

"""simple docstring"""
import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class        _UpperCamelCase	(						SCREAMING_SNAKE_CASE_	):


  """simple docstring"""





  snake_case_        =    CONFIG_NAME



  @register_to_config
  def __init__( self					: Tuple       ,       snake_case					: bool = True       ,       snake_case					: int = 8       ,       snake_case					: str = "lanczos"       ,       snake_case					: bool = True       ,       snake_case					: bool = False       ,       )	->			Union[str, Any]:


         '''simple docstring'''





         super().__init__()



  @staticmethod
  def 							_UpperCAmelCase      ( snake_case					: np.ndarray      )	->			PIL.Image.Image:


         '''simple docstring'''





         if images.ndim == 3:
                __magic_name__			:		List[Any]        =     images[None, ...]
         __magic_name__			:		str        =     (images * 255).round().astype('''uint8'''      )
         if images.shape[-1] == 1:
                # special case for grayscale (single channel) images
                __magic_name__			:		List[Any]        =     [Image.fromarray(image.squeeze()       ,       mode='''L'''      ) for image in images]
         else:
                __magic_name__			:		Dict        =     [Image.fromarray(lowercase_      ) for image in images]

         return pil_images



  @staticmethod
  def 							_UpperCAmelCase      ( snake_case					: Union[List[PIL.Image.Image], PIL.Image.Image]      )	->			np.ndarray:


         '''simple docstring'''





         if not isinstance(lowercase_       ,       lowercase_      ):
                __magic_name__			:		str        =     [images]
         __magic_name__			:		Dict        =     [np.array(lowercase_      ).astype(np.floataa      ) / 255.0 for image in images]
         __magic_name__			:		Dict        =     np.stack(lowercase_       ,       axis=0      )

         return images



  @staticmethod
  def 							_UpperCAmelCase      ( snake_case					: np.ndarray      )	->			torch.FloatTensor:


         '''simple docstring'''





         if images.ndim == 3:
                __magic_name__			:		List[Any]        =     images[..., None]

         __magic_name__			:		Optional[int]        =     torch.from_numpy(images.transpose(0       ,       3       ,       1       ,       2      )      )
         return images



  @staticmethod
  def 							_UpperCAmelCase      ( snake_case					: torch.FloatTensor      )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		str        =     images.cpu().permute(0       ,       2       ,       3       ,       1      ).float().numpy()
         return images



  @staticmethod
  def 							_UpperCAmelCase      ( snake_case					: List[Any]      )	->			Optional[int]:


         '''simple docstring'''





         return 2.0 * images - 1.0



  @staticmethod
  def 							_UpperCAmelCase      ( snake_case					: str      )	->			str:


         '''simple docstring'''





         return (images / 2 + 0.5).clamp(0       ,       1      )



  @staticmethod
  def 							_UpperCAmelCase      ( snake_case					: PIL.Image.Image      )	->			PIL.Image.Image:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     image.convert('''RGB'''      )
         return image



  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: PIL.Image.Image       ,       snake_case					: Optional[int] = None       ,       snake_case					: Optional[int] = None       ,       )	->			PIL.Image.Image:


         '''simple docstring'''





         if height is None:
                __magic_name__			:		Union[str, Any]        =     image.height
         if width is None:
                __magic_name__			:		Optional[int]        =     image.width

         __magic_name__       ,							__magic_name__			:		Union[str, Any]        =     (
             x - x % self.config.vae_scale_factor for x in (width, height)
         )  # resize to integer multiple of vae_scale_factor
         __magic_name__			:		Optional[Any]        =     image.resize((width, height)       ,       resample=PIL_INTERPOLATION[self.config.resample]      )
         return image



