




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




class 		__A      (			__UpperCAmelCase			):

						def __init__(		self   :Any    , __snake_case   :VQModel    , __snake_case   :UNetaDModel    , __snake_case   :Union[
						        DDIMScheduler,
						        PNDMScheduler,
						        LMSDiscreteScheduler,
						        EulerDiscreteScheduler,
						        EulerAncestralDiscreteScheduler,
						        DPMSolverMultistepScheduler,
						    ]    , ):
										'''simple docstring'''

										super().__init__()
										self.register_modules(vqvae=__SCREAMING_SNAKE_CASE    , unet=__SCREAMING_SNAKE_CASE    , scheduler=__SCREAMING_SNAKE_CASE )


						@torch.no_grad()
						def __call__(		self   :List[Any]    , __snake_case   :Union[torch.Tensor, PIL.Image.Image] = None    , __snake_case   :Optional[int] = 1    , __snake_case   :Optional[int] = 1_00    , __snake_case   :Optional[float] = 0.0    , __snake_case   :Optional[Union[torch.Generator, List[torch.Generator]]] = None    , __snake_case   :Optional[str] = "pil"    , __snake_case   :bool = True    , ):
										'''simple docstring'''

										if isinstance(__SCREAMING_SNAKE_CASE    , PIL.Image.Image ):
														__magic_name__	:	Any								=1
										elif isinstance(__SCREAMING_SNAKE_CASE    , torch.Tensor ):
														__magic_name__	:	Optional[Any]								=image.shape[0]
										else:
														raise ValueError(f"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__SCREAMING_SNAKE_CASE )}" )

										if isinstance(__SCREAMING_SNAKE_CASE    , PIL.Image.Image ):
														__magic_name__	:	Optional[Any]								=preprocess(__SCREAMING_SNAKE_CASE )

										__magic_name__				, __magic_name__	:	str								=image.shape[-2:]

										# in_channels should be 6: 3 for latents, 3 for low resolution image
										__magic_name__	:	List[Any]								=(batch_size, self.unet.config.in_channels // 2, height, width)
										__magic_name__	:	List[str]								=next(self.unet.parameters() ).dtype

										__magic_name__	:	Tuple								=randn_tensor(__SCREAMING_SNAKE_CASE    , generator=__SCREAMING_SNAKE_CASE    , device=self.device    , dtype=__SCREAMING_SNAKE_CASE )

										__magic_name__	:	str								=image.to(device=self.device    , dtype=__SCREAMING_SNAKE_CASE )

										# set timesteps and move to the correct device
										self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    , device=self.device )
										__magic_name__	:	Optional[Any]								=self.scheduler.timesteps

										# scale the initial noise by the standard deviation required by the scheduler
										__magic_name__	:	Tuple								=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]
										__magic_name__	:	List[Any]								="""eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
										__magic_name__	:	Optional[int]								={}
										if accepts_eta:
														__magic_name__	:	List[str]								=eta

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

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

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

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE )










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_		:				Dict									=     {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_		:				Optional[int]									=     [
							    "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "WavLMForAudioFrameClassification",
							    "WavLMForCTC",
							    "WavLMForSequenceClassification",
							    "WavLMForXVector",
							    "WavLMModel",
							    "WavLMPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_wavlm import (
														    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    WavLMForAudioFrameClassification,
														    WavLMForCTC,
														    WavLMForSequenceClassification,
														    WavLMForXVector,
														    WavLMModel,
														    WavLMPreTrainedModel,
														)

else:
							import sys

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




