'''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"""
   A				:							Tuple			     =     CONFIG_NAME
   @register_to_config
   def __init__(self							,					_a = True							,					_a = 8							,					_a = "lanczos"							,					_a = True							,					_a = False							,					)     ->  Optional[int]:
        super().__init__()
   @staticmethod
   def 		_lowerCamelCase			(_a					)     ->  Any:
        if images.ndim == 3:
             lowercase_       :							List[str]  					=       images[None, ...]
        lowercase_       :							Tuple  					=       (images * 255).round().astype('uint8'					)
        if images.shape[-1] == 1:
             # special case for grayscale (single channel) images
             lowercase_       :							Union[str, Any]  					=       [Image.fromarray(image.squeeze()							,					mode='L'					) for image in images]
        else:
             lowercase_       :							Union[str, Any]  					=       [Image.fromarray(_a					) for image in images]

        return pil_images
   @staticmethod
   def 		_lowerCamelCase			(_a					)     ->  int:
        if not isinstance(_a							,					_a					):
             lowercase_       :							List[Any]  					=       [images]
        lowercase_       :							str  					=       [np.array(_a					).astype(np.floataa					) / 255.0 for image in images]
        lowercase_       :							Union[str, Any]  					=       np.stack(_a							,					axis=0					)

        return images
   @staticmethod
   def 		_lowerCamelCase			(_a					)     ->  List[Any]:
        if images.ndim == 3:
             lowercase_       :							Dict  					=       images[..., None]

        lowercase_       :							Any  					=       torch.from_numpy(images.transpose(0							,					3							,					1							,					2					)					)
        return images
   @staticmethod
   def 		_lowerCamelCase			(_a					)     ->  Optional[Any]:
        lowercase_       :							str  					=       images.cpu().permute(0							,					2							,					3							,					1					).float().numpy()
        return images
   @staticmethod
   def 		_lowerCamelCase			(_a					)     ->  Optional[Any]:
        return 2.0 * images - 1.0
   @staticmethod
   def 		_lowerCamelCase			(_a					)     ->  Optional[int]:
        return (images / 2 + 0.5).clamp(0							,					1					)
   @staticmethod
   def 		_lowerCamelCase			(_a					)     ->  Union[str, Any]:
        lowercase_       :							Optional[int]  					=       image.convert('RGB'					)
        return image
   def 		_lowerCamelCase			(self							,					_a							,					_a = None							,					_a = None							,					)     ->  Tuple:
        if height is None:
             lowercase_       :							Union[str, Any]  					=       image.height
        if width is None:
             lowercase_       :							Tuple  					=       image.width

        lowercase_					,lowercase_       :							Tuple  					=       (
            x - x % self.config.vae_scale_factor for x in (width, height)
        )  # resize to integer multiple of vae_scale_factor
        lowercase_       :							Any  					=       image.resize((width, height)							,					resample=PIL_INTERPOLATION[self.config.resample]					)
        return image
   def 		_lowerCamelCase			(self							,					_a							,					_a = None							,					_a = None							,					)     ->  Any:
        lowercase_       :							str  					=       (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(_a							,					_a					):
             lowercase_       :							List[str]  					=       [image]
        elif not (isinstance(_a							,					_a					) and all(isinstance(_a							,					_a					) for i in image					)):
             raise ValueError(
                 f'''Input is in incorrect format: {[type(_a					) for i in image]}. Currently, we only support {", ".join(_a					)}'''					)

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

        elif isinstance(image[0]							,					np.ndarray					):
             lowercase_       :							Tuple  					=       np.concatenate(_a							,					axis=0					) if image[0].ndim == 4 else np.stack(_a							,					axis=0					)
             lowercase_       :							List[Any]  					=       self.numpy_to_pt(_a					)
             lowercase_					,lowercase_					,lowercase_					,lowercase_       :							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					):
             lowercase_       :							List[str]  					=       torch.cat(_a							,					axis=0					) if image[0].ndim == 4 else torch.stack(_a							,					axis=0					)
             lowercase_					,lowercase_					,lowercase_					,lowercase_       :							Tuple  					=       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]
        lowercase_       :							List[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()}]'''							,					_a							,					)
             lowercase_       :							Optional[int]  					=       False

        if do_normalize:
             lowercase_       :							Union[str, Any]  					=       self.normalize(_a					)

        return image





   def 		_lowerCamelCase			(self							,					_a							,					_a = "pil"							,					_a = None							,					)     ->  List[str]:
        if not isinstance(_a							,					torch.Tensor					):
             raise ValueError(
                 f'''Input for postprocessing is in incorrect format: {type(_a					)}. We only support pytorch tensor'''					)
        if output_type not in ["latent", "pt", "np", "pil"]:
             lowercase_       :							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'							,					_a							,					standard_warn=_a					)
             lowercase_       :							str  					=       'np'

        if output_type == "latent":
             return image

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

        lowercase_       :							Tuple  					=       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

        lowercase_       :							Any  					=       self.pt_to_numpy(_a					)

        if output_type == "np":
             return image

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



class    UpperCAmelCase__	(							_SCREAMING_SNAKE_CASE      ):






