
'''simple docstring'''
import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						(KDPMaDiscreteScheduler,)
		lowerCamelCase     							=						10



		def       lowerCAmelCase				(      self				:			List[Any]  ,	**UpperCamelCase__				:			Optional[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						{
				    '''num_train_timesteps''': 1100,
				    '''beta_start''': 0.0_001,
				    '''beta_end''': 0.02,
				    '''beta_schedule''': '''linear''',
				}

				config.update(**UpperCamelCase__      )
				return config



		def       lowerCAmelCase				(      self				:			List[str]      )						->			Optional[Any]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			str      )						->			int:






				"""simple docstring"""







				for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]  ,	[0.0_002, 0.002, 0.02]      ):
						self.check_over_configs(beta_start=UpperCamelCase__  ,	beta_end=UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[Any]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Tuple      )						->			Optional[int]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			int      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.scheduler_classes[0]
				snake_case  :       Optional[int]     			=						self.get_scheduler_config(prediction_type='''v_prediction'''      )
				snake_case  :       int     			=						scheduler_class(**UpperCamelCase__      )

				scheduler.set_timesteps(self.num_inference_steps      )

				snake_case  :       int     			=						self.dummy_model()
				snake_case  :       Dict     			=						self.dummy_sample_deter * scheduler.init_noise_sigma
				snake_case  :       Optional[int]     			=						sample.to(UpperCamelCase__      )

				for i, t in enumerate(scheduler.timesteps      ):
						snake_case  :       Tuple     			=						scheduler.scale_model_input(UpperCamelCase__  ,	UpperCamelCase__      )

						snake_case  :       str     			=						model(UpperCamelCase__  ,	UpperCamelCase__      )

						snake_case  :       Any     			=						scheduler.step(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
						snake_case  :       int     			=						output.prev_sample

				snake_case  :       Optional[int]     			=						torch.sum(torch.abs(UpperCamelCase__      )      )
				snake_case  :       Union[str, Any]     			=						torch.mean(torch.abs(UpperCamelCase__      )      )

				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() - 0.0_002      ) < 1e-3



		def       lowerCAmelCase				(      self				:			str      )						->			Optional[int]:






				"""simple docstring"""







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

				scheduler.set_timesteps(self.num_inference_steps      )

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

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

						snake_case  :       List[str]     			=						model(UpperCamelCase__  ,	UpperCamelCase__      )

						snake_case  :       Union[str, Any]     			=						scheduler.step(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
						snake_case  :       str     			=						output.prev_sample

				snake_case  :       int     			=						torch.sum(torch.abs(UpperCamelCase__      )      )
				snake_case  :       Tuple     			=						torch.mean(torch.abs(UpperCamelCase__      )      )

				if torch_device in ["cpu", "mps"]:
						assert abs(result_sum.item() - 20.4_125      ) < 1e-2
						assert abs(result_mean.item() - 0.0_266      ) < 1e-3
				else:
						# CUDA
						assert abs(result_sum.item() - 20.4_125      ) < 1e-2
						assert abs(result_mean.item() - 0.0_266      ) < 1e-3



		def       lowerCAmelCase				(      self				:			Any      )						->			Tuple:






				"""simple docstring"""







				if torch_device == "mps":
						return
				snake_case  :       str     			=						self.scheduler_classes[0]
				snake_case  :       str     			=						self.get_scheduler_config()
				snake_case  :       List[str]     			=						scheduler_class(**UpperCamelCase__      )

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

				snake_case  :       int     			=						self.dummy_model()
				snake_case  :       List[Any]     			=						self.dummy_sample_deter.to(UpperCamelCase__      ) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
						snake_case  :       str     			=						scheduler.scale_model_input(UpperCamelCase__  ,	UpperCamelCase__      )

						snake_case  :       int     			=						model(UpperCamelCase__  ,	UpperCamelCase__      )

						snake_case  :       List[str]     			=						scheduler.step(UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
						snake_case  :       Tuple     			=						output.prev_sample

				snake_case  :       Any     			=						torch.sum(torch.abs(UpperCamelCase__      )      )
				snake_case  :       Dict     			=						torch.mean(torch.abs(UpperCamelCase__      )      )

				if str(UpperCamelCase__      ).startswith('''cpu'''      ):
						# The following sum varies between 148 and 156 on mps. Why?
						assert abs(result_sum.item() - 20.4_125      ) < 1e-2
						assert abs(result_mean.item() - 0.0_266      ) < 1e-3
				else:
						# CUDA
						assert abs(result_sum.item() - 20.4_125      ) < 1e-2
						assert abs(result_mean.item() - 0.0_266      ) < 1e-3




'''simple docstring'''
from typing import TYPE_CHECKING

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


lowercase__						       =      {
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["PoolFormerFeatureExtractor"]
						lowercase__						       =      ["PoolFormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      [
						    "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PoolFormerForImageClassification",
						    "PoolFormerModel",
						    "PoolFormerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_poolformer import (
						    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    PoolFormerConfig,
						    PoolFormerOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_poolformer import PoolFormerFeatureExtractor
												from .image_processing_poolformer import PoolFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_poolformer import (
												    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PoolFormerForImageClassification,
												    PoolFormerModel,
												    PoolFormerPreTrainedModel,
												)


else:
						import sys

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure)