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]       ,       snake_case					: Optional[int] = None       ,       snake_case					: Optional[int] = None       ,       )	->			torch.Tensor:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     (PIL.Image.Image, np.ndarray, torch.Tensor)
         if isinstance(lowercase_       ,       lowercase_      ):
                __magic_name__			:		str        =     [image]
         elif not (isinstance(lowercase_       ,       lowercase_      ) and all(isinstance(lowercase_       ,       lowercase_      ) for i in image      )):
                raise ValueError(
                    f"""Input is in incorrect format: {[type(lowercase_      ) for i in image]}. Currently, we only support {', '.join(lowercase_      )}"""      )

         if isinstance(image[0]       ,       PIL.Image.Image      ):
                if self.config.do_convert_rgb:
                       __magic_name__			:		int        =     [self.convert_to_rgb(lowercase_      ) for i in image]
                if self.config.do_resize:
                       __magic_name__			:		List[Any]        =     [self.resize(lowercase_       ,       lowercase_       ,       lowercase_      ) for i in image]
                __magic_name__			:		Dict        =     self.pil_to_numpy(lowercase_      )  # to np
                __magic_name__			:		Dict        =     self.numpy_to_pt(lowercase_      )  # to pt

         elif isinstance(image[0]       ,       np.ndarray      ):
                __magic_name__			:		Dict        =     np.concatenate(lowercase_       ,       axis=0      ) if image[0].ndim == 4 else np.stack(lowercase_       ,       axis=0      )
                __magic_name__			:		Optional[int]        =     self.numpy_to_pt(lowercase_      )
                __magic_name__       ,							__magic_name__       ,							__magic_name__       ,							__magic_name__			:		List[Any]        =     image.shape
                if self.config.do_resize and (
                    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                ):
                       raise ValueError(
                           f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
                           f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""      )

         elif isinstance(image[0]       ,       torch.Tensor      ):
                __magic_name__			:		int        =     torch.cat(lowercase_       ,       axis=0      ) if image[0].ndim == 4 else torch.stack(lowercase_       ,       axis=0      )
                __magic_name__       ,							__magic_name__       ,							__magic_name__       ,							__magic_name__			:		str        =     image.shape

                # don't need any preprocess if the image is latents
                if channel == 4:
                       return image

                if self.config.do_resize and (
                    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
                ):
                       raise ValueError(
                           f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
                           f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""      )

        # expected range [0,1], normalize to [-1,1]
         __magic_name__			:		Any        =     self.config.do_normalize
         if image.min() < 0:
                warnings.warn(
                    '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
                    f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""       ,       lowercase_       ,       )
                __magic_name__			:		Dict        =     False

         if do_normalize:
                __magic_name__			:		int        =     self.normalize(lowercase_      )

         return image




  def 							_UpperCAmelCase      ( self					: Union[str, Any]       ,       snake_case					: torch.FloatTensor       ,       snake_case					: str = "pil"       ,       snake_case					: Optional[List[bool]] = None       ,       )	->			List[str]:


         '''simple docstring'''





         if not isinstance(lowercase_       ,       torch.Tensor      ):
                raise ValueError(
                    f"""Input for postprocessing is in incorrect format: {type(lowercase_      )}. We only support pytorch tensor"""      )
         if output_type not in ["latent", "pt", "np", "pil"]:
                __magic_name__			:		Dict        =     (
                    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                    '''`pil`, `np`, `pt`, `latent`'''
                )
                deprecate('''Unsupported output_type'''       ,       '''1.0.0'''       ,       lowercase_       ,       standard_warn=lowercase_      )
                __magic_name__			:		Optional[int]        =     '''np'''

         if output_type == "latent":
                return image

         if do_denormalize is None:
                __magic_name__			:		str        =     [self.config.do_normalize] * image.shape[0]

         __magic_name__			:		Optional[int]        =     torch.stack(
             [self.denormalize(image[i]      ) if do_denormalize[i] else image[i] for i in range(image.shape[0]      )]      )

         if output_type == "pt":
                return image

         __magic_name__			:		int        =     self.pt_to_numpy(lowercase_      )

         if output_type == "np":
                return image

         if output_type == "pil":
                return self.numpy_to_pil(lowercase_      )

class        _UpperCamelCase	(						SCREAMING_SNAKE_CASE_	):


  """simple docstring"""





  snake_case_        =    CONFIG_NAME



  @register_to_config
  def __init__( self					: Union[str, Any]       ,       snake_case					: bool = True       ,       snake_case					: int = 8       ,       snake_case					: str = "lanczos"       ,       snake_case					: bool = True       ,       )	->			List[Any]:


         '''simple docstring'''





         super().__init__()



  @staticmethod
  def 							_UpperCAmelCase      ( snake_case					: Optional[int]      )	->			Optional[Any]:


