





'''simple docstring'''




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput


class 					SCREAMING_SNAKE_CASE       (a__				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          self.register_modules(unet=_UpperCAmelCase		,      scheduler=_UpperCAmelCase)


     @torch.no_grad()
     def __call__(		self		,      _UpperCAmelCase = 1		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 50		,      _UpperCAmelCase = "pil"		,      _UpperCAmelCase = True		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					Tuple						=			torch.randn(
              (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=_UpperCAmelCase		,      )
          __A		:					Union[str, Any]						=			image.to(self.device)

          # set step values
          self.scheduler.set_timesteps(_UpperCAmelCase)

          for t in self.progress_bar(self.scheduler.timesteps):
               # 1. predict noise model_output
               __A		:					Union[str, Any]						=			self.unet(_UpperCAmelCase		,      _UpperCAmelCase).sample

               # 2. predict previous mean of image x_t-1 and add variance depending on eta
               # eta corresponds to η in paper and should be between [0, 1]
               # do x_t -> x_t-1
               __A		:					List[str]						=			self.scheduler.step(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase).prev_sample

          __A		:					Tuple						=			(image / 2 + 0.5).clamp(0		,      1)
          __A		:					Optional[int]						=			image.cpu().permute(0		,      2		,      3		,      1).numpy()
          if output_type == "pil":
               __A		:					Dict						=			self.numpy_to_pil(_UpperCAmelCase)

          if not return_dict:
               return (image,), "This is a local test"

          return ImagePipelineOutput(images=_UpperCAmelCase), "This is a local test"






'''simple docstring'''

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							(boundary[1] - boundary[0]) / steps
   __SCREAMING_SNAKE_CASE   				=							boundary[0]
   __SCREAMING_SNAKE_CASE   				=							boundary[1]
   __SCREAMING_SNAKE_CASE   				=							make_points(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							0.0
   y += (h / 2.0) * f(__UpperCAmelCase   )
   for i in x_i:
      # print(i)
      y += h * f(__UpperCAmelCase   )
   y += (h / 2.0) * f(__UpperCAmelCase   )
   return y

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							a + h
   while x < (b - h):
      yield x
      __SCREAMING_SNAKE_CASE   				=							x + h

def     __magic_name__							(				__UpperCAmelCase   )    ->       int:  # enter your function here

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							(x - 0) * (x - 0)
   return y

def     __magic_name__							(				)    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							0.0  # Lower bound of integration
   __SCREAMING_SNAKE_CASE   				=							1.0  # Upper bound of integration
   __SCREAMING_SNAKE_CASE   				=							1_0.0  # define number of steps or resolution
   __SCREAMING_SNAKE_CASE   				=							[a, b]  # define boundary of integration
   __SCREAMING_SNAKE_CASE   				=							method_a(__UpperCAmelCase				,						__UpperCAmelCase   )
   print(f"""y = {y}"""   )


if __name__ == "__main__":
    main()





