




"""simple docstring"""






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   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = '''maskformer-swin'''

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





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




											"""simple docstring"""







											super().__init__(**a__)

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












"""simple docstring"""






import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class   a__			(       UpperCamelCase_  ):
						snake_case__             = (KDPMaDiscreteScheduler,)
						snake_case__             = 1_0





						def       __UpperCamelCase     (							self      :	Dict     ,**a__      :	str)						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  {
											    '''num_train_timesteps''': 1100,
											    '''beta_start''': 0.0001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											}

											config.update(**a__)
											return config





						def       __UpperCamelCase     (							self      :	Optional[int])						->       Optional[int]:




											"""simple docstring"""







											for timesteps in [10, 50, 100, 1000]:
																self.check_over_configs(num_train_timesteps=a__)





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




											"""simple docstring"""







											for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]     ,[0.0002, 0.002, 0.02]):
																self.check_over_configs(beta_start=a__     ,beta_end=a__)





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Union[str, Any]:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	str)						->       str:




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Tuple)						->       Tuple:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  self.scheduler_classes[0]
											_lowerCAmelCase:Union[str, Any]									=  self.get_scheduler_config(prediction_type='''v_prediction''')
											_lowerCAmelCase:Tuple									=  scheduler_class(**a__)

											scheduler.set_timesteps(self.num_inference_steps)

											_lowerCAmelCase:Optional[Any]									=  self.dummy_model()
											_lowerCAmelCase:List[Any]									=  self.dummy_sample_deter * scheduler.init_noise_sigma
											_lowerCAmelCase:Optional[Any]									=  sample.to(a__)

											for i, t in enumerate(scheduler.timesteps):
																_lowerCAmelCase:Tuple									=  scheduler.scale_model_input(a__     ,a__)

																_lowerCAmelCase:Any									=  model(a__     ,a__)

																_lowerCAmelCase:Union[str, Any]									=  scheduler.step(a__     ,a__     ,a__)
																_lowerCAmelCase:Optional[int]									=  output.prev_sample

											_lowerCAmelCase:str									=  torch.sum(torch.abs(a__))
											_lowerCAmelCase:Union[str, Any]									=  torch.mean(torch.abs(a__))

											if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 4.6_934E-07) < 1E-2
																assert abs(result_mean.item() - 6.1_112E-10) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 4.693_428_650_170_972E-07) < 1E-2
																assert abs(result_mean.item() - 0.0002) < 1E-3





						def       __UpperCamelCase     (							self      :	Tuple)						->       Dict:




											"""simple docstring"""







											if torch_device == "mps":
																return
											_lowerCAmelCase:Tuple									=  self.scheduler_classes[0]
											_lowerCAmelCase:List[str]									=  self.get_scheduler_config()
											_lowerCAmelCase:Union[str, Any]									=  scheduler_class(**a__)

											scheduler.set_timesteps(self.num_inference_steps)

											_lowerCAmelCase:Any									=  self.dummy_model()
											_lowerCAmelCase:str									=  self.dummy_sample_deter * scheduler.init_noise_sigma
											_lowerCAmelCase:List[str]									=  sample.to(a__)

											for i, t in enumerate(scheduler.timesteps):
																_lowerCAmelCase:Tuple									=  scheduler.scale_model_input(a__     ,a__)

																_lowerCAmelCase:List[str]									=  model(a__     ,a__)

																_lowerCAmelCase:int									=  scheduler.step(a__     ,a__     ,a__)
																_lowerCAmelCase:List[Any]									=  output.prev_sample

											_lowerCAmelCase:Tuple									=  torch.sum(torch.abs(a__))
											_lowerCAmelCase:Dict									=  torch.mean(torch.abs(a__))

											if torch_device in ["cpu", "mps"]:
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3







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




											"""simple docstring"""







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

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

											_lowerCAmelCase:Optional[int]									=  self.dummy_model()
											_lowerCAmelCase:Optional[Any]									=  self.dummy_sample_deter.to(a__) * scheduler.init_noise_sigma

											for t in scheduler.timesteps:
																_lowerCAmelCase:Union[str, Any]									=  scheduler.scale_model_input(a__     ,a__)

																_lowerCAmelCase:Tuple									=  model(a__     ,a__)

																_lowerCAmelCase:str									=  scheduler.step(a__     ,a__     ,a__)
																_lowerCAmelCase:Dict									=  output.prev_sample

											_lowerCAmelCase:Any									=  torch.sum(torch.abs(a__))
											_lowerCAmelCase:str									=  torch.mean(torch.abs(a__))

											if str(a__).startswith('''cpu'''):
																# The following sum varies between 148 and 156 on mps. Why?
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3
											else:
																# CUDA
																assert abs(result_sum.item() - 20.4125) < 1E-2
																assert abs(result_mean.item() - 0.0266) < 1E-3