   """simple docstring"""
   A				:							Optional[Any]			     =     CONFIG_NAME
   @register_to_config
   def __init__(self							,					_a = True							,					_a = 8							,					_a = "lanczos"							,					_a = True							,					)     ->  str:
        super().__init__()
   @staticmethod
   def 		_lowerCamelCase			(_a					)     ->  Dict:
        if images.ndim == 3:
             lowercase_       :							Union[str, Any]  					=       images[None, ...]
        lowercase_       :							Union[str, Any]  					=       (images * 255).round().astype('uint8'					)
        if images.shape[-1] == 1:
             # special case for grayscale (single channel) images
             lowercase_       :							Any  					=       [Image.fromarray(image.squeeze()							,					mode='L'					) for image in images]
        else:
             lowercase_       :							int  					=       [Image.fromarray(image[:, :, :3]					) for image in images]

        return pil_images
   @staticmethod
   def 		_lowerCamelCase			(_a					)     ->  int:
        return image[:, :, 1] * 2**8 + image[:, :, 2]
   def 		_lowerCamelCase			(self							,					_a					)     ->  Union[str, Any]:
        if images.ndim == 3:
             lowercase_       :							List[Any]  					=       images[None, ...]
        lowercase_       :							Union[str, Any]  					=       images[:, :, :, 3:]
        if images.shape[-1] == 6:
             lowercase_       :							Dict  					=       (images_depth * 255).round().astype('uint8'					)
             lowercase_       :							Optional[int]  					=       [
                 Image.fromarray(self.rgblike_to_depthmap(_a					)							,					mode='I;16'					) for image_depth in images_depth
             ]
        elif images.shape[-1] == 4:
             lowercase_       :							List[Any]  					=       (images_depth * 65_535.0).astype(np.uintaa					)
             lowercase_       :							Union[str, Any]  					=       [Image.fromarray(_a							,					mode='I;16'					) for image_depth in images_depth]
        else:
             raise Exception('Not supported'					)

        return pil_images





   def 		_lowerCamelCase			(self							,					_a							,					_a = "pil"							,					_a = None							,					)     ->  int:
        if not isinstance(_a							,					torch.Tensor					):
             raise ValueError(
                 f'''Input for postprocessing is in incorrect format: {type(_a					)}. We only support pytorch tensor'''					)
        if output_type not in ["latent", "pt", "np", "pil"]:
             lowercase_       :							List[str]  					=       (
                 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'							,					_a							,					standard_warn=_a					)
             lowercase_       :							Tuple  					=       'np'

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

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

        lowercase_       :							Optional[Any]  					=       self.pt_to_numpy(_a					)

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

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










class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Optional[Any]			,							snake_case       :			int					):
          __UpperCamelCase			=    data
          __UpperCamelCase			=    42
          __UpperCamelCase			=    42







def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						None:

    """simple docstring"""


    __UpperCamelCase			=    0
    __UpperCamelCase			=    x




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						None:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    find_set(lowercase_   ), find_set(lowercase_   )
    if x == y:
        return

    elif x.rank > y.rank:
        __UpperCamelCase			=    x
    else:
        __UpperCamelCase			=    y
        if x.rank == y.rank:
            y.rank += 1




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Node:

    """simple docstring"""


    if x != x.parent:
        __UpperCamelCase			=    find_set(x.parent   )
    return x.parent




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						set:

    """simple docstring"""


    __UpperCamelCase			=    ({0, 1, 2}, {3, 4, 5})
    for s in sets:
        if node.data in s:
            return s
    __UpperCamelCase			=    F"{node.data} is not in {sets}"
    raise ValueError(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	)							->						None:

    """simple docstring"""


    __UpperCamelCase			=    [Node(lowercase_   ) for i in range(6   )]
    for v in vertex:
        make_set(lowercase_   )

    union_set(vertex[0]       ,  vertex[1]   )
    union_set(vertex[1]       ,  vertex[2]   )
    union_set(vertex[3]       ,  vertex[4]   )
    union_set(vertex[3]       ,  vertex[5]   )

    for nodea in vertex:
        for nodea in vertex:
            if find_python_set(lowercase_   ).isdisjoint(find_python_set(lowercase_   )   ):
                assert find_set(lowercase_   ) != find_set(lowercase_   )
            else:
                assert find_set(lowercase_   ) == find_set(lowercase_   )


if __name__ == "__main__":
 test_disjoint_set()






