




'''simple docstring'''

# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class __magic_name__(						__lowerCAmelCase      ):






						def __init__(   self						:						Dict					,   __UpperCamelCase						:						Union[str, Any]					,   __UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






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







						@torch.no_grad()
						def __call__(   self						:						Optional[Any]					,   __UpperCamelCase						:						int = 1					,   __UpperCamelCase						:						Optional[torch.Generator] = None					,   __UpperCamelCase						:						int = 5_0					,   __UpperCamelCase						:						Optional[str] = "pil"					,   __UpperCamelCase						:						bool = True					,   **__UpperCamelCase						:						Optional[Any]					,   ):



									'''simple docstring'''






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

									snake_case__          =						(image / 2 + 0.5).clamp(0					,   1       )
									snake_case__          =						image.cpu().permute(0					,   2					,   3					,   1       ).numpy()
									if output_type == "pil":
												snake_case__          =						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_tf_available, is_torch_available


a__         =     {'''configuration_cvt''': ['''CVT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CvtConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CvtForImageClassification''',
		    '''CvtModel''',
		    '''CvtPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFCvtForImageClassification''',
		    '''TFCvtModel''',
		    '''TFCvtPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_cvt import (
				    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CvtForImageClassification,
				    CvtModel,
				    CvtPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_cvt import (
				    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCvtForImageClassification,
				    TFCvtModel,
				    TFCvtPreTrainedModel,
				)


else:
		import sys

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