






'''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  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				List[str]         =       CONFIG_NAME






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

									'''simple docstring'''







									super().__init__()






			@staticmethod
			def 					A   ( lowercase				:    np.ndarray      )				->     PIL.Image.Image:

									'''simple docstring'''







									if images.ndim == 3:
															UpperCamelCase__       =			images[None, ...]
									UpperCamelCase__       =			(images * 2_5_5).round().astype("""uint8"""      )
									if images.shape[-1] == 1:
															# special case for grayscale (single channel) images
															UpperCamelCase__       =			[Image.fromarray(image.squeeze()			,   mode="""L"""      ) for image in images]
									else:
															UpperCamelCase__       =			[Image.fromarray(lowercase      ) for image in images]

									return pil_images






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

									'''simple docstring'''







									if not isinstance(lowercase			,   lowercase      ):
															UpperCamelCase__       =			[images]
									UpperCamelCase__       =			[np.array(lowercase      ).astype(np.floataa      ) / 2_5_5.0 for image in images]
									UpperCamelCase__       =			np.stack(lowercase			,   axis=0      )

									return images






			@staticmethod
			def 					A   ( lowercase				:    np.ndarray      )				->     torch.FloatTensor:

									'''simple docstring'''







									if images.ndim == 3:
															UpperCamelCase__       =			images[..., None]

									UpperCamelCase__       =			torch.from_numpy(images.transpose(0			,   3			,   1			,   2      )      )
									return images






			@staticmethod
			def 					A   ( lowercase				:    torch.FloatTensor      )				->     np.ndarray:

									'''simple docstring'''







									UpperCamelCase__       =			images.cpu().permute(0			,   2			,   3			,   1      ).float().numpy()
									return images






			@staticmethod
			def 					A   ( lowercase				:    Optional[Any]      )				->     str:

									'''simple docstring'''







									return 2.0 * images - 1.0






			@staticmethod
			def 					A   ( lowercase				:    List[str]      )				->     List[Any]:

									'''simple docstring'''







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






			@staticmethod
			def 					A   ( lowercase				:    PIL.Image.Image      )				->     PIL.Image.Image:

									'''simple docstring'''







									UpperCamelCase__       =			image.convert("""RGB"""      )
									return image






			def 					A   ( self				:    Any			,   lowercase				:    PIL.Image.Image			,   lowercase				:    Optional[int] = None			,   lowercase				:    Optional[int] = None			,   )				->     PIL.Image.Image:

									'''simple docstring'''







									if height is None:
															UpperCamelCase__       =			image.height
									if width is None:
															UpperCamelCase__       =			image.width

									UpperCamelCase__     , UpperCamelCase__       =			(
									    x - x % self.config.vae_scale_factor for x in (width, height)
									)  # resize to integer multiple of vae_scale_factor
									UpperCamelCase__       =			image.resize((width, height)			,   resample=PIL_INTERPOLATION[self.config.resample]      )
									return image






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

									'''simple docstring'''







									UpperCamelCase__       =			(PIL.Image.Image, np.ndarray, torch.Tensor)
									if isinstance(lowercase			,   lowercase      ):
															UpperCamelCase__       =			[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:
																					UpperCamelCase__       =			[self.convert_to_rgb(lowercase      ) for i in image]
															if self.config.do_resize:
																					UpperCamelCase__       =			[self.resize(lowercase			,   lowercase			,   lowercase      ) for i in image]
															UpperCamelCase__       =			self.pil_to_numpy(lowercase      )  # to np
															UpperCamelCase__       =			self.numpy_to_pt(lowercase      )  # to pt

									elif isinstance(image[0]			,   np.ndarray      ):
															UpperCamelCase__       =			np.concatenate(lowercase			,   axis=0      ) if image[0].ndim == 4 else np.stack(lowercase			,   axis=0      )
															UpperCamelCase__       =			self.numpy_to_pt(lowercase      )
															UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__       =			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      ):
															UpperCamelCase__       =			torch.cat(lowercase			,   axis=0      ) if image[0].ndim == 4 else torch.stack(lowercase			,   axis=0      )
															UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__       =			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]
									UpperCamelCase__       =			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			,   )
															UpperCamelCase__       =			False

