




from typing import TYPE_CHECKING

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


UpperCamelCase__		=						{'configuration_yoso': ['YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YosoConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__		=						[
							    'YOSO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'YosoForMaskedLM',
							    'YosoForMultipleChoice',
							    'YosoForQuestionAnswering',
							    'YosoForSequenceClassification',
							    'YosoForTokenClassification',
							    'YosoLayer',
							    'YosoModel',
							    'YosoPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_yoso import (
														    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    YosoForMaskedLM,
														    YosoForMultipleChoice,
														    YosoForQuestionAnswering,
														    YosoForSequenceClassification,
														    YosoForTokenClassification,
														    YosoLayer,
														    YosoModel,
														    YosoPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase__		=						_LazyModule(__name__, globals()['__file__'], _import_structure)






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			(HeunDiscreteScheduler,)
					_lowerCamelCase		  =			10

					def      lowercase_							(    self			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							{
						    'num_train_timesteps': 1_100,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						}

						config.update(**__lowercase )
						return config

					def      lowercase_							(    self ):







						'''simple docstring'''




						for timesteps in [10, 50, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=__lowercase )

					def      lowercase_							(    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=__lowercase			,	beta_end=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




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

					def      lowercase_							(    self ):







						'''simple docstring'''




						for clip_sample_range in [1.0, 2.0, 3.0]:
							self.check_over_configs(clip_sample_range=__lowercase			,	clip_sample=__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




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

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		List[str]							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Union[str, Any]							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps )

						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[int]							       =							self.dummy_sample_deter * scheduler.init_noise_sigma
						lowerCamelCase_							:		Dict							       =							sample.to(__lowercase )

						for i, t in enumerate(scheduler.timesteps ):
							lowerCamelCase_							:		Any							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		int							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Optional[int]							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Tuple							       =							output.prev_sample

						lowerCamelCase_							:		str							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Tuple							       =							torch.mean(torch.abs(__lowercase ) )

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		str							       =							self.get_scheduler_config(prediction_type='v_prediction' )
						lowerCamelCase_							:		Optional[Any]							       =							scheduler_class(**__lowercase )

						scheduler.set_timesteps(self.num_inference_steps )

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

						for i, t in enumerate(scheduler.timesteps ):
							lowerCamelCase_							:		int							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		int							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Dict							       =							output.prev_sample

						lowerCamelCase_							:		Any							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		List[Any]							       =							torch.mean(torch.abs(__lowercase ) )

						if torch_device in ["cpu", "mps"]:
							assert abs(result_sum.item() - 4.6934E-07 ) < 1E-2
							assert abs(result_mean.item() - 6.1112E-10 ) < 1E-3
						else:
							# CUDA
							assert abs(result_sum.item() - 4.693428650170972E-07 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		int							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Dict							       =							scheduler_class(**__lowercase )

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

						lowerCamelCase_							:		str							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[int]							       =							self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
							lowerCamelCase_							:		Tuple							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		List[Any]							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Union[str, Any]							       =							output.prev_sample

						lowerCamelCase_							:		Dict							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		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() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3
						elif str(__lowercase ).startswith('mps' ):
							# Larger tolerance on mps
							assert abs(result_mean.item() - 0.00_02 ) < 1E-2
						else:
							# CUDA
							assert abs(result_sum.item() - 0.12_33 ) < 1E-2
							assert abs(result_mean.item() - 0.00_02 ) < 1E-3





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.scheduler_classes[0]
						lowerCamelCase_							:		Optional[Any]							       =							self.get_scheduler_config()
						lowerCamelCase_							:		Dict							       =							scheduler_class(**__lowercase			,	use_karras_sigmas=__lowercase )

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

						lowerCamelCase_							:		int							       =							self.dummy_model()
						lowerCamelCase_							:		Optional[Any]							       =							self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma
						lowerCamelCase_							:		Tuple							       =							sample.to(__lowercase )

						for t in scheduler.timesteps:
							lowerCamelCase_							:		str							       =							scheduler.scale_model_input(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Tuple							       =							model(__lowercase			,	__lowercase )

							lowerCamelCase_							:		Dict							       =							scheduler.step(__lowercase			,	__lowercase			,	__lowercase )
							lowerCamelCase_							:		Dict							       =							output.prev_sample

						lowerCamelCase_							:		Tuple							       =							torch.sum(torch.abs(__lowercase ) )
						lowerCamelCase_							:		Optional[Any]							       =							torch.mean(torch.abs(__lowercase ) )

						assert abs(result_sum.item() - 0.0_00_15 ) < 1E-2
						assert abs(result_mean.item() - 1.9869554535034695E-07 ) < 1E-2
