



'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__ :			Dict												=				{
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :			Any												=				[
						    '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''GraphormerForGraphClassification''',
						    '''GraphormerModel''',
						    '''GraphormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_graphormer import (
												    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GraphormerForGraphClassification,
												    GraphormerModel,
												    GraphormerPreTrainedModel,
												)


else:
						import sys

						UpperCamelCase__ :			str												=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




'''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