



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 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					UNetaDModel
				UpperCamelCase__				:					KarrasVeScheduler




				def __init__(						self					:						Dict,					__lowercase					:						UNetaDModel,					__lowercase					:						KarrasVeScheduler						):
							super().__init__()
							self.register_modules(unet=__lowercase,					scheduler=__lowercase						)




				@torch.no_grad()
				def __call__(						self					:						Optional[Any],					__lowercase					:						int = 1,					__lowercase					:						int = 50,					__lowercase					:						Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__lowercase					:						Optional[str] = "pil",					__lowercase					:						bool = True,					**__lowercase					:						int,					):
							lowercase__					  =				self.unet.config.sample_size
							lowercase__					  =				(batch_size, 3, img_size, img_size)

							lowercase__					  =				self.unet

							# sample x_0 ~ N(0, sigma_0^2 * I)
							lowercase__					  =				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
										lowercase__					  =				self.scheduler.schedule[t]
										lowercase__					  =				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
										lowercase__  ,       lowercase__					  =				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].
										lowercase__					  =				(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
										lowercase__					  =				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].
													lowercase__					  =				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2,					sigma_prev / 2						).sample
													lowercase__					  =				self.scheduler.step_correct(
													    __lowercase,					__lowercase,					__lowercase,					__lowercase,					step_output.prev_sample,					step_output["derivative"],					)
										lowercase__					  =				step_output.prev_sample

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

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=__lowercase						)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_								=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TimesformerModel""",
							    """TimesformerForVideoClassification""",
							    """TimesformerPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_timesformer import (
														    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TimesformerForVideoClassification,
														    TimesformerModel,
														    TimesformerPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




