  )

					if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
							__SCREAMING_SNAKE_CASE :	Dict					 =	backbone_config.pop("""model_type"""		)
							__SCREAMING_SNAKE_CASE :	List[str]					 =	CONFIG_MAPPING[backbone_model_type]
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	config_class.from_dict(lowerCAmelCase__		)

					# verify that the backbone is supported
					if backbone_config.model_type not in self.backbones_supported:
							logger.warning_once(
							    F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
							    F"Supported model types: {','.join(self.backbones_supported		)}"		)

					if decoder_config is None:
							# fall back to https://huggingface.co/facebook/detr-resnet-50
							__SCREAMING_SNAKE_CASE :	List[str]					 =	DetrConfig()
					else:
							# verify that the decoder is supported
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	(
							    decoder_config.pop("""model_type"""		) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		) else decoder_config.model_type
							)
							if decoder_type not in self.decoders_supported:
									raise ValueError(
									    F"Transformer Decoder {decoder_type} not supported, please use one of"
									    F" {','.join(self.decoders_supported		)}"		)
							if isinstance(lowerCAmelCase__							,  lowerCAmelCase__		):
									__SCREAMING_SNAKE_CASE :	int					 =	CONFIG_MAPPING[decoder_type]
									__SCREAMING_SNAKE_CASE :	Dict					 =	config_class.from_dict(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Any					 =	backbone_config
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	decoder_config
					# main feature dimension for the model
					__SCREAMING_SNAKE_CASE :	List[str]					 =	fpn_feature_size
					__SCREAMING_SNAKE_CASE :	int					 =	mask_feature_size
					# initializer
					__SCREAMING_SNAKE_CASE :	str					 =	init_std
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	init_xavier_std
					# Hungarian matcher && loss
					__SCREAMING_SNAKE_CASE :	Tuple					 =	cross_entropy_weight
					__SCREAMING_SNAKE_CASE :	Dict					 =	dice_weight
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	mask_weight
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	use_auxiliary_loss
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	no_object_weight
					__SCREAMING_SNAKE_CASE :	Dict					 =	output_auxiliary_logits

					__SCREAMING_SNAKE_CASE :	Any					 =	self.decoder_config.encoder_attention_heads
					__SCREAMING_SNAKE_CASE :	int					 =	self.decoder_config.num_hidden_layers
					super().__init__(**lowerCAmelCase__		)

			@classmethod
			def UpperCamelCase__      (							cls  :				List[Any]							,  lowerCAmelCase__  :				PretrainedConfig							,  lowerCAmelCase__  :				PretrainedConfig							,  **lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					return cls(
					    backbone_config=lowerCAmelCase__							,  decoder_config=lowerCAmelCase__							,  **lowerCAmelCase__							,  )



			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	str					 =	copy.deepcopy(self.__dict__		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.backbone_config.to_dict()
					__SCREAMING_SNAKE_CASE :	str					 =	self.decoder_config.to_dict()
					__SCREAMING_SNAKE_CASE :	List[str]					 =	self.__class__.model_type
					return output