
# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				super().__init__()
				self.register_modules(unet=A_				,      scheduler=A_       )



		@torch.no_grad()
		def __call__( self				,      A_ = 1				,      A_ = None				,      A_ = 50				,      A_ = "pil"				,      A_ = True				,      **A_				,      )  ->					Union[ImagePipelineOutput, Tuple]:
				"""simple docstring"""
				UpperCamelCase		=     torch.randn(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,      generator=A_				,      )
				UpperCamelCase		=     image.to(self.device       )

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

				for t in self.progress_bar(self.scheduler.timesteps       ):
						# 1. predict noise model_output
						UpperCamelCase		=     self.unet(A_				,      A_       ).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
						UpperCamelCase		=     self.scheduler.step(A_				,      A_				,      A_       ).prev_sample

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

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

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


from random import randint
from tempfile import TemporaryFile

import numpy as np


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  str:
		'''simple docstring'''

		UpperCamelCase		=     0
		if start < end:
				UpperCamelCase		=     randint(lowercase    ,       lowercase )
				UpperCamelCase		=     a[end]
				UpperCamelCase		=     a[pivot]
				UpperCamelCase		=     temp

				UpperCamelCase	,  UpperCamelCase		=     _in_place_partition(lowercase    ,       lowercase    ,       lowercase )
				count += _in_place_quick_sort(lowercase    ,       lowercase    ,       p - 1 )
				count += _in_place_quick_sort(lowercase    ,       p + 1    ,       lowercase )
		return count


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     0
		UpperCamelCase		=     randint(lowercase    ,       lowercase )
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     a[pivot]
		UpperCamelCase		=     temp
		UpperCamelCase		=     start - 1
		for index in range(lowercase    ,       lowercase ):
				count += 1
				if a[index] < a[end]:  # check if current val is less than pivot value
						UpperCamelCase		=     new_pivot_index + 1
						UpperCamelCase		=     a[new_pivot_index]
						UpperCamelCase		=     a[index]
						UpperCamelCase		=     temp

		UpperCamelCase		=     a[new_pivot_index + 1]
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     temp
		return new_pivot_index + 1, count


_UpperCAmelCase					: Union[str, Any]      =       TemporaryFile()
_UpperCAmelCase					: List[Any]      =       100  # 1000 elements are to be sorted


_UpperCAmelCase   ,_UpperCAmelCase					: Any      =       0, 1  # mean and standard deviation
_UpperCAmelCase					: Any      =       np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
_UpperCAmelCase					: Any      =       np.load(outfile)
_UpperCAmelCase					: str      =       len(M) - 1
_UpperCAmelCase					: List[str]      =       _in_place_quick_sort(M, 0, r)

print(
    "No of Comparisons for 100 elements selected from a standard normal distribution"
    "is :"
)


print(z)
