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__		(					UpperCAmelCase_):
			UpperCamelCase__       = 42
			UpperCamelCase__       = 42







			def __init__(			self       :						Any       ,    lowercase_       :						List[str]       ,    lowercase_       :						Tuple					):
										super().__init__()
										self.register_modules(unet=_lowercase       ,    scheduler=_lowercase					)







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

										lowercase_      :    Union[str, Any]       		= self.unet

										# sample x_0 ~ N(0, sigma_0^2 * I)
										lowercase_      :    Dict       		= 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_      :    Any       		= self.scheduler.schedule[t]
																	lowercase_      :    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
																	lowercase_      :    Union[str, Any]       		= 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_      :    Optional[int]       		= (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_      :    Optional[Any]       		= 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_      :    Dict       		= (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,    sigma_prev / 2					).sample
																								lowercase_      :    Tuple       		= self.scheduler.step_correct(
																								    _lowercase       ,    _lowercase       ,    _lowercase       ,    _lowercase       ,    step_output.prev_sample       ,    step_output["""derivative"""]       ,    )
																	lowercase_      :    Dict       		= step_output.prev_sample

										lowercase_      :    Tuple       		= (sample / 2 + 0.5).clamp(0       ,    1					)
										lowercase_      :    Tuple       		= sample.cpu().permute(0       ,    2       ,    3       ,    1					).numpy()
										if output_type == "pil":
																	lowercase_      :    Optional[int]       		= self.numpy_to_pil(_lowercase					)

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=_lowercase					)






'''simple docstring'''




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase		:				List[Any]	       =		{"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase		:				int	       =		[
						    "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "FocalNetForImageClassification",
						    "FocalNetForMaskedImageModeling",
						    "FocalNetBackbone",
						    "FocalNetModel",
						    "FocalNetPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_focalnet import (
												    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FocalNetBackbone,
												    FocalNetForImageClassification,
												    FocalNetForMaskedImageModeling,
												    FocalNetModel,
												    FocalNetPreTrainedModel,
												)

else:
						import sys

						_lowercase		:				Union[str, Any]	       =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





