






"""simple docstring"""



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



def 						lowercase    (				__snake_case				: List[Any]	):
		lowercase_       ,					lowercase_ : List[Any]							       =   image.size
		lowercase_       ,					lowercase_ : int							       =   (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
		lowercase_ : str							       =   image.resize((w, h)		,					resample=PIL_INTERPOLATION['''lanczos''']	)
		lowercase_ : Union[str, Any]							       =   np.array(__snake_case	).astype(np.floataa	) / 255.0
		lowercase_ : Union[str, Any]							       =   image[None].transpose(0		,					3		,					1		,					2	)
		lowercase_ : Optional[int]							       =   torch.from_numpy(__snake_case	)
		return 2.0 * image - 1.0






class 					_UpperCAmelCase       ( _A							):




					def __init__(				self     :						Tuple    ,						A     :						VQModel    ,						A     :						UNetaDModel    ,						A     :						Union[
					        DDIMScheduler,
					        PNDMScheduler,
					        LMSDiscreteScheduler,
					        EulerDiscreteScheduler,
					        EulerAncestralDiscreteScheduler,
					        DPMSolverMultistepScheduler,
					    ]    ,						)  ->  str:
							super().__init__()
							self.register_modules(vqvae=A    ,						unet=A    ,						scheduler=A   )






					@torch.no_grad()
					def __call__(				self     :						Optional[int]    ,						A     :						Union[torch.Tensor, PIL.Image.Image] = None    ,						A     :						Optional[int] = 1    ,						A     :						Optional[int] = 1_00    ,						A     :						Optional[float] = 0.0    ,						A     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,						A     :						Optional[str] = "pil"    ,						A     :						bool = True    ,						)  ->  Union[Tuple, ImagePipelineOutput]:
							if isinstance(A    ,						PIL.Image.Image   ):
									lowercase_ : Optional[int]							       =   1
							elif isinstance(A    ,						torch.Tensor   ):
									lowercase_ : str							       =   image.shape[0]
							else:
									raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(A   )}'''   )

							if isinstance(A    ,						PIL.Image.Image   ):
									lowercase_ : Optional[Any]							       =   preprocess(A   )

							lowercase_       ,					lowercase_ : List[str]							       =   image.shape[-2:]

							# in_channels should be 6: 3 for latents, 3 for low resolution image
							lowercase_ : Optional[int]							       =   (batch_size, self.unet.config.in_channels // 2, height, width)
							lowercase_ : Dict							       =   next(self.unet.parameters()   ).dtype

							lowercase_ : Dict							       =   randn_tensor(A    ,						generator=A    ,						device=self.device    ,						dtype=A   )

							lowercase_ : List[Any]							       =   image.to(device=self.device    ,						dtype=A   )

							# set timesteps and move to the correct device
							self.scheduler.set_timesteps(A    ,						device=self.device   )
							lowercase_ : str							       =   self.scheduler.timesteps

							# scale the initial noise by the standard deviation required by the scheduler
							lowercase_ : List[Any]							       =   latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							lowercase_ : Optional[int]							       =   '''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
							lowercase_ : Any							       =   {}
							if accepts_eta:
									lowercase_ : Union[str, Any]							       =   eta

							for t in self.progress_bar(A   ):
									# concat latents and low resolution image in the channel dimension.
									lowercase_ : Optional[int]							       =   torch.cat([latents, image]    ,						dim=1   )
									lowercase_ : Union[str, Any]							       =   self.scheduler.scale_model_input(A    ,						A   )
									# predict the noise residual
									lowercase_ : List[str]							       =   self.unet(A    ,						A   ).sample
									# compute the previous noisy sample x_t -> x_t-1
									lowercase_ : List[str]							       =   self.scheduler.step(A    ,						A    ,						A    ,						**A   ).prev_sample

							# decode the image latents with the VQVAE
							lowercase_ : Union[str, Any]							       =   self.vqvae.decode(A   ).sample
							lowercase_ : Optional[Any]							       =   torch.clamp(A    ,						-1.0    ,						1.0   )
							lowercase_ : Dict							       =   image / 2 + 0.5
							lowercase_ : int							       =   image.cpu().permute(0    ,						2    ,						3    ,						1   ).numpy()

							if output_type == "pil":
									lowercase_ : Union[str, Any]							       =   self.numpy_to_pil(A   )

							if not return_dict:
									return (image,)

							return ImagePipelineOutput(images=A   )











"""simple docstring"""



from typing import TYPE_CHECKING

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


__A		:       List[str]  				=		{
    '''configuration_mobilenet_v2''': [
        '''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV2Config''',
        '''MobileNetV2OnnxConfig''',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Dict  				=		['''MobileNetV2FeatureExtractor''']
						__A		:       Optional[int]  				=		['''MobileNetV2ImageProcessor''']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Optional[Any]  				=		[
						    '''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileNetV2ForImageClassification''',
						    '''MobileNetV2ForSemanticSegmentation''',
						    '''MobileNetV2Model''',
						    '''MobileNetV2PreTrainedModel''',
						    '''load_tf_weights_in_mobilenet_v2''',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

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



