






'''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 				snake_case__     (			UpperCamelCase):
					a_       =			CONFIG_NAME




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




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

								return pil_images




					@staticmethod
					def 						A     (       _A       :						Union[List[PIL.Image.Image], PIL.Image.Image]     )  ->    np.ndarray:
								if not isinstance(_A							,				_A     ):
											UpperCAmelCase_     :   Any       =   [images]
								UpperCAmelCase_     :   Union[str, Any]       =   [np.array(_A     ).astype(np.floataa     ) / 255.0 for image in images]
								UpperCAmelCase_     :   Optional[int]       =   np.stack(_A							,				axis=0     )

								return images




					@staticmethod
					def 						A     (       _A       :						np.ndarray     )  ->    torch.FloatTensor:
								if images.ndim == 3:
											UpperCAmelCase_     :   Any       =   images[..., None]

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




					@staticmethod
					def 						A     (       _A       :						torch.FloatTensor     )  ->    np.ndarray:
								UpperCAmelCase_     :   List[str]       =   images.cpu().permute(0							,				2							,				3							,				1     ).float().numpy()
								return images




					@staticmethod
					def 						A     (       _A       :						Optional[Any]     )  ->    Any:
								return 2.0 * images - 1.0




					@staticmethod
					def 						A     (       _A       :						List[str]     )  ->    str:
								return (images / 2 + 0.5).clamp(0							,				1     )




					@staticmethod
					def 						A     (       _A       :						PIL.Image.Image     )  ->    PIL.Image.Image:
								UpperCAmelCase_     :   Union[str, Any]       =   image.convert('''RGB'''     )
								return image




					def 						A     (       self       :						List[Any]							,				_A       :						PIL.Image.Image							,				_A       :						Optional[int] = None							,				_A       :						Optional[int] = None							,				)  ->    PIL.Image.Image:
								if height is None:
											UpperCAmelCase_     :   Dict       =   image.height
								if width is None:
											UpperCAmelCase_     :   List[str]       =   image.width

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




					def 						A     (       self       :						List[str]							,				_A       :						Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]							,				_A       :						Optional[int] = None							,				_A       :						Optional[int] = None							,				)  ->    torch.Tensor:
								UpperCAmelCase_     :   List[Any]       =   (PIL.Image.Image, np.ndarray, torch.Tensor)
								if isinstance(_A							,				_A     ):
											UpperCAmelCase_     :   Dict       =   [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:
														UpperCAmelCase_     :   Any       =   [self.convert_to_rgb(_A     ) for i in image]
											if self.config.do_resize:
														UpperCAmelCase_     :   Dict       =   [self.resize(_A							,				_A							,				_A     ) for i in image]
											UpperCAmelCase_     :   Tuple       =   self.pil_to_numpy(_A     )  # to np
											UpperCAmelCase_     :   Tuple       =   self.numpy_to_pt(_A     )  # to pt

								elif isinstance(image[0]							,				np.ndarray     ):
											UpperCAmelCase_     :   int       =   np.concatenate(_A							,				axis=0     ) if image[0].ndim == 4 else np.stack(_A							,				axis=0     )
											UpperCAmelCase_     :   List[Any]       =   self.numpy_to_pt(_A     )
											UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_     :   int       =   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_     :   int       =   torch.cat(_A							,				axis=0     ) if image[0].ndim == 4 else torch.stack(_A							,				axis=0     )
											UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_     :   Dict       =   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_     :   Dict       =   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							,				)
											UpperCAmelCase_     :   Any       =   False

								if do_normalize:
											UpperCAmelCase_     :   List[str]       =   self.normalize(_A     )

								return image





					def 						A     (       self       :						str							,				_A       :						torch.FloatTensor							,				_A       :						str = "pil"							,				_A       :						Optional[List[bool]] = None							,				)  ->    Optional[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"]:
											UpperCAmelCase_     :   List[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'''							,				_A							,				standard_warn=_A     )
											UpperCAmelCase_     :   Tuple       =   '''np'''

								if output_type == "latent":
											return image

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

								UpperCAmelCase_     :   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

								UpperCAmelCase_     :   Union[str, Any]       =   self.pt_to_numpy(_A     )

								if output_type == "np":
											return image

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






class 				snake_case__     (			UpperCamelCase):
					a_       =			CONFIG_NAME




					@register_to_config
					def __init__(       self       :						Optional[Any]							,				_A       :						bool = True							,				_A       :						int = 8							,				_A       :						str = "lanczos"							,				_A       :						bool = True							,				)  ->    int:
								super().__init__()




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

								return pil_images




					@staticmethod
					def 						A     (       _A       :						List[str]     )  ->    Union[str, Any]:
								return image[:, :, 1] * 2**8 + image[:, :, 2]




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

								return pil_images





					def 						A     (       self       :						List[str]							,				_A       :						torch.FloatTensor							,				_A       :						str = "pil"							,				_A       :						Optional[List[bool]] = None							,				)  ->    Optional[Any]:
								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"]:
											UpperCAmelCase_     :   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'''							,				_A							,				standard_warn=_A     )
											UpperCAmelCase_     :   Tuple       =   '''np'''

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

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

								UpperCAmelCase_     :   Any       =   self.pt_to_numpy(_A     )

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










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase		:		Optional[Any]								=			{'configuration_vit_hybrid': ['VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTHybridConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		Optional[int]								=			[
						    'VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTHybridForImageClassification',
						    'ViTHybridModel',
						    'ViTHybridPreTrainedModel',
						]

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		str								=			['ViTHybridImageProcessor']


if TYPE_CHECKING:
						from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_hybrid import (
												    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTHybridForImageClassification,
												    ViTHybridModel,
												    ViTHybridPreTrainedModel,
												)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
						import sys

						_UpperCamelCase		:		Tuple								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


