import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class    _UpperCamelCase      (						lowercase_ ):


			"""simple docstring"""






			__a  : Dict			 =						(DDIMParallelScheduler,)
			__a  : List[Any]			 =						(('''eta''', 0.0), ('''num_inference_steps''', 50))





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							{
							    'num_train_timesteps': 10_00,
							    'beta_start': 0.0001,
							    'beta_end': 0.02,
							    'beta_schedule': 'linear',
							    'clip_sample': True,
							}

							config.update(**__UpperCamelCase						)
							return config





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config(**__UpperCamelCase						)
							__lowercase 			=							scheduler_class(**__UpperCamelCase						)

							__lowercase 			=							10, 0.0

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter

							scheduler.set_timesteps(__UpperCamelCase						)

							for t in scheduler.timesteps:
											__lowercase 			=							model(__UpperCamelCase      ,   __UpperCamelCase						)
											__lowercase 			=							scheduler.step(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						).prev_sample

							return sample





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							for timesteps in [1_00, 5_00, 10_00]:
											self.check_over_configs(num_train_timesteps=__UpperCamelCase						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


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

							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config(steps_offset=1						)
							__lowercase 			=							scheduler_class(**__UpperCamelCase						)
							scheduler.set_timesteps(5						)
							assert torch.equal(scheduler.timesteps      ,   torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]						)						)





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

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      ,   [0.002, 0.02, 0.2, 2]						):
											self.check_over_configs(beta_start=__UpperCamelCase      ,   beta_end=__UpperCamelCase						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''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        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							for t in [1, 10, 49]:
											self.check_over_forward(time_step=__UpperCamelCase						)





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

							'''simple docstring'''


							for t, num_inference_steps in zip([1, 10, 50]      ,   [10, 50, 5_00]						):
											self.check_over_forward(time_step=__UpperCamelCase      ,   num_inference_steps=__UpperCamelCase						)





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

							'''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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							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(4_20      ,   4_00						) - 0.1_4771						)						) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_80      ,   9_60						) - 0.3_2460						)						) < 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_87      ,   4_86						) - 0.0_0979						)						) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99      ,   9_98						) - 0.02						)						) < 1E-5





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

							'''simple docstring'''


							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**__UpperCamelCase						)

							__lowercase 			=							10, 0.0
							scheduler.set_timesteps(__UpperCamelCase						)

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

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

							__lowercase 			=							model(samples.flatten(0      ,   1						)      ,   timesteps.flatten(0      ,   1						)						)
							__lowercase 			=							scheduler.batch_step_no_noise(__UpperCamelCase      ,   timesteps.flatten(0      ,   1						)      ,   samples.flatten(0      ,   1						)      ,   __UpperCamelCase						)

							__lowercase 			=							torch.sum(torch.abs(__UpperCamelCase						)						)
							__lowercase 			=							torch.mean(torch.abs(__UpperCamelCase						)						)

							assert abs(result_sum.item() - 1147.7904						) < 1E-2
							assert abs(result_mean.item() - 0.4982						) < 1E-3





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

							'''simple docstring'''


							__lowercase 			=							self.full_loop()

							__lowercase 			=							torch.sum(torch.abs(__UpperCamelCase						)						)
							__lowercase 			=							torch.mean(torch.abs(__UpperCamelCase						)						)

							assert abs(result_sum.item() - 172.0067						) < 1E-2
							assert abs(result_mean.item() - 0.22_3967						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							self.full_loop(prediction_type='''v_prediction'''						)

							__lowercase 			=							torch.sum(torch.abs(__UpperCamelCase						)						)
							__lowercase 			=							torch.mean(torch.abs(__UpperCamelCase						)						)

							assert abs(result_sum.item() - 52.5302						) < 1E-2
							assert abs(result_mean.item() - 0.0684						) < 1E-3





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

							'''simple docstring'''


							__lowercase 			=							self.full_loop(set_alpha_to_one=__UpperCamelCase      ,   beta_start=0.01						)
							__lowercase 			=							torch.sum(torch.abs(__UpperCamelCase						)						)
							__lowercase 			=							torch.mean(torch.abs(__UpperCamelCase						)						)

							assert abs(result_sum.item() - 149.8295						) < 1E-2
							assert abs(result_mean.item() - 0.1951						) < 1E-3





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

							'''simple docstring'''


							__lowercase 			=							self.full_loop(set_alpha_to_one=__UpperCamelCase      ,   beta_start=0.01						)
							__lowercase 			=							torch.sum(torch.abs(__UpperCamelCase						)						)
							__lowercase 			=							torch.mean(torch.abs(__UpperCamelCase						)						)

							assert abs(result_sum.item() - 149.0784						) < 1E-2
							assert abs(result_mean.item() - 0.1941						) < 1E-3
"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			lowercase_  ):
   def __init__(			self     ,*__UpperCamelCase     ,**__UpperCamelCase			)	->      None:


        '''simple docstring'''


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