         '''simple docstring'''





         if images.ndim == 3:
                __magic_name__			:		Optional[int]        =     images[None, ...]
         __magic_name__			:		Union[str, Any]        =     (images * 255).round().astype('''uint8'''      )
         if images.shape[-1] == 1:
                # special case for grayscale (single channel) images
                __magic_name__			:		List[Any]        =     [Image.fromarray(image.squeeze()       ,       mode='''L'''      ) for image in images]
         else:
                __magic_name__			:		Optional[Any]        =     [Image.fromarray(image[:, :, :3]      ) for image in images]

         return pil_images



  @staticmethod
  def 							_UpperCAmelCase      ( snake_case					: Any      )	->			Union[str, Any]:


         '''simple docstring'''





         return image[:, :, 1] * 2**8 + image[:, :, 2]



  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: int      )	->			Tuple:


         '''simple docstring'''





         if images.ndim == 3:
                __magic_name__			:		List[str]        =     images[None, ...]
         __magic_name__			:		Optional[Any]        =     images[:, :, :, 3:]
         if images.shape[-1] == 6:
                __magic_name__			:		List[Any]        =     (images_depth * 255).round().astype('''uint8'''      )
                __magic_name__			:		Optional[Any]        =     [
                    Image.fromarray(self.rgblike_to_depthmap(lowercase_      )       ,       mode='''I;16'''      ) for image_depth in images_depth
                ]
         elif images.shape[-1] == 4:
                __magic_name__			:		List[Any]        =     (images_depth * 6_5535.0).astype(np.uintaa      )
                __magic_name__			:		Dict        =     [Image.fromarray(lowercase_       ,       mode='''I;16'''      ) for image_depth in images_depth]
         else:
                raise Exception('''Not supported'''      )

         return pil_images




  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: torch.FloatTensor       ,       snake_case					: str = "pil"       ,       snake_case					: Optional[List[bool]] = None       ,       )	->			Dict:


         '''simple docstring'''





         if not isinstance(lowercase_       ,       torch.Tensor      ):
                raise ValueError(
                    f"""Input for postprocessing is in incorrect format: {type(lowercase_      )}. We only support pytorch tensor"""      )
         if output_type not in ["latent", "pt", "np", "pil"]:
                __magic_name__			:		Optional[int]        =     (
                    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
                    '''`pil`, `np`, `pt`, `latent`'''
                )
                deprecate('''Unsupported output_type'''       ,       '''1.0.0'''       ,       lowercase_       ,       standard_warn=lowercase_      )
                __magic_name__			:		Optional[int]        =     '''np'''

         if do_denormalize is None:
                __magic_name__			:		Dict        =     [self.config.do_normalize] * image.shape[0]

         __magic_name__			:		Optional[int]        =     torch.stack(
             [self.denormalize(image[i]      ) if do_denormalize[i] else image[i] for i in range(image.shape[0]      )]      )

         __magic_name__			:		str        =     self.pt_to_numpy(lowercase_      )

         if output_type == "np":
                if image.shape[-1] == 6:
                       __magic_name__			:		Optional[int]        =     np.stack([self.rgblike_to_depthmap(im[:, :, 3:]      ) for im in image]       ,       axis=0      )
                else:
                       __magic_name__			:		Union[str, Any]        =     image[:, :, :, 3:]
                return image[:, :, :, :3], image_depth

         if output_type == "pil":
                return self.numpy_to_pil(lowercase_      ), self.numpy_to_depth(lowercase_      )
         else:
                raise Exception(f"""This type {output_type} is not supported"""      )





"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






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






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		UpperCAmelCase			 =					(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCAmelCase			 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					image.to(self.device   )
												else:
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=self.device   )

												# set step values
												self.scheduler.set_timesteps(lowercase_   )

												for t in self.progress_bar(self.scheduler.timesteps   ):
																		# 1. predict noise model_output
																		UpperCAmelCase			 =					self.unet(lowercase_  ,    lowercase_   ).sample

																		# 2. compute previous image: x_t -> x_t-1
																		UpperCAmelCase			 =					self.scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   ).prev_sample

												UpperCAmelCase			 =					(image / 2 + 0.5).clamp(0  ,    1   )
												UpperCAmelCase			 =					image.cpu().permute(0  ,    2  ,    3  ,    1   ).numpy()
												if output_type == "pil":
																		UpperCAmelCase			 =					self.numpy_to_pil(lowercase_   )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_   )