									if do_normalize:
															UpperCamelCase__       =			self.normalize(lowercase      )

									return image







			def 					A   ( self				:    Any			,   lowercase				:    torch.FloatTensor			,   lowercase				:    str = "pil"			,   lowercase				:    Optional[List[bool]] = None			,   )				->     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"]:
															UpperCamelCase__       =			(
															    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      )
															UpperCamelCase__       =			"""np"""

									if output_type == "latent":
															return image

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

									UpperCamelCase__       =			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

									UpperCamelCase__       =			self.pt_to_numpy(lowercase      )

									if output_type == "np":
															return image

									if output_type == "pil":
															return self.numpy_to_pil(lowercase      )
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				Tuple         =       CONFIG_NAME






			@register_to_config
			def __init__( self				:    Dict			,   lowercase				:    bool = True			,   lowercase				:    int = 8			,   lowercase				:    str = "lanczos"			,   lowercase				:    bool = True			,   )				->     str:

									'''simple docstring'''







									super().__init__()






			@staticmethod
			def 					A   ( lowercase				:    str      )				->     List[Any]:

									'''simple docstring'''







									if images.ndim == 3:
															UpperCamelCase__       =			images[None, ...]
									UpperCamelCase__       =			(images * 2_5_5).round().astype("""uint8"""      )
									if images.shape[-1] == 1:
															# special case for grayscale (single channel) images
															UpperCamelCase__       =			[Image.fromarray(image.squeeze()			,   mode="""L"""      ) for image in images]
									else:
															UpperCamelCase__       =			[Image.fromarray(image[:, :, :3]      ) for image in images]

									return pil_images






			@staticmethod
			def 					A   ( lowercase				:    int      )				->     Any:

									'''simple docstring'''







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






			def 					A   ( self				:    str			,   lowercase				:    Dict      )				->     int:

									'''simple docstring'''







									if images.ndim == 3:
															UpperCamelCase__       =			images[None, ...]
									UpperCamelCase__       =			images[:, :, :, 3:]
									if images.shape[-1] == 6:
															UpperCamelCase__       =			(images_depth * 2_5_5).round().astype("""uint8"""      )
															UpperCamelCase__       =			[
															    Image.fromarray(self.rgblike_to_depthmap(lowercase      )			,   mode="""I;16"""      ) for image_depth in images_depth
															]
									elif images.shape[-1] == 4:
															UpperCamelCase__       =			(images_depth * 6_5_5_3_5.0).astype(np.uintaa      )
															UpperCamelCase__       =			[Image.fromarray(lowercase			,   mode="""I;16"""      ) for image_depth in images_depth]
									else:
															raise Exception("""Not supported"""      )

									return pil_images







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

									'''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"]:
															UpperCamelCase__       =			(
															    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      )
															UpperCamelCase__       =			"""np"""

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

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

									UpperCamelCase__       =			self.pt_to_numpy(lowercase      )

									if output_type == "np":
															if image.shape[-1] == 6:
																					UpperCamelCase__       =			np.stack([self.rgblike_to_depthmap(im[:, :, 3:]      ) for im in image]			,   axis=0      )
															else:
																					UpperCamelCase__       =			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'''


lowerCamelCase_					:			List[Any]     						=				{0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

lowerCamelCase_					:			Tuple     						=				{0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}







def 						__magic_name__(			_A    ,   _A    ,   _A       ):
						'''simple docstring'''





						UpperCamelCase__       =			True
						UpperCamelCase__       =			[]

						for neighbour in graph[vert]:
												if not visited[neighbour]:
																		connected_verts += dfs(_A    ,   _A    ,   _A       )

						return [vert, *connected_verts]







def 						__magic_name__(			_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			len(_A       )
						UpperCamelCase__       =			graph_size * [False]
						UpperCamelCase__       =			[]

						for i in range(_A       ):
												if not visited[i]:
																		UpperCamelCase__       =			dfs(_A    ,   _A    ,   _A       )
																		components_list.append(_A       )

						return components_list


if __name__ == "__main__":
					import doctest

					doctest.testmod()
