


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class    _a				(							snake_case__):




	def __init__(   self		:						Any						,   _SCREAMING_SNAKE_CASE		:						List[Any]						,   _SCREAMING_SNAKE_CASE		:						Tuple						)->			Union[str, Any]:
			super().__init__()
			self.register_modules(unet=_A						,   scheduler=_A						)




	@torch.no_grad()
	def __call__(   self		:						int						,   _SCREAMING_SNAKE_CASE		:						Dict = 1						,   _SCREAMING_SNAKE_CASE		:						Any = None						,   _SCREAMING_SNAKE_CASE		:						Union[str, Any] = 50						,   _SCREAMING_SNAKE_CASE		:						Optional[int] = "pil"						,   _SCREAMING_SNAKE_CASE		:						Dict = True						,   **_SCREAMING_SNAKE_CASE		:						int						,   )->			Tuple:
			lowerCAmelCase__  :     Any       	=   torch.randn(
			    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,   generator=_A						,   )
			lowerCAmelCase__  :     Optional[Any]       	=   image.to(self.device						)

			# set step values
			self.scheduler.set_timesteps(_A						)

			for t in self.progress_bar(self.scheduler.timesteps						):
					# 1. predict noise model_output
					lowerCAmelCase__  :     Union[str, Any]       	=   self.unet(_A						,   _A						).sample

					# 2. predict previous mean of image x_t-1 and add variance depending on eta
					# eta corresponds to η in paper and should be between [0, 1]
					# do x_t -> x_t-1
					lowerCAmelCase__  :     Union[str, Any]       	=   self.scheduler.step(_A						,   _A						,   _A						).prev_sample

			lowerCAmelCase__  :     Any       	=   (image / 2 + 0.5).clamp(0						,   1						)
			lowerCAmelCase__  :     List[Any]       	=   image.cpu().permute(0						,   2						,   3						,   1						).numpy()
			if output_type == "pil":
					lowerCAmelCase__  :     Dict       	=   self.numpy_to_pil(_A						)

			if not return_dict:
					return (image,), "This is a local test"

			return ImagePipelineOutput(images=_A						), "This is a local test"










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__            =     {"""configuration_vit_hybrid""": ["""VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTHybridConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     [
					    """VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ViTHybridForImageClassification""",
					    """ViTHybridModel""",
					    """ViTHybridPreTrainedModel""",
					]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""ViTHybridImageProcessor"""]


if TYPE_CHECKING:
					from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_hybrid import (
										    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTHybridForImageClassification,
										    ViTHybridModel,
										    ViTHybridPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
					import sys

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




