





'''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 numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey







class 					_a :





     '''simple docstring'''



     def __init__(				self							,__a							,__a							,__a							)      ->				Tuple:
          if dst_width < 0 or dst_height < 0:
               raise ValueError("""Destination width/height should be > 0"""							)

          snake_case			:      str        =			img
          snake_case			:      str        =			img.shape[1]
          snake_case			:      Union[str, Any]        =			img.shape[0]
          snake_case			:      Optional[int]        =			dst_width
          snake_case			:      List[str]        =			dst_height

          snake_case			:      List[str]        =			self.src_w / self.dst_w
          snake_case			:      int        =			self.src_h / self.dst_h

          snake_case			:      List[Any]        =			(
              np.ones((self.dst_h, self.dst_w, 3)							,np.uinta							) * 255
          )







     def 			snake_case_      (				self							)      ->				int:
          for i in range(self.dst_h							):
               for j in range(self.dst_w							):
                    snake_case			:      Optional[Any]        =			self.img[self.get_y(__a							)][self.get_x(__a							)]







     def 			snake_case_      (				self							,__a							)      ->				int:
          return int(self.ratio_x * x							)







     def 			snake_case_      (				self							,__a							)      ->				int:
          return int(self.ratio_y * y							)


if __name__ == "__main__":
      lowercase,	lowercase      :   Optional[int]      =       800, 600
      lowercase      :   Union[str, Any]      =       imread("""image_data/lena.jpg""", 1)
      lowercase      :   int      =       NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          F"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
      )
      waitKey(0)
      destroyAllWindows()



