

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class    _lowercase				(					snake_case__			):
   _lowercase     :       Dict  					=    (KDPMaDiscreteScheduler,)
   _lowercase     :       List[str]  					=    10

   def     UpperCamelCase					(				self	:    Optional[Any]			,						**lowerCamelCase__	:    Any       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		{
             '''num_train_timesteps''': 1_1_0_0,
             '''beta_start''': 0.0001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**_UpperCamelCase       )
         return config

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			Any:
         """simple docstring"""





         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=_UpperCamelCase       )

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			str:
         """simple docstring"""





         for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]			,						[0.0002, 0.002, 0.02]       ):
               self.check_over_configs(beta_start=_UpperCamelCase			,						beta_end=_UpperCamelCase       )

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			str:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			str:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.scheduler_classes[0]
         A_							       =		self.get_scheduler_config(prediction_type='''v_prediction'''       )
         A_							       =		scheduler_class(**_UpperCamelCase       )

         scheduler.set_timesteps(self.num_inference_steps       )

         A_							       =		self.dummy_model()
         A_							       =		self.dummy_sample_deter * scheduler.init_noise_sigma
         A_							       =		sample.to(_UpperCamelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
               A_							       =		scheduler.scale_model_input(_UpperCamelCase			,						_UpperCamelCase       )

               A_							       =		model(_UpperCamelCase			,						_UpperCamelCase       )

               A_							       =		scheduler.step(_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase       )
               A_							       =		output.prev_sample

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

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 4.6934e-07       ) < 1e-2
               assert abs(result_mean.item() - 6.1112e-10       ) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 4.693_4286_5017_0972e-07       ) < 1e-2
               assert abs(result_mean.item() - 0.0002       ) < 1e-3

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Optional[Any]:
         """simple docstring"""





         if torch_device == "mps":
               return
         A_							       =		self.scheduler_classes[0]
         A_							       =		self.get_scheduler_config()
         A_							       =		scheduler_class(**_UpperCamelCase       )

         scheduler.set_timesteps(self.num_inference_steps       )

         A_							       =		self.dummy_model()
         A_							       =		self.dummy_sample_deter * scheduler.init_noise_sigma
         A_							       =		sample.to(_UpperCamelCase       )

         for i, t in enumerate(scheduler.timesteps       ):
               A_							       =		scheduler.scale_model_input(_UpperCamelCase			,						_UpperCamelCase       )

               A_							       =		model(_UpperCamelCase			,						_UpperCamelCase       )

               A_							       =		scheduler.step(_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase       )
               A_							       =		output.prev_sample

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

         if torch_device in ["cpu", "mps"]:
               assert abs(result_sum.item() - 20.4125       ) < 1e-2
               assert abs(result_mean.item() - 0.0266       ) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 20.4125       ) < 1e-2
               assert abs(result_mean.item() - 0.0266       ) < 1e-3





   def     UpperCamelCase					(				self	:    List[str]       ) ->			Optional[int]:
         """simple docstring"""





         if torch_device == "mps":
               return
         A_							       =		self.scheduler_classes[0]
         A_							       =		self.get_scheduler_config()
         A_							       =		scheduler_class(**_UpperCamelCase       )

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

         A_							       =		self.dummy_model()
         A_							       =		self.dummy_sample_deter.to(_UpperCamelCase       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
               A_							       =		scheduler.scale_model_input(_UpperCamelCase			,						_UpperCamelCase       )

               A_							       =		model(_UpperCamelCase			,						_UpperCamelCase       )

               A_							       =		scheduler.step(_UpperCamelCase			,						_UpperCamelCase			,						_UpperCamelCase       )
               A_							       =		output.prev_sample

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

         if str(_UpperCamelCase       ).startswith('''cpu'''       ):
               # The following sum varies between 148 and 156 on mps. Why?
               assert abs(result_sum.item() - 20.4125       ) < 1e-2
               assert abs(result_mean.item() - 0.0266       ) < 1e-3
         else:
               # CUDA
               assert abs(result_sum.item() - 20.4125       ) < 1e-2
               assert abs(result_mean.item() - 0.0266       ) < 1e-3








from PIL import Image







def 						lowerCamelCase__      ( __A    :Image		):


       """simple docstring"""




       __snake_case	,    __snake_case       	=							image.size
       __snake_case       	=							0
       __snake_case       	=							image.load()
       for i in range(__A		):
              for j in range(__A		):
                     __snake_case       	=							pixels[j, i]
                     mean += pixel
       mean //= width * height

       for j in range(__A		):
              for i in range(__A		):
                     __snake_case       	=							2_5_5 if pixels[i, j] > mean else 0
       return image


if __name__ == "__main__":
    UpperCamelCase__		 =     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
    image.save('''output_image_path''')

