





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_     (     snake_case__       ):
					_a			:	Dict							=				CONFIG_NAME



					@register_to_config
					def __init__(		self    :    str					,			lowerCamelCase    :    bool = True					,			lowerCamelCase    :    int = 8					,			lowerCamelCase    :    str = "lanczos"					,			lowerCamelCase    :    bool = True					,			lowerCamelCase    :    bool = False					,			):
							super().__init__()



					@staticmethod
					def      __a    (		lowerCamelCase    :    np.ndarray	):
							if images.ndim == 3:
									lowerCamelCase_  :    List[str]   	=			images[None, ...]
							lowerCamelCase_  :    str   	=			(images * 2_55).round().astype('uint8'	)
							if images.shape[-1] == 1:
									# special case for grayscale (single channel) images
									lowerCamelCase_  :    int   	=			[Image.fromarray(image.squeeze()					,			mode='L'	) for image in images]
							else:
									lowerCamelCase_  :    str   	=			[Image.fromarray(lowerCamelCase	) for image in images]

							return pil_images



					@staticmethod
					def      __a    (		lowerCamelCase    :    Union[List[PIL.Image.Image], PIL.Image.Image]	):
							if not isinstance(lowerCamelCase					,			lowerCamelCase	):
									lowerCamelCase_  :    List[Any]   	=			[images]
							lowerCamelCase_  :    List[Any]   	=			[np.array(lowerCamelCase	).astype(np.floataa	) / 255.0 for image in images]
							lowerCamelCase_  :    str   	=			np.stack(lowerCamelCase					,			axis=0	)

							return images



					@staticmethod
					def      __a    (		lowerCamelCase    :    np.ndarray	):
							if images.ndim == 3:
									lowerCamelCase_  :    Tuple   	=			images[..., None]

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



					@staticmethod
					def      __a    (		lowerCamelCase    :    torch.FloatTensor	):
							lowerCamelCase_  :    Dict   	=			images.cpu().permute(0					,			2					,			3					,			1	).float().numpy()
							return images



					@staticmethod
					def      __a    (		lowerCamelCase    :    Union[str, Any]	):
							return 2.0 * images - 1.0



					@staticmethod
					def      __a    (		lowerCamelCase    :    List[str]	):
							return (images / 2 + 0.5).clamp(0					,			1	)



					@staticmethod
					def      __a    (		lowerCamelCase    :    PIL.Image.Image	):
							lowerCamelCase_  :    Any   	=			image.convert('RGB'	)
							return image



					def      __a    (		self    :    List[str]					,			lowerCamelCase    :    PIL.Image.Image					,			lowerCamelCase    :    Optional[int] = None					,			lowerCamelCase    :    Optional[int] = None					,			):
							if height is None:
									lowerCamelCase_  :    Any   	=			image.height
							if width is None:
									lowerCamelCase_  :    Any   	=			image.width

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



					def      __a    (		self    :    Any					,			lowerCamelCase    :    Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]					,			lowerCamelCase    :    Optional[int] = None					,			lowerCamelCase    :    Optional[int] = None					,			):
							lowerCamelCase_  :    int   	=			(PIL.Image.Image, np.ndarray, torch.Tensor)
							if isinstance(lowerCamelCase					,			lowerCamelCase	):
									lowerCamelCase_  :    Union[str, Any]   	=			[image]
							elif not (isinstance(lowerCamelCase					,			lowerCamelCase	) and all(isinstance(lowerCamelCase					,			lowerCamelCase	) for i in image	)):
									raise ValueError(
									    F"Input is in incorrect format: {[type(lowerCamelCase	) for i in image]}. Currently, we only support {', '.join(lowerCamelCase	)}"	)

