


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		(DDIMScheduler,)
							_UpperCAmelCase     =		(("""eta""", 0.0), ("""num_inference_steps""", 5_0))






							def  UpperCamelCase__			(	self       ,		**lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= {
										    'num_train_timesteps': 1_0_0_0,
										    'beta_start': 0.0_001,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										    'clip_sample': True,
										}

										config.update(**lowerCAmelCase__    )
										return config






							def  UpperCamelCase__			(	self       ,		**lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.get_scheduler_config(**lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= scheduler_class(**lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Dict       						= 1_0, 0.0

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= self.dummy_model()
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.dummy_sample_deter

										scheduler.set_timesteps(lowerCAmelCase__    )

										for t in scheduler.timesteps:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(lowerCAmelCase__       ,		lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			Tuple       						= scheduler.step(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ).prev_sample

										return sample






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			Dict       						= self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE_						:			str       						= self.get_scheduler_config(steps_offset=1    )
										SCREAMING_SNAKE_CASE_						:			Any       						= scheduler_class(**lowerCAmelCase__    )
										scheduler.set_timesteps(5    )
										assert torch.equal(scheduler.timesteps       ,		torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]    )    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]       ,		[0.002, 0.02, 0.2, 2]    ):
													self.check_over_configs(beta_start=lowerCAmelCase__       ,		beta_end=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




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






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										self.check_over_configs(thresholding=lowerCAmelCase__    )
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=lowerCAmelCase__       ,		prediction_type=lowerCAmelCase__       ,		sample_max_value=lowerCAmelCase__       ,		)






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for t in [1, 1_0, 4_9]:
													self.check_over_forward(time_step=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for t, num_inference_steps in zip([1, 1_0, 5_0]       ,		[1_0, 5_0, 5_0_0]    ):
													self.check_over_forward(time_step=lowerCAmelCase__       ,		num_inference_steps=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for t, eta in zip([1, 1_0, 4_9]       ,		[0.0, 0.5, 1.0]    ):
													self.check_over_forward(time_step=lowerCAmelCase__       ,		eta=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE_						:			Any       						= self.get_scheduler_config()
										SCREAMING_SNAKE_CASE_						:			Dict       						= scheduler_class(**lowerCAmelCase__    )

										assert torch.sum(torch.abs(scheduler._get_variance(0       ,		0    ) - 0.0    )    ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_2_0       ,		4_0_0    ) - 0.14_771    )    ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_8_0       ,		9_6_0    ) - 0.32_460    )    ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(0       ,		0    ) - 0.0    )    ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ,		4_8_6    ) - 0.00_979    )    ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ,		9_9_8    ) - 0.02    )    ) < 1E-5






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= self.full_loop()

										SCREAMING_SNAKE_CASE_						:			List[str]       						= torch.sum(torch.abs(lowerCAmelCase__    )    )
										SCREAMING_SNAKE_CASE_						:			Any       						= torch.mean(torch.abs(lowerCAmelCase__    )    )

										assert abs(result_sum.item() - 172.0_067    ) < 1E-2
										assert abs(result_mean.item() - 0.223_967    ) < 1E-3






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Dict       						= self.full_loop(prediction_type='v_prediction'    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= torch.sum(torch.abs(lowerCAmelCase__    )    )
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= torch.mean(torch.abs(lowerCAmelCase__    )    )

										assert abs(result_sum.item() - 52.5_302    ) < 1E-2
										assert abs(result_mean.item() - 0.0_684    ) < 1E-3






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.full_loop(set_alpha_to_one=lowerCAmelCase__       ,		beta_start=0.01    )
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= torch.sum(torch.abs(lowerCAmelCase__    )    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= torch.mean(torch.abs(lowerCAmelCase__    )    )

										assert abs(result_sum.item() - 149.8_295    ) < 1E-2
										assert abs(result_mean.item() - 0.1_951    ) < 1E-3






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= self.full_loop(set_alpha_to_one=lowerCAmelCase__       ,		beta_start=0.01    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= torch.sum(torch.abs(lowerCAmelCase__    )    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= torch.mean(torch.abs(lowerCAmelCase__    )    )

										assert abs(result_sum.item() - 149.0_784    ) < 1E-2
										assert abs(result_mean.item() - 0.1_941    ) < 1E-3









import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


lowerCAmelCase__       :	int     				=logging.get_logger(__name__)







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def __init__(	self       ,		*lowerCAmelCase__       ,		**lowerCAmelCase__    ):





										"""simple docstring"""




										warnings.warn(
										    'The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
										    ' use ViltImageProcessor instead.'       ,		lowerCAmelCase__       ,		)
										super().__init__(*lowerCAmelCase__       ,		**lowerCAmelCase__    )





