

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        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  UNetaDModel
					UpperCAmelCase__:  KarrasVeScheduler






					def __init__(					self       ,			A__       ,			A__     ):
						super().__init__()
						self.register_modules(unet=A__       ,			scheduler=A__     )






					@torch.no_grad()
					def __call__(					self       ,			A__ = 1       ,			A__ = 50       ,			A__ = None       ,			A__ = "pil"       ,			A__ = True       ,			**A__       ,			):
						A__				:       List[Any]	  =						self.unet.config.sample_size
						A__				:       List[Any]	  =						(batch_size, 3, img_size, img_size)

						A__				:       Optional[int]	  =						self.unet

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

						self.scheduler.set_timesteps(A__     )

						for t in self.progress_bar(self.scheduler.timesteps     ):
							# here sigma_t == t_i from the paper
							A__				:       List[str]	  =						self.scheduler.schedule[t]
							A__				:       Optional[Any]	  =						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
							A__  ,  A__				:       Union[str, Any]	  =						self.scheduler.add_noise_to_input(A__       ,			A__       ,			generator=A__     )

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							A__				:       List[str]	  =						(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
							A__				:       List[Any]	  =						self.scheduler.step(A__       ,			A__       ,			A__       ,			A__     )

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

						A__				:       Tuple	  =						(sample / 2 + 0.5).clamp(0       ,			1     )
						A__				:       List[str]	  =						sample.cpu().permute(0       ,			2       ,			3       ,			1     ).numpy()
						if output_type == "pil":
							A__				:       List[Any]	  =						self.numpy_to_pil(A__     )

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=A__     )






A_			:					Union[str, Any]    			=     '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A_			:					str    			=     [{'type': 'code', 'content': INSTALL_CONTENT}]
A_			:					Any    			=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



