


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




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




						@torch.no_grad()
						def __call__(   self				,							a_ = 1				,							a_ = None				,							a_ = 50				,							a_ = "pil"				,							a_ = True				,							**a_				,							):
							lowerCamelCase_			:							Optional[int]      =       torch.randn(
							    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)				,							generator=a_				,							)
							lowerCamelCase_			:							str      =       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_			:							Tuple      =       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_			:							Optional[int]      =       self.scheduler.step(a_				,							a_				,							a_     ).prev_sample

							lowerCamelCase_			:							List[str]      =       (image / 2 + 0.5).clamp(0				,							1     )
							lowerCamelCase_			:							List[str]      =       image.cpu().permute(0				,							2				,							3				,							1     ).numpy()
							if output_type == "pil":
								lowerCamelCase_			:							int      =       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


__magic_name__       					=      {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''DetrFeatureExtractor''']
							__magic_name__       					=      ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DetrForObjectDetection''',
							    '''DetrForSegmentation''',
							    '''DetrModel''',
							    '''DetrPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

							__magic_name__       					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

