
'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 		__UpperCAmelCase   (      _lowerCamelCase		):


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								super().__init__()

								# make sure scheduler can always be converted to DDIM
								_snake_case     		=	DDIMScheduler.from_config(scheduler.config						)

								self.register_modules(unet=lowerCAmelCase_					,     scheduler=lowerCAmelCase_						)




							@torch.no_grad()
							def __call__( self					,     lowerCAmelCase_ = 1					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = 0.0					,     lowerCAmelCase_ = 50					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = "pil"					,     lowerCAmelCase_ = True					,     ):





								"""simple docstring"""






								if isinstance(self.unet.config.sample_size					,     lowerCAmelCase_						):
									_snake_case     		=	(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
								else:
									_snake_case     		=	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if isinstance(lowerCAmelCase_					,     lowerCAmelCase_						) and len(lowerCAmelCase_						) != batch_size:
									raise ValueError(
									    F'You have passed a list of generators of length {len(lowerCAmelCase_						)}, but requested an effective batch'
									    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'						)

								_snake_case     		=	randn_tensor(lowerCAmelCase_					,     generator=lowerCAmelCase_					,     device=self.device					,     dtype=self.unet.dtype						)

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

								for t in self.progress_bar(self.scheduler.timesteps						):
									# 1. predict noise model_output
									_snake_case     		=	self.unet(lowerCAmelCase_					,     lowerCAmelCase_						).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(
									    lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     eta=lowerCAmelCase_					,     use_clipped_model_output=lowerCAmelCase_					,     generator=lowerCAmelCase_						).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(lowerCAmelCase_						)

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase_						)



'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase					:							int   =      {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Optional[int]   =      ["Mask2FormerImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Optional[Any]   =      [
					    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Mask2FormerForUniversalSegmentation",
					    "Mask2FormerModel",
					    "Mask2FormerPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					lowercase					:							int   =      _LazyModule(__name__, globals()["__file__"], _import_structure)

