




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class     __lowerCAmelCase (       _snake_case ):

			"""simple docstring"""







			snake_case_   		=       (DDIMParallelScheduler,)
			snake_case_   		=       (('''eta''', 0.0), ('''num_inference_steps''', 50))


			def       lowercase_    (      self			,						**lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    {
							    'num_train_timesteps': 1_000,
							    'beta_start': 0.00_01,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'clip_sample': True,
							}

							config.update(**_UpperCamelCase							)
							return config


			def       lowercase_    (      self			,						**lowerCamelCase__							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    self.scheduler_classes[0]
							__lowerCamelCase		       =    self.get_scheduler_config(**_UpperCamelCase							)
							__lowerCamelCase		       =    scheduler_class(**_UpperCamelCase							)

							__lowerCamelCase		       =    10, 0.0

							__lowerCamelCase		       =    self.dummy_model()
							__lowerCamelCase		       =    self.dummy_sample_deter

							scheduler.set_timesteps(_UpperCamelCase							)

							for t in scheduler.timesteps:
											__lowerCamelCase		       =    model(_UpperCamelCase			,						_UpperCamelCase							)
											__lowerCamelCase		       =    scheduler.step(_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase							).prev_sample

							return sample


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							for timesteps in [100, 500, 1_000]:
											self.check_over_configs(num_train_timesteps=_UpperCamelCase							)


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=_UpperCamelCase							)

							__lowerCamelCase		       =    self.scheduler_classes[0]
							__lowerCamelCase		       =    self.get_scheduler_config(steps_offset=1							)
							__lowerCamelCase		       =    scheduler_class(**_UpperCamelCase							)
							scheduler.set_timesteps(5							)
							assert torch.equal(scheduler.timesteps			,						torch.LongTensor([801, 601, 401, 201, 1]							)							)


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



							'''simple docstring'''
							for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,						[0.0_02, 0.02, 0.2, 2]							):
											self.check_over_configs(beta_start=_UpperCamelCase			,						beta_end=_UpperCamelCase							)


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=_UpperCamelCase							)


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=_UpperCamelCase							)


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=_UpperCamelCase							)


			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=_UpperCamelCase							)


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



							'''simple docstring'''
							for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=_UpperCamelCase							)


			def       lowercase_    (      self							)    ->      str:



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


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							for t in [1, 10, 49]:
											self.check_over_forward(time_step=_UpperCamelCase							)


			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							for t, num_inference_steps in zip([1, 10, 50]			,						[10, 50, 500]							):
											self.check_over_forward(time_step=_UpperCamelCase			,						num_inference_steps=_UpperCamelCase							)


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



							'''simple docstring'''
							for t, eta in zip([1, 10, 49]			,						[0.0, 0.5, 1.0]							):
											self.check_over_forward(time_step=_UpperCamelCase			,						eta=_UpperCamelCase							)


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



							'''simple docstring'''
							__lowerCamelCase		       =    self.scheduler_classes[0]
							__lowerCamelCase		       =    self.get_scheduler_config()
							__lowerCamelCase		       =    scheduler_class(**_UpperCamelCase							)

							assert torch.sum(torch.abs(scheduler._get_variance(0			,						0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420			,						400							) - 0.1_47_71							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980			,						960							) - 0.3_24_60							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0			,						0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487			,						486							) - 0.0_09_79							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999			,						998							) - 0.02							)							) < 1e-5


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    self.scheduler_classes[0]
							__lowerCamelCase		       =    self.get_scheduler_config()
							__lowerCamelCase		       =    scheduler_class(**_UpperCamelCase							)

							__lowerCamelCase		       =    10, 0.0
							scheduler.set_timesteps(_UpperCamelCase							)

							__lowerCamelCase		       =    self.dummy_model()
							__lowerCamelCase		       =    self.dummy_sample_deter
							__lowerCamelCase		       =    self.dummy_sample_deter + 0.1
							__lowerCamelCase		       =    self.dummy_sample_deter - 0.1

							__lowerCamelCase		       =    samplea.shape[0]
							__lowerCamelCase		       =    torch.stack([samplea, samplea, samplea]			,						dim=0							)
							__lowerCamelCase		       =    torch.arange(_UpperCamelCase							)[0:3, None].repeat(1			,						_UpperCamelCase							)

							__lowerCamelCase		       =    model(samples.flatten(0			,						1							)			,						timesteps.flatten(0			,						1							)							)
							__lowerCamelCase		       =    scheduler.batch_step_no_noise(_UpperCamelCase			,						timesteps.flatten(0			,						1							)			,						samples.flatten(0			,						1							)			,						_UpperCamelCase							)

							__lowerCamelCase		       =    torch.sum(torch.abs(_UpperCamelCase							)							)
							__lowerCamelCase		       =    torch.mean(torch.abs(_UpperCamelCase							)							)

							assert abs(result_sum.item() - 1_147.7_904							) < 1e-2
							assert abs(result_mean.item() - 0.49_82							) < 1e-3


			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    self.full_loop()

							__lowerCamelCase		       =    torch.sum(torch.abs(_UpperCamelCase							)							)
							__lowerCamelCase		       =    torch.mean(torch.abs(_UpperCamelCase							)							)

							assert abs(result_sum.item() - 172.0_067							) < 1e-2
							assert abs(result_mean.item() - 0.22_39_67							) < 1e-3


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.full_loop(prediction_type='v_prediction'							)

							__lowerCamelCase		       =    torch.sum(torch.abs(_UpperCamelCase							)							)
							__lowerCamelCase		       =    torch.mean(torch.abs(_UpperCamelCase							)							)

							assert abs(result_sum.item() - 52.53_02							) < 1e-2
							assert abs(result_mean.item() - 0.06_84							) < 1e-3


			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							# We specify different beta, so that the first alpha is 0.99
							__lowerCamelCase		       =    self.full_loop(set_alpha_to_one=_UpperCamelCase			,						beta_start=0.01							)
							__lowerCamelCase		       =    torch.sum(torch.abs(_UpperCamelCase							)							)
							__lowerCamelCase		       =    torch.mean(torch.abs(_UpperCamelCase							)							)

							assert abs(result_sum.item() - 149.8_295							) < 1e-2
							assert abs(result_mean.item() - 0.19_51							) < 1e-3






			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							# We specify different beta, so that the first alpha is 0.99
							__lowerCamelCase		       =    self.full_loop(set_alpha_to_one=_UpperCamelCase			,						beta_start=0.01							)
							__lowerCamelCase		       =    torch.sum(torch.abs(_UpperCamelCase							)							)
							__lowerCamelCase		       =    torch.mean(torch.abs(_UpperCamelCase							)							)

							assert abs(result_sum.item() - 149.0_784							) < 1e-2
							assert abs(result_mean.item() - 0.19_41							) < 1e-3










import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__UpperCAmelCase								=						logging.get_logger(__name__)



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		*_UpperCamelCase				,		**_UpperCamelCase   )	->							None:
							warnings.warn(
							    'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use ImageGPTImageProcessor instead.'				,		_UpperCamelCase				,		)
							super().__init__(*_UpperCamelCase				,		**_UpperCamelCase   )






