






from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		def __init__(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							Dict    ,_SCREAMING_SNAKE_CASE		:							Optional[int]		)     -> Dict:


						'''simple docstring'''
						super().__init__()
						self.register_modules(unet=_SCREAMING_SNAKE_CASE    ,scheduler=_SCREAMING_SNAKE_CASE		)







		@torch.no_grad()
		def __call__(	self		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							int = 1    ,_SCREAMING_SNAKE_CASE		:							Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,_SCREAMING_SNAKE_CASE		:							int = 1_0_0_0    ,_SCREAMING_SNAKE_CASE		:							Optional[str] = "pil"    ,_SCREAMING_SNAKE_CASE		:							bool = True    ,)     -> Union[ImagePipelineOutput, Tuple]:


						'''simple docstring'''
						# Sample gaussian noise to begin loop
						if isinstance(self.unet.config.sample_size    ,_SCREAMING_SNAKE_CASE		):
										A									=   (
										    batch_size,
										    self.unet.config.in_channels,
										    self.unet.config.sample_size,
										    self.unet.config.sample_size,
										)
						else:
										A									=   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

						if self.device.type == "mps":
										# randn does not work reproducibly on mps
										A									=   randn_tensor(_SCREAMING_SNAKE_CASE    ,generator=_SCREAMING_SNAKE_CASE		)
										A									=   image.to(self.device		)
						else:
										A									=   randn_tensor(_SCREAMING_SNAKE_CASE    ,generator=_SCREAMING_SNAKE_CASE    ,device=self.device		)

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

						for t in self.progress_bar(self.scheduler.timesteps		):
										# 1. predict noise model_output
										A									=   self.unet(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		).sample

										# 2. compute previous image: x_t -> x_t-1
										A									=   self.scheduler.step(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,generator=_SCREAMING_SNAKE_CASE		).prev_sample

						A									=   (image / 2 + 0.5).clamp(0    ,1		)
						A									=   image.cpu().permute(0    ,2    ,3    ,1		).numpy()
						if output_type == "pil":
										A									=   self.numpy_to_pil(_SCREAMING_SNAKE_CASE		)

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE		)













def   snake_case		(				UpperCAmelCase						:							Optional[int],    UpperCAmelCase						:							str ):
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				A									=   (boundary[1] - boundary[0]) / steps
				A									=   boundary[0]
				A									=   boundary[1]
				A									=   make_points(UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase )
				A									=   0.0
				y += (h / 3.0) * f(UpperCAmelCase )
				A									=   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   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							int,    UpperCAmelCase						:							List[Any] ):
				A									=   a + h
				while x < (b - h):
								yield x
								A									=   x + h







def   snake_case		(				UpperCAmelCase						:							str ):  # enter your function here
				A									=   (x - 0) * (x - 0)
				return y







def   snake_case		(				):
				A									=   0.0  # Lower bound of integration
				A									=   1.0  # Upper bound of integration
				A									=   10.0  # define number of steps or resolution
				A									=   [a, b]  # define boundary of integration
				A									=   method_a(UpperCAmelCase,    UpperCAmelCase )
				print(f'y = {y}' )


if __name__ == "__main__":
					main()





