

"""simple docstring"""

# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       _snake_case  (    UpperCamelCase							):







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







						@torch.no_grad()
						def __call__(self  ,				UpperCAmelCase_ = 1  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = 5_0  ,				UpperCAmelCase_ = "pil"  ,				UpperCAmelCase_ = True  ,				**UpperCAmelCase_  ,				)      ->    Union[ImagePipelineOutput, Tuple]:
											__lowercase        =      torch.randn(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)  ,				generator=UpperCAmelCase_  ,				)
											__lowercase        =      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
																__lowercase        =      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
																__lowercase        =      self.scheduler.step(UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				).prev_sample

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


a__			:						Tuple							=			{
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						List[str]							=			["""SegformerFeatureExtractor"""]
							a__			:						str							=			["""SegformerImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						Optional[Any]							=			[
							    """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """SegformerDecodeHead""",
							    """SegformerForImageClassification""",
							    """SegformerForSemanticSegmentation""",
							    """SegformerLayer""",
							    """SegformerModel""",
							    """SegformerPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						List[Any]							=			[
							    """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFSegformerDecodeHead""",
							    """TFSegformerForImageClassification""",
							    """TFSegformerForSemanticSegmentation""",
							    """TFSegformerModel""",
							    """TFSegformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_segformer import SegformerFeatureExtractor
														from .image_processing_segformer import SegformerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_segformer import (
														    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SegformerDecodeHead,
														    SegformerForImageClassification,
														    SegformerForSemanticSegmentation,
														    SegformerLayer,
														    SegformerModel,
														    SegformerPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_segformer import (
														    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSegformerDecodeHead,
														    TFSegformerForImageClassification,
														    TFSegformerForSemanticSegmentation,
														    TFSegformerModel,
														    TFSegformerPreTrainedModel,
														)

else:
							import sys

							a__			:						Any							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


