




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput
class 			a__    (					snake_case__      ):






						def __init__(   self		,			_A		,			_A			):




											"""simple docstring"""



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






						@torch.no_grad()
						def __call__(   self		,			_A = 1		,			_A = None		,			_A = 5_0		,			_A = "pil"		,			_A = True		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,			generator=_A		,			)
											__lowerCAmelCase				   =       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				   =       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				   =       self.scheduler.step(_A		,			_A		,			_A			).prev_sample

											__lowerCAmelCase				   =       (image / 2 + 0.5).clamp(0		,			1			)
											__lowerCAmelCase				   =       image.cpu().permute(0		,			2		,			3		,			1			).numpy()
											if output_type == "pil":
																__lowerCAmelCase				   =       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 .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




