




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    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: int						     =    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: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

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






from __future__ import annotations

from pathlib import Path


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		int:





  """simple docstring"""





  return pointa[0] * pointa[1] - pointa[1] * pointa[0]


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		bool:





  """simple docstring"""





  _lowercase: tuple[int, int]						     =    (xa, ya)
  _lowercase: tuple[int, int]						     =    (xa - xa, ya - ya)
  _lowercase: tuple[int, int]						     =    (xa - xa, ya - ya)
  _lowercase: float						     =    -vector_product(UpperCamelCase					,     UpperCamelCase							) / vector_product(
      UpperCamelCase					,     UpperCamelCase							)
  _lowercase: float						     =    +vector_product(UpperCamelCase					,     UpperCamelCase							) / vector_product(
      UpperCamelCase					,     UpperCamelCase							)

  return a > 0 and b > 0 and a + b < 1


def 		_lowerCamelCase				(					UpperCamelCase = "p102_triangles.txt"							)			->		int:





  """simple docstring"""





  _lowercase: str						     =    Path(UpperCamelCase							).parent.joinpath(UpperCamelCase							).read_text(encoding='utf-8'							)

  _lowercase: list[list[int]]						     =    []
  for line in data.strip().split('\n'							):
    triangles.append([int(UpperCamelCase							) for number in line.split(','							)]							)

  _lowercase: int						     =    0
  _lowercase: list[int]

  for triangle in triangles:
    ret += contains_origin(*UpperCamelCase							)

  return ret


if __name__ == "__main__":
   print(f'''{solution() = }''')
