
"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''



			def __init__(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     Optional[int]:
				super().__init__()
				self.register_modules(unet=__UpperCAmelCase     ,     scheduler=__UpperCAmelCase  )



			@torch.no_grad()
			def __call__(							self     ,     __UpperCAmelCase = 1     ,     __UpperCAmelCase = None     ,     __UpperCAmelCase = 50     ,     __UpperCAmelCase = "pil"     ,     __UpperCAmelCase = True     ,     **__UpperCAmelCase     ,     ) ->     Union[ImagePipelineOutput, Tuple]:
				_lowerCAmelCase								=torch.randn(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,     generator=__UpperCAmelCase     ,     )
				_lowerCAmelCase								=image.to(self.device  )

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

				for t in self.progress_bar(self.scheduler.timesteps  ):
					# 1. predict noise model_output
					_lowerCAmelCase								=self.unet(__UpperCAmelCase     ,     __UpperCAmelCase  ).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(__UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ).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(__UpperCAmelCase  )

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

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





"""simple docstring"""


from typing import TYPE_CHECKING

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


__A       						=       {'configuration_bert_generation': ['BertGenerationConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       ['BertGenerationTokenizer']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       [
			    'BertGenerationDecoder',
			    'BertGenerationEncoder',
			    'BertGenerationPreTrainedModel',
			    'load_tf_weights_in_bert_generation',
			]


if TYPE_CHECKING:
			from .configuration_bert_generation import BertGenerationConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bert_generation import BertGenerationTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bert_generation import (
						    BertGenerationDecoder,
						    BertGenerationEncoder,
						    BertGenerationPreTrainedModel,
						    load_tf_weights_in_bert_generation,
						)

else:
			import sys

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



