





'''simple docstring'''


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''
       _lowerCamelCase								=(EulerDiscreteScheduler,)
       _lowerCamelCase								=10

       def 			__snake_case      ( self					:				str       ,     **a__					:				Tuple						):
              UpperCAmelCase  =						{
                  '''num_train_timesteps''': 1100,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
              }

              config.update(**a__						)
              return config

       def 			__snake_case      ( self					:				Dict						):
              for timesteps in [10, 50, 100, 1000]:
                     self.check_over_configs(num_train_timesteps=a__						)

       def 			__snake_case      ( self					:				Optional[int]						):
              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=a__       ,     beta_end=a__						)

       def 			__snake_case      ( self					:				List[str]						):
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=a__						)

       def 			__snake_case      ( self					:				int						):
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=a__						)

       def 			__snake_case      ( self					:				Tuple						):
              UpperCAmelCase  =						self.scheduler_classes[0]
              UpperCAmelCase  =						self.get_scheduler_config()
              UpperCAmelCase  =						scheduler_class(**a__						)

              scheduler.set_timesteps(self.num_inference_steps						)

              UpperCAmelCase  =						torch.manual_seed(0						)

              UpperCAmelCase  =						self.dummy_model()
              UpperCAmelCase  =						self.dummy_sample_deter * scheduler.init_noise_sigma
              UpperCAmelCase  =						sample.to(a__						)

              for i, t in enumerate(scheduler.timesteps						):
                     UpperCAmelCase  =						scheduler.scale_model_input(a__       ,     a__						)

                     UpperCAmelCase  =						model(a__       ,     a__						)

                     UpperCAmelCase  =						scheduler.step(a__       ,     a__       ,     a__       ,     generator=a__						)
                     UpperCAmelCase  =						output.prev_sample

              UpperCAmelCase  =						torch.sum(torch.abs(a__						)						)
              UpperCAmelCase  =						torch.mean(torch.abs(a__						)						)

              assert abs(result_sum.item() - 10.0_807						) < 1e-2
              assert abs(result_mean.item() - 0.0_131						) < 1e-3

       def 			__snake_case      ( self					:				str						):
              UpperCAmelCase  =						self.scheduler_classes[0]
              UpperCAmelCase  =						self.get_scheduler_config(prediction_type='''v_prediction'''						)
              UpperCAmelCase  =						scheduler_class(**a__						)

              scheduler.set_timesteps(self.num_inference_steps						)

              UpperCAmelCase  =						torch.manual_seed(0						)

              UpperCAmelCase  =						self.dummy_model()
              UpperCAmelCase  =						self.dummy_sample_deter * scheduler.init_noise_sigma
              UpperCAmelCase  =						sample.to(a__						)

              for i, t in enumerate(scheduler.timesteps						):
                     UpperCAmelCase  =						scheduler.scale_model_input(a__       ,     a__						)

                     UpperCAmelCase  =						model(a__       ,     a__						)

                     UpperCAmelCase  =						scheduler.step(a__       ,     a__       ,     a__       ,     generator=a__						)
                     UpperCAmelCase  =						output.prev_sample

              UpperCAmelCase  =						torch.sum(torch.abs(a__						)						)
              UpperCAmelCase  =						torch.mean(torch.abs(a__						)						)

              assert abs(result_sum.item() - 0.0_002						) < 1e-2
              assert abs(result_mean.item() - 2.2_6_7_6e-0_6						) < 1e-3

       def 			__snake_case      ( self					:				Optional[int]						):
              UpperCAmelCase  =						self.scheduler_classes[0]
              UpperCAmelCase  =						self.get_scheduler_config()
              UpperCAmelCase  =						scheduler_class(**a__						)

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

              UpperCAmelCase  =						torch.manual_seed(0						)

              UpperCAmelCase  =						self.dummy_model()
              UpperCAmelCase  =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              UpperCAmelCase  =						sample.to(a__						)

              for t in scheduler.timesteps:
                     UpperCAmelCase  =						scheduler.scale_model_input(a__       ,     a__						)

                     UpperCAmelCase  =						model(a__       ,     a__						)

                     UpperCAmelCase  =						scheduler.step(a__       ,     a__       ,     a__       ,     generator=a__						)
                     UpperCAmelCase  =						output.prev_sample

              UpperCAmelCase  =						torch.sum(torch.abs(a__						)						)
              UpperCAmelCase  =						torch.mean(torch.abs(a__						)						)

              assert abs(result_sum.item() - 10.0_807						) < 1e-2
              assert abs(result_mean.item() - 0.0_131						) < 1e-3



       def 			__snake_case      ( self					:				str						):
              UpperCAmelCase  =						self.scheduler_classes[0]
              UpperCAmelCase  =						self.get_scheduler_config()
              UpperCAmelCase  =						scheduler_class(**a__       ,     use_karras_sigmas=a__						)

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

              UpperCAmelCase  =						torch.manual_seed(0						)

              UpperCAmelCase  =						self.dummy_model()
              UpperCAmelCase  =						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
              UpperCAmelCase  =						sample.to(a__						)

              for t in scheduler.timesteps:
                     UpperCAmelCase  =						scheduler.scale_model_input(a__       ,     a__						)

                     UpperCAmelCase  =						model(a__       ,     a__						)

                     UpperCAmelCase  =						scheduler.step(a__       ,     a__       ,     a__       ,     generator=a__						)
                     UpperCAmelCase  =						output.prev_sample

              UpperCAmelCase  =						torch.sum(torch.abs(a__						)						)
              UpperCAmelCase  =						torch.mean(torch.abs(a__						)						)

              assert abs(result_sum.item() - 124.52_299_499_511_719						) < 1e-2
              assert abs(result_mean.item() - 0.16_213_932_633_399_963						) < 1e-3









'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


a__					:			int          =     logging.get_logger(__name__)






class 				__snake_case     (					__magic_name__							):





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



