






'''simple docstring'''

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      List[str]        =  (KDPMaDiscreteScheduler,)
	SCREAMING_SNAKE_CASE_					:      Dict        =  1_0
	def        lowerCamelCase__		(							self  ,      **_lowercase       )						->  List[Any]:
							lowercase_      :   str        =			{
							    'num_train_timesteps': 1100,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							}

							config.update(**_lowercase       )
							return config
	def        lowerCamelCase__		(							self       )						->  Dict:
							for timesteps in [10, 50, 100, 1000]:
													self.check_over_configs(num_train_timesteps=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  List[str]:
							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=_lowercase  ,      beta_end=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  int:
							for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Any:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Any:
							lowercase_      :   Dict        =			self.scheduler_classes[0]
							lowercase_      :   int        =			self.get_scheduler_config(prediction_type='v_prediction'       )
							lowercase_      :   Union[str, Any]        =			scheduler_class(**_lowercase       )

							scheduler.set_timesteps(self.num_inference_steps       )

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

							for i, t in enumerate(scheduler.timesteps       ):
													lowercase_      :   Optional[int]        =			scheduler.scale_model_input(_lowercase  ,      _lowercase       )

													lowercase_      :   Tuple        =			model(_lowercase  ,      _lowercase       )

													lowercase_      :   Any        =			scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase       )
													lowercase_      :   str        =			output.prev_sample

							lowercase_      :   Optional[Any]        =			torch.sum(torch.abs(_lowercase       )       )
							lowercase_      :   List[Any]        =			torch.mean(torch.abs(_lowercase       )       )

							if torch_device in ["cpu", "mps"]:
													assert abs(result_sum.item() - 4.6_9_3_4E-0_7       ) < 1E-2
													assert abs(result_mean.item() - 6.1_1_1_2E-1_0       ) < 1E-3
							else:
													# CUDA
													assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7       ) < 1E-2
													assert abs(result_mean.item() - 0.00_02       ) < 1E-3
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							if torch_device == "mps":
													return
							lowercase_      :   str        =			self.scheduler_classes[0]
							lowercase_      :   Tuple        =			self.get_scheduler_config()
							lowercase_      :   Tuple        =			scheduler_class(**_lowercase       )

							scheduler.set_timesteps(self.num_inference_steps       )

							lowercase_      :   Optional[int]        =			self.dummy_model()
							lowercase_      :   List[str]        =			self.dummy_sample_deter * scheduler.init_noise_sigma
							lowercase_      :   str        =			sample.to(_lowercase       )

							for i, t in enumerate(scheduler.timesteps       ):
													lowercase_      :   Dict        =			scheduler.scale_model_input(_lowercase  ,      _lowercase       )

													lowercase_      :   Union[str, Any]        =			model(_lowercase  ,      _lowercase       )

													lowercase_      :   List[Any]        =			scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase       )
													lowercase_      :   List[Any]        =			output.prev_sample

							lowercase_      :   Union[str, Any]        =			torch.sum(torch.abs(_lowercase       )       )
							lowercase_      :   str        =			torch.mean(torch.abs(_lowercase       )       )

							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        lowerCamelCase__		(							self       )						->  Dict:
							if torch_device == "mps":
													return
							lowercase_      :   str        =			self.scheduler_classes[0]
							lowercase_      :   Dict        =			self.get_scheduler_config()
							lowercase_      :   Optional[int]        =			scheduler_class(**_lowercase       )

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

							lowercase_      :   Dict        =			self.dummy_model()
							lowercase_      :   List[Any]        =			self.dummy_sample_deter.to(_lowercase       ) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
													lowercase_      :   Tuple        =			scheduler.scale_model_input(_lowercase  ,      _lowercase       )

													lowercase_      :   Union[str, Any]        =			model(_lowercase  ,      _lowercase       )

													lowercase_      :   Dict        =			scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase       )
													lowercase_      :   Union[str, Any]        =			output.prev_sample

							lowercase_      :   Any        =			torch.sum(torch.abs(_lowercase       )       )
							lowercase_      :   Optional[Any]        =			torch.mean(torch.abs(_lowercase       )       )

							if str(_lowercase       ).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








'''simple docstring'''

from typing import TYPE_CHECKING

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


A:      int  				=				{
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      Union[str, Any]  				=				[
						    "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TrajectoryTransformerModel",
						    "TrajectoryTransformerPreTrainedModel",
						    "load_tf_weights_in_trajectory_transformer",
						]


if TYPE_CHECKING:
						from .configuration_trajectory_transformer import (
						    TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TrajectoryTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trajectory_transformer import (
												    TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TrajectoryTransformerModel,
												    TrajectoryTransformerPreTrainedModel,
												    load_tf_weights_in_trajectory_transformer,
												)


else:
						import sys

						A:      int  				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
