

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:UNetaDModel
     UpperCamelCase_	:KarrasVeScheduler






     def __init__(				self		,						_lowercase		,						_lowercase	)->     Optional[int]:
         super().__init__()
         self.register_modules(unet=_lowercase		,						scheduler=_lowercase	)






     @torch.no_grad()
     def __call__(				self		,						_lowercase = 1		,						_lowercase = 50		,						_lowercase = None		,						_lowercase = "pil"		,						_lowercase = True		,						**_lowercase		,						)->     Union[Tuple, ImagePipelineOutput]:
         UpperCamelCase_			  =						self.unet.config.sample_size
         UpperCamelCase_			  =						(batch_size, 3, img_size, img_size)

         UpperCamelCase_			  =						self.unet

         # sample x_0 ~ N(0, sigma_0^2 * I)
         UpperCamelCase_			  =						randn_tensor(_lowercase		,						generator=_lowercase		,						device=self.device	) * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(_lowercase	)

         for t in self.progress_bar(self.scheduler.timesteps	):
             # here sigma_t == t_i from the paper
             UpperCamelCase_			  =						self.scheduler.schedule[t]
             UpperCamelCase_			  =						self.scheduler.schedule[t - 1] if t > 0 else 0

             # 1. Select temporarily increased noise level sigma_hat
             # 2. Add new noise to move from sample_i to sample_hat
             UpperCamelCase_  ,       UpperCamelCase_			  =						self.scheduler.add_noise_to_input(_lowercase		,						_lowercase		,						generator=_lowercase	)

             # 3. Predict the noise residual given the noise magnitude `sigma_hat`
             # The model inputs and output are adjusted by following eq. (213) in [1].
             UpperCamelCase_			  =						(sigma_hat / 2) * model((sample_hat + 1) / 2		,						sigma_hat / 2	).sample

             # 4. Evaluate dx/dt at sigma_hat
             # 5. Take Euler step from sigma to sigma_prev
             UpperCamelCase_			  =						self.scheduler.step(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)

             if sigma_prev != 0:
                 # 6. Apply 2nd order correction
                 # The model inputs and output are adjusted by following eq. (213) in [1].
                 UpperCamelCase_			  =						(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2		,						sigma_prev / 2	).sample
                 UpperCamelCase_			  =						self.scheduler.step_correct(
                     _lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						step_output.prev_sample		,						step_output["derivative"]		,						)
             UpperCamelCase_			  =						step_output.prev_sample

         UpperCamelCase_			  =						(sample / 2 + 0.5).clamp(0		,						1	)
         UpperCamelCase_			  =						sample.cpu().permute(0		,						2		,						3		,						1	).numpy()
         if output_type == "pil":
             UpperCamelCase_			  =						self.numpy_to_pil(_lowercase	)

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=_lowercase	)



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 4_0_0_0_0_0_0			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[0, 1]
    UpperCamelCase_			  =						0
    while fib[i] <= n:
        fib.append(fib[i] + fib[i + 1]			)
        if fib[i + 2] > n:
            break
        i += 1
    UpperCamelCase_			  =						0
    for j in range(len(SCREAMING_SNAKE_CASE_			) - 1			):
        if fib[j] % 2 == 0:
            total += fib[j]

    return total


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