





"""simple docstring"""







import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class __magic_name__   (       A__		):
       UpperCamelCase    :						int      =	(EulerDiscreteScheduler,)
       UpperCamelCase    :						int      =	10
       def 		_lowerCamelCase   (     self   ,  **__magic_name__	):





         """simple docstring"""


         _lowerCAmelCase	      =					{
             'num_train_timesteps': 1_1_0_0,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**__magic_name__	)
         return config
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


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





         """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=__magic_name__   ,  beta_end=__magic_name__	)
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


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





         """simple docstring"""


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





         """simple docstring"""


         _lowerCAmelCase	      =					self.scheduler_classes[0]
         _lowerCAmelCase	      =					self.get_scheduler_config()
         _lowerCAmelCase	      =					scheduler_class(**__magic_name__	)

         scheduler.set_timesteps(self.num_inference_steps	)

         _lowerCAmelCase	      =					torch.manual_seed(0	)

         _lowerCAmelCase	      =					self.dummy_model()
         _lowerCAmelCase	      =					self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowerCAmelCase	      =					sample.to(__magic_name__	)

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

           _lowerCAmelCase	      =					model(__magic_name__   ,  __magic_name__	)

           _lowerCAmelCase	      =					scheduler.step(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  generator=__magic_name__	)
           _lowerCAmelCase	      =					output.prev_sample

         _lowerCAmelCase	      =					torch.sum(torch.abs(__magic_name__	)	)
         _lowerCAmelCase	      =					torch.mean(torch.abs(__magic_name__	)	)

         assert abs(result_sum.item() - 10.08_07	) < 1e-2
         assert abs(result_mean.item() - 0.01_31	) < 1e-3
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         _lowerCAmelCase	      =					self.scheduler_classes[0]
         _lowerCAmelCase	      =					self.get_scheduler_config(prediction_type='v_prediction'	)
         _lowerCAmelCase	      =					scheduler_class(**__magic_name__	)

         scheduler.set_timesteps(self.num_inference_steps	)

         _lowerCAmelCase	      =					torch.manual_seed(0	)

         _lowerCAmelCase	      =					self.dummy_model()
         _lowerCAmelCase	      =					self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowerCAmelCase	      =					sample.to(__magic_name__	)

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

           _lowerCAmelCase	      =					model(__magic_name__   ,  __magic_name__	)

           _lowerCAmelCase	      =					scheduler.step(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  generator=__magic_name__	)
           _lowerCAmelCase	      =					output.prev_sample

         _lowerCAmelCase	      =					torch.sum(torch.abs(__magic_name__	)	)
         _lowerCAmelCase	      =					torch.mean(torch.abs(__magic_name__	)	)

         assert abs(result_sum.item() - 0.00_02	) < 1e-2
         assert abs(result_mean.item() - 2.2_676e-06	) < 1e-3
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         _lowerCAmelCase	      =					self.scheduler_classes[0]
         _lowerCAmelCase	      =					self.get_scheduler_config()
         _lowerCAmelCase	      =					scheduler_class(**__magic_name__	)

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

         _lowerCAmelCase	      =					torch.manual_seed(0	)

         _lowerCAmelCase	      =					self.dummy_model()
         _lowerCAmelCase	      =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         _lowerCAmelCase	      =					sample.to(__magic_name__	)

         for t in scheduler.timesteps:
           _lowerCAmelCase	      =					scheduler.scale_model_input(__magic_name__   ,  __magic_name__	)

           _lowerCAmelCase	      =					model(__magic_name__   ,  __magic_name__	)

           _lowerCAmelCase	      =					scheduler.step(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  generator=__magic_name__	)
           _lowerCAmelCase	      =					output.prev_sample

         _lowerCAmelCase	      =					torch.sum(torch.abs(__magic_name__	)	)
         _lowerCAmelCase	      =					torch.mean(torch.abs(__magic_name__	)	)

         assert abs(result_sum.item() - 10.08_07	) < 1e-2
         assert abs(result_mean.item() - 0.01_31	) < 1e-3
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         _lowerCAmelCase	      =					self.scheduler_classes[0]
         _lowerCAmelCase	      =					self.get_scheduler_config()
         _lowerCAmelCase	      =					scheduler_class(**__magic_name__   ,  use_karras_sigmas=__magic_name__	)

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

         _lowerCAmelCase	      =					torch.manual_seed(0	)

         _lowerCAmelCase	      =					self.dummy_model()
         _lowerCAmelCase	      =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         _lowerCAmelCase	      =					sample.to(__magic_name__	)

         for t in scheduler.timesteps:
           _lowerCAmelCase	      =					scheduler.scale_model_input(__magic_name__   ,  __magic_name__	)

           _lowerCAmelCase	      =					model(__magic_name__   ,  __magic_name__	)

           _lowerCAmelCase	      =					scheduler.step(__magic_name__   ,  __magic_name__   ,  __magic_name__   ,  generator=__magic_name__	)
           _lowerCAmelCase	      =					output.prev_sample

         _lowerCAmelCase	      =					torch.sum(torch.abs(__magic_name__	)	)
         _lowerCAmelCase	      =					torch.mean(torch.abs(__magic_name__	)	)

         assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19	) < 1e-2
         assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63	) < 1e-3











import copy

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


A__      :     Tuple       =					logging.get_logger(__name__)
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			"""upernet"""




				def __init__(	self		:       Dict,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       str=512,    lowerCamelCase		:       Optional[Any]=0.02,    lowerCamelCase		:       Optional[Any]=[1, 2, 3, 6],    lowerCamelCase		:       Optional[int]=True,    lowerCamelCase		:       Tuple=0.4,    lowerCamelCase		:       Optional[int]=384,    lowerCamelCase		:       Optional[int]=256,    lowerCamelCase		:       Dict=1,    lowerCamelCase		:       str=False,    lowerCamelCase		:       List[str]=255,    **lowerCamelCase		:       List[Any],    ):

								'''simple docstring'''
								super().__init__(**lowerCamelCase    )

								if backbone_config is None:
												logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''    )
												lowercase__  					=     CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
								elif isinstance(lowerCamelCase,    lowerCamelCase    ):
												lowercase__  					=     backbone_config.get('''model_type'''    )
												lowercase__  					=     CONFIG_MAPPING[backbone_model_type]
												lowercase__  					=     config_class.from_dict(lowerCamelCase    )

								lowercase__  					=     backbone_config
								lowercase__  					=     hidden_size
								lowercase__  					=     initializer_range
								lowercase__  					=     pool_scales
								lowercase__  					=     use_auxiliary_head
								lowercase__  					=     auxiliary_loss_weight
								lowercase__  					=     auxiliary_in_channels
								lowercase__  					=     auxiliary_channels
								lowercase__  					=     auxiliary_num_convs
								lowercase__  					=     auxiliary_concat_input
								lowercase__  					=     loss_ignore_index




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__  					=     copy.deepcopy(self.__dict__    )
								lowercase__  					=     self.backbone_config.to_dict()
								lowercase__  					=     self.__class__.model_type
								return output




