"""simple docstring"""







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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_a  :  Any=				logging.get_logger(__name__)  # pylint: disable=invalid-name
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[List, PIL.Image.Image, torch.Tensor]						)							->    List[Any]:

	'''simple docstring'''





	warnings.warn(
	    'The preprocess method is deprecated and will be removed in a future version. Please'
	    ' use VaeImageProcessor.preprocess instead'    ,							UpperCAmelCase_    ,							)
	if isinstance(UpperCAmelCase_    ,							torch.Tensor						):
		return image
	elif isinstance(UpperCAmelCase_    ,							PIL.Image.Image						):
		__snake_case    :							Union[str, Any]   	=				[image]

	if isinstance(image[0]    ,							PIL.Image.Image						):
		__snake_case	,       __snake_case    :							List[str]   	=				image[0].size
		__snake_case	,       __snake_case    :							Union[str, Any]   	=				(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

		__snake_case    :							List[Any]   	=				[np.array(i.resize((w, h)    ,							resample=PIL_INTERPOLATION['lanczos']						)						)[None, :] for i in image]
		__snake_case    :							str   	=				np.concatenate(UpperCAmelCase_    ,							axis=0						)
		__snake_case    :							Tuple   	=				np.array(UpperCAmelCase_						).astype(np.floataa						) / 255.0
		__snake_case    :							Any   	=				image.transpose(0    ,							3    ,							1    ,							2						)
		__snake_case    :							List[Any]   	=				2.0 * image - 1.0
		__snake_case    :							Any   	=				torch.from_numpy(UpperCAmelCase_						)
	elif isinstance(image[0]    ,							torch.Tensor						):
		__snake_case    :							Any   	=				torch.cat(UpperCAmelCase_    ,							dim=0						)
	return image
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Union[List, PIL.Image.Image, torch.Tensor]						)							->    Optional[Any]:

	'''simple docstring'''





	if isinstance(UpperCAmelCase_    ,							torch.Tensor						):
		return mask
	elif isinstance(UpperCAmelCase_    ,							PIL.Image.Image						):
		__snake_case    :							Optional[Any]   	=				[mask]

	if isinstance(mask[0]    ,							PIL.Image.Image						):
		__snake_case	,       __snake_case    :							List[Any]   	=				mask[0].size
		__snake_case	,       __snake_case    :							Union[str, Any]   	=				(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		__snake_case    :							Optional[Any]   	=				[np.array(m.convert('L'						).resize((w, h)    ,							resample=PIL_INTERPOLATION['nearest']						)						)[None, :] for m in mask]
		__snake_case    :							Dict   	=				np.concatenate(UpperCAmelCase_    ,							axis=0						)
		__snake_case    :							Tuple   	=				mask.astype(np.floataa						) / 255.0
		__snake_case    :							List[Any]   	=				0
		__snake_case    :							Tuple   	=				1
		__snake_case    :							Optional[Any]   	=				torch.from_numpy(UpperCAmelCase_						)
	elif isinstance(mask[0]    ,							torch.Tensor						):
		__snake_case    :							List[str]   	=				torch.cat(UpperCAmelCase_    ,							dim=0						)
	return mask

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   UNetaDModel
				UpperCAmelCase       :   RePaintScheduler



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






				@torch.no_grad()
				def __call__(self       :   List[Any]			,						_A       :   Union[torch.Tensor, PIL.Image.Image]			,						_A       :   Union[torch.Tensor, PIL.Image.Image]			,						_A       :   int = 2_50			,						_A       :   float = 0.0			,						_A       :   int = 10			,						_A       :   int = 10			,						_A       :   Optional[Union[torch.Generator, List[torch.Generator]]] = None			,						_A       :   Optional[str] = "pil"			,						_A       :   bool = True			,						)					->			Union[ImagePipelineOutput, Tuple]:
					__snake_case    :							List[Any]   	=				image

					__snake_case    :							int   	=				_preprocess_image(_A)
					__snake_case    :							Union[str, Any]   	=				original_image.to(device=self.device			,						dtype=self.unet.dtype)
					__snake_case    :							Union[str, Any]   	=				_preprocess_mask(_A)
					__snake_case    :							List[str]   	=				mask_image.to(device=self.device			,						dtype=self.unet.dtype)

					__snake_case    :							str   	=				original_image.shape[0]

					# sample gaussian noise to begin the loop
					if isinstance(_A			,						_A) and len(_A) != batch_size:
						raise ValueError(
						    f"You have passed a list of generators of length {len(_A)}, but requested an effective batch"
						    f" size of {batch_size}. Make sure the batch size matches the length of the generators.")

					__snake_case    :							List[str]   	=				original_image.shape
					__snake_case    :							Union[str, Any]   	=				randn_tensor(_A			,						generator=_A			,						device=self.device			,						dtype=self.unet.dtype)

					# set step values
					self.scheduler.set_timesteps(_A			,						_A			,						_A			,						self.device)
					__snake_case    :							List[Any]   	=				eta

					__snake_case    :							int   	=				self.scheduler.timesteps[0] + 1
					__snake_case    :							List[str]   	=				generator[0] if isinstance(_A			,						_A) else generator
					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
						if t < t_last:
							# predict the noise residual
							__snake_case    :							List[str]   	=				self.unet(_A			,						_A).sample
							# compute previous image: x_t -> x_t-1
							__snake_case    :							int   	=				self.scheduler.step(_A			,						_A			,						_A			,						_A			,						_A			,						_A).prev_sample

						else:
							# compute the reverse: x_t-1 -> x_t
							__snake_case    :							Union[str, Any]   	=				self.scheduler.undo_step(_A			,						_A			,						_A)
						__snake_case    :							Dict   	=				t

					__snake_case    :							int   	=				(image / 2 + 0.5).clamp(0			,						1)
					__snake_case    :							Any   	=				image.cpu().permute(0			,						2			,						3			,						1).numpy()
					if output_type == "pil":
						__snake_case    :							Optional[int]   	=				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_torch_available


_a  :  Any=				{"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Union[str, Any]=				["SpeechEncoderDecoderModel"]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Tuple=				["FlaxSpeechEncoderDecoderModel"]

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

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





