
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 		UpperCAmelCase     (      a_		)						->      Dict:







				"""simple docstring"""



				__A     ,				__A       =					image.size
				__A     ,				__A       =					(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
				__A       =					image.resize((w, h)						,  resample=PIL_INTERPOLATION["lanczos"]		)
				__A       =					np.array(a_		).astype(np.floataa		) / 255.0
				__A       =					image[None].transpose(0						,  3						,  1						,  2		)
				__A       =					torch.from_numpy(a_		)
				return 2.0 * image - 1.0





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





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







	@torch.no_grad()
	def __call__(			self   :					Optional[Any]				,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				,):
					if isinstance(A				,PIL.Image.Image					):
									__A       =					1
					elif isinstance(A				,torch.Tensor					):
									__A       =					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					):
									__A       =					preprocess(A					)

					__A     ,				__A       =					image.shape[-2:]

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

					__A       =					randn_tensor(A				,generator=A				,device=self.device				,dtype=A					)

					__A       =					image.to(device=self.device				,dtype=A					)

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

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

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

					# decode the image latents with the VQVAE
					__A       =					self.vqvae.decode(A					).sample
					__A       =					torch.clamp(A				,-1.0				,1.0					)
					__A       =					image / 2 + 0.5
					__A       =					image.cpu().permute(0				,2				,3				,1					).numpy()

					if output_type == "pil":
									__A       =					self.numpy_to_pil(A					)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A					)







"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


_UpperCamelCase					      =   logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							str									=		["""pixel_values"""]




				def __init__(  self	:str    ,     __lowercase	:bool = True    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:float = None    ,     __lowercase	:PILImageResampling = PILImageResampling.BILINEAR    ,     __lowercase	:bool = True    ,     __lowercase	:Union[int, float] = 1 / 255    ,     __lowercase	:bool = True    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     **__lowercase	:Tuple    ,     ):
											super().__init__(**__lowercase      )
											__lowerCamelCase   :   int       =size if size is not None else {'''shortest_edge''': 384}
											__lowerCamelCase   :   List[str]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )

											__lowerCamelCase   :   Any       =do_resize
											__lowerCamelCase   :   List[str]       =size
											# Default value set here for backwards compatibility where the value in config is None
											__lowerCamelCase   :   Tuple       =crop_pct if crop_pct is not None else 224 / 256
											__lowerCamelCase   :   Any       =resample
											__lowerCamelCase   :   List[str]       =do_rescale
											__lowerCamelCase   :   Dict       =rescale_factor
											__lowerCamelCase   :   Any       =do_normalize
											__lowerCamelCase   :   int       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase   :   List[Any]       =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        __lowercase	(  self	:Any    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:float    ,     __lowercase	:PILImageResampling = PILImageResampling.BICUBIC    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:int    ,     ):
											__lowerCamelCase   :   int       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											if "shortest_edge" not in size:
																		raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'      )
											__lowerCamelCase   :   Optional[int]       =size['''shortest_edge''']

											if shortest_edge < 384:
																		# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																		__lowerCamelCase   :   int       =int(shortest_edge / crop_pct      )
																		__lowerCamelCase   :   str       =get_resize_output_image_size(__lowercase    ,     size=__lowercase    ,     default_to_square=__lowercase      )
																		__lowerCamelCase   :   str       =resize(image=__lowercase    ,     size=__lowercase    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )
																		# then crop to (shortest_edge, shortest_edge)
																		return center_crop(image=__lowercase    ,     size=(shortest_edge, shortest_edge)    ,     data_format=__lowercase    ,     **__lowercase      )
											else:
																		# warping (no cropping) when evaluated at 384 or larger
																		return resize(
																		    __lowercase    ,     size=(shortest_edge, shortest_edge)    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:int    ,     __lowercase	:np.ndarray    ,     __lowercase	:Union[int, float]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:List[Any]    ,     ):
											return rescale(__lowercase    ,     scale=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:str    ,     __lowercase	:np.ndarray    ,     __lowercase	:Union[float, List[float]]    ,     __lowercase	:Union[float, List[float]]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:Optional[int]    ,     ):
											return normalize(__lowercase    ,     mean=__lowercase    ,     std=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:int    ,     __lowercase	:ImageInput    ,     __lowercase	:bool = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:float = None    ,     __lowercase	:PILImageResampling = None    ,     __lowercase	:bool = None    ,     __lowercase	:float = None    ,     __lowercase	:bool = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[str, TensorType]] = None    ,     __lowercase	:ChannelDimension = ChannelDimension.FIRST    ,     **__lowercase	:Optional[Any]    ,     ):
											__lowerCamelCase   :   Optional[Any]       =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase   :   int       =crop_pct if crop_pct is not None else self.crop_pct
											__lowerCamelCase   :   List[Any]       =resample if resample is not None else self.resample
											__lowerCamelCase   :   List[Any]       =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase   :   List[Any]       =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase   :   List[Any]       =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase   :   Optional[Any]       =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase   :   str       =image_std if image_std is not None else self.image_std

											__lowerCamelCase   :   str       =size if size is not None else self.size
											__lowerCamelCase   :   Optional[int]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )

											__lowerCamelCase   :   Optional[int]       =make_list_of_images(__lowercase      )

											if not valid_images(__lowercase      ):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

											if do_resize and size is None or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.'''      )

											if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																		raise ValueError('''crop_pct must be specified if size < 384.'''      )

											if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''Image mean and std must be specified if do_normalize is True.'''      )

											# All transformations expect numpy arrays.
											__lowerCamelCase   :   List[Any]       =[to_numpy_array(__lowercase      ) for image in images]

											if do_resize:
																		__lowerCamelCase   :   Union[str, Any]       =[self.resize(image=__lowercase    ,     size=__lowercase    ,     crop_pct=__lowercase    ,     resample=__lowercase      ) for image in images]

											if do_rescale:
																		__lowerCamelCase   :   int       =[self.rescale(image=__lowercase    ,     scale=__lowercase      ) for image in images]

											if do_normalize:
																		__lowerCamelCase   :   Union[str, Any]       =[self.normalize(image=__lowercase    ,     mean=__lowercase    ,     std=__lowercase      ) for image in images]

											__lowerCamelCase   :   Union[str, Any]       =[to_channel_dimension_format(__lowercase    ,     __lowercase      ) for image in images]

											__lowerCamelCase   :   Tuple       ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase    ,     tensor_type=__lowercase      )




