


def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[Any]:




       '''simple docstring'''






       _lowercase					:				Dict           =			(boundary[1] - boundary[0]) / steps
       _lowercase					:				str           =			boundary[0]
       _lowercase					:				List[Any]           =			boundary[1]
       _lowercase					:				str           =			make_points(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
       _lowercase					:				Optional[Any]           =			0.0
       y += (h / 3.0) * f(UpperCAmelCase_			)
       _lowercase					:				Optional[int]           =			2
       for i in x_i:
              y += (h / 3) * (4 - 2 * (cnt % 2)) * f(UpperCAmelCase_			)
              cnt += 1
       y += (h / 3.0) * f(UpperCAmelCase_			)
       return y

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[Any]:




       '''simple docstring'''






       _lowercase					:				Union[str, Any]           =			a + h
       while x < (b - h):
              yield x
              _lowercase					:				Optional[Any]           =			x + h

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							List[Any]:  # enter your function here




       '''simple docstring'''






       _lowercase					:				Tuple           =			(x - 0) * (x - 0)
       return y

def   UpperCamelCase__   (    )						->							str:




       '''simple docstring'''






       _lowercase					:				int           =			0.0  # Lower bound of integration
       _lowercase					:				int           =			1.0  # Upper bound of integration
       _lowercase					:				int           =			10.0  # define number of steps or resolution
       _lowercase					:				Optional[Any]           =			[a, b]  # define boundary of integration
       _lowercase					:				Optional[int]           =			method_a(UpperCAmelCase_    ,     UpperCAmelCase_			)
       print(F'y = {y}'			)


if __name__ == "__main__":
   main()



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''




     def __init__( self       :       List[str]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       int		):





            """simple docstring"""



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




     @torch.no_grad()
     def __call__( self       :       Any	,      UpperCamelCase       :       int = 1	,      UpperCamelCase       :       Optional[torch.Generator] = None	,      UpperCamelCase       :       int = 50	,      UpperCamelCase       :       Optional[str] = "pil"	,      UpperCamelCase       :       bool = True	,      **UpperCamelCase       :       int	,      ):





            """simple docstring"""



            _lowercase					:				Optional[int]           =			torch.randn(
                (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,      generator=UpperCamelCase	,      )
            _lowercase					:				Dict           =			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
                   _lowercase					:				Dict           =			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
                   _lowercase					:				Tuple           =			self.scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample

            _lowercase					:				Optional[int]           =			(image / 2 + 0.5).clamp(0	,      1		)
            _lowercase					:				int           =			image.cpu().permute(0	,      2	,      3	,      1		).numpy()
            if output_type == "pil":
                   _lowercase					:				List[Any]           =			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"