





'''simple docstring'''


import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class  a_  (							SCREAMING_SNAKE_CASE__				):





     def __init__(					self						:   int      , lowercase						:   VQModel      , lowercase						:   UNetaDModel      , lowercase						:   DDIMScheduler		):





        """simple docstring"""




        super().__init__()
        self.register_modules(vqvae=snake_case__      , unet=snake_case__      , scheduler=snake_case__		)







     @torch.no_grad()
     def __call__(					self						:   int      , lowercase						:   int = 1      , lowercase						:   Optional[Union[torch.Generator, List[torch.Generator]]] = None      , lowercase						:   float = 0.0      , lowercase						:   int = 50      , lowercase						:   Optional[str] = "pil"      , lowercase						:   bool = True      , **lowercase						:   Dict      , ):





        """simple docstring"""




        lowercase_     :List[Any]            =					randn_tensor(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      , generator=snake_case__      , )
        lowercase_     :int            =					latents.to(self.device		)

        # scale the initial noise by the standard deviation required by the scheduler
        lowercase_     :Any            =					latents * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(snake_case__		)

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        lowercase_     :int            =					"eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)

        lowercase_     :Optional[int]            =					{}
        if accepts_eta:
           lowercase_     :Any            =					eta

        for t in self.progress_bar(self.scheduler.timesteps		):
           lowercase_     :Dict            =					self.scheduler.scale_model_input(snake_case__      , snake_case__		)
           # predict the noise residual
           lowercase_     :Dict            =					self.unet(snake_case__      , snake_case__		).sample
           # compute the previous noisy sample x_t -> x_t-1
           lowercase_     :Tuple            =					self.scheduler.step(snake_case__      , snake_case__      , snake_case__      , **snake_case__		).prev_sample

        # decode the image latents with the VAE
        lowercase_     :str            =					self.vqvae.decode(snake_case__		).sample

        lowercase_     :Any            =					(image / 2 + 0.5).clamp(0      , 1		)
        lowercase_     :Tuple            =					image.cpu().permute(0      , 2      , 3      , 1		).numpy()
        if output_type == "pil":
           lowercase_     :Dict            =					self.numpy_to_pil(snake_case__		)

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=snake_case__		)








from __future__ import annotations

import math


def 					_a							(					lowerCAmelCase )->	bool:

		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5    ,						int(math.sqrt(lowerCAmelCase ) + 1 )    ,						6 ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True


SCREAMING_SNAKE_CASE:     List[str]									= [num for num in range(3, 1_0_0_0_0_1, 2) if not is_prime(num)]


def 					_a							(					lowerCAmelCase )->	list[int]:
		if not isinstance(lowerCAmelCase    ,						lowerCAmelCase ):
				raise ValueError('n must be an integer' )
		if n <= 0:
				raise ValueError('n must be >= 0' )

		SCREAMING_SNAKE_CASE_					      =							[]
		for num in range(len(lowerCAmelCase ) ):
				SCREAMING_SNAKE_CASE_					      =							0
				while 2 * i * i <= odd_composites[num]:
						SCREAMING_SNAKE_CASE_					      =							odd_composites[num] - 2 * i * i
						if is_prime(lowerCAmelCase ):
								break
						i += 1
				else:
						list_nums.append(odd_composites[num] )
						if len(lowerCAmelCase ) == n:
								return list_nums

		return []


def 					_a							(					)->	int:
		return compute_nums(1 )[0]


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