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

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

							if do_normalize:
									lowerCamelCase_  :    Union[str, Any]   	=			self.normalize(lowerCamelCase	)

							return image





					def      __a    (		self    :    Any					,			lowerCamelCase    :    torch.FloatTensor					,			lowerCamelCase    :    str = "pil"					,			lowerCamelCase    :    Optional[List[bool]] = None					,			):
							if not isinstance(lowerCamelCase					,			torch.Tensor	):
									raise ValueError(
									    F"Input for postprocessing is in incorrect format: {type(lowerCamelCase	)}. We only support pytorch tensor"	)
							if output_type not in ["latent", "pt", "np", "pil"]:
									lowerCamelCase_  :    Any   	=			(
									    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'					,			lowerCamelCase					,			standard_warn=lowerCamelCase	)
									lowerCamelCase_  :    Any   	=			'np'

							if output_type == "latent":
									return image

							if do_denormalize is None:
									lowerCamelCase_  :    Union[str, Any]   	=			[self.config.do_normalize] * image.shape[0]

							lowerCamelCase_  :    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

							lowerCamelCase_  :    List[Any]   	=			self.pt_to_numpy(lowerCamelCase	)

							if output_type == "np":
									return image

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






class 	UpperCamelCase_     (     snake_case__       ):
					_a			:	Tuple							=				CONFIG_NAME



					@register_to_config
					def __init__(		self    :    List[Any]					,			lowerCamelCase    :    bool = True					,			lowerCamelCase    :    int = 8					,			lowerCamelCase    :    str = "lanczos"					,			lowerCamelCase    :    bool = True					,			):
							super().__init__()



					@staticmethod
					def      __a    (		lowerCamelCase    :    str	):
							if images.ndim == 3:
									lowerCamelCase_  :    Optional[int]   	=			images[None, ...]
							lowerCamelCase_  :    str   	=			(images * 2_55).round().astype('uint8'	)
							if images.shape[-1] == 1:
									# special case for grayscale (single channel) images
									lowerCamelCase_  :    int   	=			[Image.fromarray(image.squeeze()					,			mode='L'	) for image in images]
							else:
									lowerCamelCase_  :    Optional[Any]   	=			[Image.fromarray(image[:, :, :3]	) for image in images]

							return pil_images



					@staticmethod
					def      __a    (		lowerCamelCase    :    Tuple	):
							return image[:, :, 1] * 2**8 + image[:, :, 2]



					def      __a    (		self    :    Dict					,			lowerCamelCase    :    Optional[int]	):
							if images.ndim == 3:
									lowerCamelCase_  :    Tuple   	=			images[None, ...]
							lowerCamelCase_  :    Any   	=			images[:, :, :, 3:]
							if images.shape[-1] == 6:
									lowerCamelCase_  :    Optional[int]   	=			(images_depth * 2_55).round().astype('uint8'	)
									lowerCamelCase_  :    Optional[Any]   	=			[
									    Image.fromarray(self.rgblike_to_depthmap(lowerCamelCase	)					,			mode='I;16'	) for image_depth in images_depth
									]
							elif images.shape[-1] == 4:
									lowerCamelCase_  :    List[Any]   	=			(images_depth * 65_535.0).astype(np.uintaa	)
									lowerCamelCase_  :    List[Any]   	=			[Image.fromarray(lowerCamelCase					,			mode='I;16'	) for image_depth in images_depth]
							else:
									raise Exception('Not supported'	)

							return pil_images





					def      __a    (		self    :    List[Any]					,			lowerCamelCase    :    torch.FloatTensor					,			lowerCamelCase    :    str = "pil"					,			lowerCamelCase    :    Optional[List[bool]] = None					,			):
							if not isinstance(lowerCamelCase					,			torch.Tensor	):
									raise ValueError(
									    F"Input for postprocessing is in incorrect format: {type(lowerCamelCase	)}. We only support pytorch tensor"	)
							if output_type not in ["latent", "pt", "np", "pil"]:
									lowerCamelCase_  :    Union[str, Any]   	=			(
									    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'					,			lowerCamelCase					,			standard_warn=lowerCamelCase	)
									lowerCamelCase_  :    Dict   	=			'np'

							if do_denormalize is None:
									lowerCamelCase_  :    Union[str, Any]   	=			[self.config.do_normalize] * image.shape[0]

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

							lowerCamelCase_  :    Tuple   	=			self.pt_to_numpy(lowerCamelCase	)

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

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







def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
		return (input_a, input_a).count(0	) % 2


def 		_SCREAMING_SNAKE_CASE    (   ):
		assert xor_gate(0       ,0	) == 0
		assert xor_gate(0       ,1	) == 1
		assert xor_gate(1       ,0	) == 1
		assert xor_gate(1       ,1	) == 0


if __name__ == "__main__":
		print(xor_gate(0, 0))
		print(xor_gate(0, 1))
