
"""simple docstring"""




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


snake_case    =		logging.get_logger(__name__)

snake_case    =		{
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				UpperCAmelCase_				:      Any						  =				"oneformer"
				UpperCAmelCase_				:      int						  =				{"hidden_size": "hidden_dim"}


				def __init__(				self		,	lowercase__ = None		,	lowercase__ = 255		,	lowercase__ = 150		,	lowercase__ = 0.1		,	lowercase__ = 2.0		,	lowercase__ = 5.0		,	lowercase__ = 5.0		,	lowercase__ = 0.5		,	lowercase__ = 0.07		,	lowercase__ = 12544		,	lowercase__ = 3.0		,	lowercase__ = 0.75		,	lowercase__ = 0.02		,	lowercase__ = 1.0		,	lowercase__ = 1E-05		,	lowercase__ = False		,	lowercase__ = True		,	lowercase__ = True		,	lowercase__ = [4, 8, 16, 32]		,	lowercase__ = 77		,	lowercase__ = 256		,	lowercase__ = 77		,	lowercase__ = 6		,	lowercase__ = 49408		,	lowercase__ = 2		,	lowercase__ = 16		,	lowercase__ = 256		,	lowercase__ = 256		,	lowercase__ = 256		,	lowercase__ = 1024		,	lowercase__ = "GN"		,	lowercase__ = 6		,	lowercase__ = 10		,	lowercase__ = True		,	lowercase__ = 8		,	lowercase__ = 0.1		,	lowercase__ = 2048		,	lowercase__ = False		,	lowercase__ = False		,	lowercase__ = 2		,	lowercase__ = 4		,	**lowercase__		,	)    ->    List[str]:
									"""simple docstring"""







									if backbone_config is None:
														logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'			)
														SCREAMING_SNAKE_CASE				=	CONFIG_MAPPING['swin'](
														    image_size=224		,	in_channels=3		,	patch_size=4		,	embed_dim=96		,	depths=[2, 2, 6, 2]		,	num_heads=[3, 6, 12, 24]		,	window_size=7		,	drop_path_rate=0.3		,	use_absolute_embeddings=lowercase__		,	out_features=['stage1', 'stage2', 'stage3', 'stage4']		,	)
									elif isinstance(lowercase__		,	lowercase__			):
														SCREAMING_SNAKE_CASE				=	backbone_config.get('model_type'			)
														SCREAMING_SNAKE_CASE				=	CONFIG_MAPPING[backbone_model_type]
														SCREAMING_SNAKE_CASE				=	config_class.from_dict(lowercase__			)

									SCREAMING_SNAKE_CASE				=	backbone_config

									SCREAMING_SNAKE_CASE				=	ignore_value
									SCREAMING_SNAKE_CASE				=	num_queries
									SCREAMING_SNAKE_CASE				=	no_object_weight
									SCREAMING_SNAKE_CASE				=	class_weight
									SCREAMING_SNAKE_CASE				=	mask_weight
									SCREAMING_SNAKE_CASE				=	dice_weight
									SCREAMING_SNAKE_CASE				=	contrastive_weight
									SCREAMING_SNAKE_CASE				=	contrastive_temperature
									SCREAMING_SNAKE_CASE				=	train_num_points
									SCREAMING_SNAKE_CASE				=	oversample_ratio
									SCREAMING_SNAKE_CASE				=	importance_sample_ratio
									SCREAMING_SNAKE_CASE				=	init_std
									SCREAMING_SNAKE_CASE				=	init_xavier_std
									SCREAMING_SNAKE_CASE				=	layer_norm_eps
									SCREAMING_SNAKE_CASE				=	is_training
									SCREAMING_SNAKE_CASE				=	use_auxiliary_loss
									SCREAMING_SNAKE_CASE				=	output_auxiliary_logits
									SCREAMING_SNAKE_CASE				=	strides
									SCREAMING_SNAKE_CASE				=	task_seq_len
									SCREAMING_SNAKE_CASE				=	text_encoder_width
									SCREAMING_SNAKE_CASE				=	text_encoder_context_length
									SCREAMING_SNAKE_CASE				=	text_encoder_num_layers
									SCREAMING_SNAKE_CASE				=	text_encoder_vocab_size
									SCREAMING_SNAKE_CASE				=	text_encoder_proj_layers
									SCREAMING_SNAKE_CASE				=	text_encoder_n_ctx
									SCREAMING_SNAKE_CASE				=	conv_dim
									SCREAMING_SNAKE_CASE				=	mask_dim
									SCREAMING_SNAKE_CASE				=	hidden_dim
									SCREAMING_SNAKE_CASE				=	encoder_feedforward_dim
									SCREAMING_SNAKE_CASE				=	norm
									SCREAMING_SNAKE_CASE				=	encoder_layers
									SCREAMING_SNAKE_CASE				=	decoder_layers
									SCREAMING_SNAKE_CASE				=	use_task_norm
									SCREAMING_SNAKE_CASE				=	num_attention_heads
									SCREAMING_SNAKE_CASE				=	dropout
									SCREAMING_SNAKE_CASE				=	dim_feedforward
									SCREAMING_SNAKE_CASE				=	pre_norm
									SCREAMING_SNAKE_CASE				=	enforce_input_proj
									SCREAMING_SNAKE_CASE				=	query_dec_layers
									SCREAMING_SNAKE_CASE				=	common_stride
									SCREAMING_SNAKE_CASE				=	decoder_layers

									super().__init__(**lowercase__			)




				def 							A					(				self			)    ->    Dict[str, any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	copy.deepcopy(self.__dict__			)
									SCREAMING_SNAKE_CASE				=	self.backbone_config.to_dict()
									SCREAMING_SNAKE_CASE				=	self.__class__.model_type
									return output


from math import cos, sin







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_:							int		=  float(lowerCamelCase  )
				return [[scaling_factor * int(x == y  ) for x in range(2  )] for y in range(2  )]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Union[str, Any]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				return [[c, -s], [s, c]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							Any		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  c * s
				return [[c * c, cs], [cs, s * s]]







def 				A__    (  lowerCamelCase  )     ->		list[list[float]]:
				UpperCamelCase_,      UpperCamelCase_:							List[str]		=  cos(lowerCamelCase  ), sin(lowerCamelCase  )
				UpperCamelCase_:							int		=  c * s
				return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"""    {scaling(5) = }""")
print(F"""  {rotation(45) = }""")
print(F"""{projection(45) = }""")





print(F"""{reflection(45) = }""")






