"""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_(__SCREAMING_SNAKE_CASE  )->    Tuple:
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image.size
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
							_SCREAMING_SNAKE_CASE		:						Dict			  =    image.resize((w, h)     ,	resample=PIL_INTERPOLATION["""lanczos"""]  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    np.array(__SCREAMING_SNAKE_CASE  ).astype(np.floataa  ) / 2_55.0
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    image[None].transpose(0     ,	3     ,	1     ,	2  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.from_numpy(__SCREAMING_SNAKE_CASE  )
							return 2.0 * image - 1.0




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				def __init__(      self							:							List[Any]      ,    _A							:							VQModel      ,    _A							:							UNetaDModel      ,    _A							:							Union[
				        DDIMScheduler,
				        PNDMScheduler,
				        LMSDiscreteScheduler,
				        EulerDiscreteScheduler,
				        EulerAncestralDiscreteScheduler,
				        DPMSolverMultistepScheduler,
				    ]      ,    ):



											"""simple docstring"""







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





				@torch.no_grad()
				def __call__(      self							:							Tuple      ,    _A							:							Union[torch.Tensor, PIL.Image.Image] = None      ,    _A							:							Optional[int] = 1      ,    _A							:							Optional[int] = 1_0_0      ,    _A							:							Optional[float] = 0.0      ,    _A							:							Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,    _A							:							Optional[str] = "pil"      ,    _A							:							bool = True      ,    ):



											"""simple docstring"""







											if isinstance(_A      ,    PIL.Image.Image):
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    1
											elif isinstance(_A      ,    torch.Tensor):
																		_SCREAMING_SNAKE_CASE		:						Any			  =    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):
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    preprocess(_A)

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image.shape[-2:]

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

											_SCREAMING_SNAKE_CASE		:						int			  =    randn_tensor(_A      ,    generator=_A      ,    device=self.device      ,    dtype=_A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    image.to(device=self.device      ,    dtype=_A)

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

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

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

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

											if output_type == "pil":
																		_SCREAMING_SNAKE_CASE		:						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_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_     =	{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''UniSpeechForCTC''',
			    '''UniSpeechForPreTraining''',
			    '''UniSpeechForSequenceClassification''',
			    '''UniSpeechModel''',
			    '''UniSpeechPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_unispeech import (
						    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
						    UniSpeechForCTC,
						    UniSpeechForPreTraining,
						    UniSpeechForSequenceClassification,
						    UniSpeechModel,
						    UniSpeechPreTrainedModel,
						)

else:
			import sys

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


