



'''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




'''simple docstring'''




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			_A   :	Optional[int]    					=	(KDPMaDiscreteScheduler,)
			_A   :	Dict    					=	10

			def UpperCamelCase__      (							self  :				Optional[int]							,  **lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	{
					    """num_train_timesteps""": 1_1_0_0,
					    """beta_start""": 0.00_01,
					    """beta_end""": 0.02,
					    """beta_schedule""": """linear""",
					}

					config.update(**lowerCAmelCase__		)
					return config

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]							,  [0.00_02, 0.0_02, 0.02]		):
							self.check_over_configs(beta_start=lowerCAmelCase__							,  beta_end=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					for schedule in ["linear", "scaled_linear"]:
							self.check_over_configs(beta_schedule=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.get_scheduler_config(prediction_type="""v_prediction"""		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dummy_sample_deter * scheduler.init_noise_sigma
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	sample.to(lowerCAmelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Dict					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	List[str]					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.69_34E-07		) < 1E-2
							assert abs(result_mean.item() - 6.11_12E-10		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07		) < 1E-2
							assert abs(result_mean.item() - 0.00_02		) < 1E-3

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					if torch_device == "mps":
							return
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	str					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps		)

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.dummy_sample_deter * scheduler.init_noise_sigma
					__SCREAMING_SNAKE_CASE :	str					 =	sample.to(lowerCAmelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
							__SCREAMING_SNAKE_CASE :	Tuple					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Any					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	int					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	int					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3



			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					if torch_device == "mps":
							return
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.scheduler_classes[0]
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.get_scheduler_config()
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	scheduler_class(**lowerCAmelCase__		)

					scheduler.set_timesteps(self.num_inference_steps							,  device=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.dummy_model()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.dummy_sample_deter.to(lowerCAmelCase__		) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
							__SCREAMING_SNAKE_CASE :	Dict					 =	scheduler.scale_model_input(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Any					 =	model(lowerCAmelCase__							,  lowerCAmelCase__		)

							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	scheduler.step(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	str					 =	output.prev_sample

					__SCREAMING_SNAKE_CASE :	Dict					 =	torch.sum(torch.abs(lowerCAmelCase__		)		)
					__SCREAMING_SNAKE_CASE :	int					 =	torch.mean(torch.abs(lowerCAmelCase__		)		)

					if str(lowerCAmelCase__		).startswith("""cpu"""		):
							# The following sum varies between 148 and 156 on mps. Why?
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3
					else:
							# CUDA
							assert abs(result_sum.item() - 20.41_25		) < 1E-2
							assert abs(result_mean.item() - 0.02_66		) < 1E-3