





'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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





class lowerCamelCase						(a__					):
		_lowercase  :	UNetaDModel
		_lowercase  :	ScoreSdeVeScheduler







		def __init__(		self     ,     lowercase__     ,     lowercase__		)		->						Optional[int]:






								"""simple docstring"""





								super().__init__()
								self.register_modules(unet=lowercase__     ,     scheduler=lowercase__		)







		@torch.no_grad()
		def __call__(		self     ,     lowercase__ = 1     ,     lowercase__ = 2_000     ,     lowercase__ = None     ,     lowercase__ = "pil"     ,     lowercase__ = True     ,     **lowercase__     ,     )		->						Union[ImagePipelineOutput, Tuple]:






								"""simple docstring"""





								_snake_case		:	Optional[int]					     =     self.unet.config.sample_size
								_snake_case		:	int					     =     (batch_size, 3, img_size, img_size)

								_snake_case		:	Dict					     =     self.unet

								_snake_case		:	List[str]					     =     randn_tensor(lowercase__     ,     generator=lowercase__		) * self.scheduler.init_noise_sigma
								_snake_case		:	Tuple					     =     sample.to(self.device		)

								self.scheduler.set_timesteps(lowercase__		)
								self.scheduler.set_sigmas(lowercase__		)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
														_snake_case		:	Dict					     =     self.scheduler.sigmas[i] * torch.ones(shape[0]     ,     device=self.device		)

														# correction step
														for _ in range(self.scheduler.config.correct_steps		):
																				_snake_case		:	Dict					     =     self.unet(lowercase__     ,     lowercase__		).sample
																				_snake_case		:	Dict					     =     self.scheduler.step_correct(lowercase__     ,     lowercase__     ,     generator=lowercase__		).prev_sample

														# prediction step
														_snake_case		:	int					     =     model(lowercase__     ,     lowercase__		).sample
														_snake_case		:	List[Any]					     =     self.scheduler.step_pred(lowercase__     ,     lowercase__     ,     lowercase__     ,     generator=lowercase__		)

														_snake_case		:	List[Any]					     =     output.prev_sample, output.prev_sample_mean

								_snake_case		:	List[str]					     =     sample_mean.clamp(0     ,     1		)
								_snake_case		:	List[Any]					     =     sample.cpu().permute(0     ,     2     ,     3     ,     1		).numpy()
								if output_type == "pil":
														_snake_case		:	Any					     =     self.numpy_to_pil(lowercase__		)

								if not return_dict:
														return (sample,)

								return ImagePipelineOutput(images=lowercase__		)









'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:		Dict		=							{'configuration_timm_backbone': ['TimmBackboneConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		Union[str, Any]		=							['TimmBackbone']


if TYPE_CHECKING:
   from .configuration_timm_backbone import TimmBackboneConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_timm_backbone import TimmBackbone

else:
   import sys

   UpperCAmelCase					:		Dict		=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


