






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_					(						lowercase__			):

						"""simple docstring"""



						snake_case__   					=			CONFIG_NAME



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




											'''simple docstring'''



											super().__init__()



						@staticmethod
						def 					UpperCAmelCase__				(__UpperCAmelCase:				np.ndarray      )						->    PIL.Image.Image:




											'''simple docstring'''



											if images.ndim == 3:
																__a				:	Optional[int]			=				images[None, ...]
											__a				:	Dict			=				(images * 255).round().astype("uint8"      )
											if images.shape[-1] == 1:
																# special case for grayscale (single channel) images
																__a				:	Dict			=				[Image.fromarray(image.squeeze()   ,				mode="L"      ) for image in images]
											else:
																__a				:	int			=				[Image.fromarray(_A      ) for image in images]

											return pil_images



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




											'''simple docstring'''



											if not isinstance(_A   ,				_A      ):
																__a				:	List[str]			=				[images]
											__a				:	str			=				[np.array(_A      ).astype(np.floataa      ) / 2_55.0 for image in images]
											__a				:	str			=				np.stack(_A   ,				axis=0      )

											return images



						@staticmethod
						def 					UpperCAmelCase__				(__UpperCAmelCase:				np.ndarray      )						->    torch.FloatTensor:




											'''simple docstring'''



											if images.ndim == 3:
																__a				:	Tuple			=				images[..., None]

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



						@staticmethod
						def 					UpperCAmelCase__				(__UpperCAmelCase:				torch.FloatTensor      )						->    np.ndarray:




											'''simple docstring'''



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



						@staticmethod
						def 					UpperCAmelCase__				(__UpperCAmelCase:				str      )						->    Union[str, Any]:




											'''simple docstring'''



											return 2.0 * images - 1.0



						@staticmethod
						def 					UpperCAmelCase__				(__UpperCAmelCase:				Any      )						->    Union[str, Any]:




											'''simple docstring'''



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



						@staticmethod
						def 					UpperCAmelCase__				(__UpperCAmelCase:				PIL.Image.Image      )						->    PIL.Image.Image:




											'''simple docstring'''



											__a				:	List[Any]			=				image.convert("RGB"      )
											return image



						def 					UpperCAmelCase__				(self:				List[Any]   ,				__UpperCAmelCase:				PIL.Image.Image   ,				__UpperCAmelCase:				Optional[int] = None   ,				__UpperCAmelCase:				Optional[int] = None   ,				)						->    PIL.Image.Image:




											'''simple docstring'''



											if height is None:
																__a				:	Tuple			=				image.height
											if width is None:
																__a				:	Optional[int]			=				image.width

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



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




											'''simple docstring'''



											__a				:	List[Any]			=				(PIL.Image.Image, np.ndarray, torch.Tensor)
											if isinstance(_A   ,				_A      ):
																__a				:	Optional[int]			=				[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:
																					__a				:	Optional[Any]			=				[self.convert_to_rgb(_A      ) for i in image]
																if self.config.do_resize:
																					__a				:	int			=				[self.resize(_A   ,				_A   ,				_A      ) for i in image]
																__a				:	Dict			=				self.pil_to_numpy(_A      )  # to np
																__a				:	int			=				self.numpy_to_pt(_A      )  # to pt

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

											if do_normalize:
																__a				:	int			=				self.normalize(_A      )

											return image



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




											'''simple docstring'''



											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"]:
																__a				:	Optional[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      )
																__a				:	Union[str, Any]			=				'np'

											if output_type == "latent":
																return image

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

											__a				:	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

											__a				:	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_					(						lowercase__			):

						"""simple docstring"""



						snake_case__   					=			CONFIG_NAME



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




											'''simple docstring'''



											super().__init__()



						@staticmethod
						def 					UpperCAmelCase__				(__UpperCAmelCase:				Union[str, Any]      )						->    Optional[int]:




											'''simple docstring'''



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

											return pil_images



						@staticmethod
						def 					UpperCAmelCase__				(__UpperCAmelCase:				Optional[int]      )						->    Optional[Any]:




											'''simple docstring'''



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



						def 					UpperCAmelCase__				(self:				Union[str, Any]   ,				__UpperCAmelCase:				Dict      )						->    Dict:




											'''simple docstring'''



											if images.ndim == 3:
																__a				:	Tuple			=				images[None, ...]
											__a				:	Any			=				images[:, :, :, 3:]
											if images.shape[-1] == 6:
																__a				:	List[str]			=				(images_depth * 255).round().astype("uint8"      )
																__a				:	Any			=				[
																    Image.fromarray(self.rgblike_to_depthmap(_A      )   ,				mode="I;16"      ) for image_depth in images_depth
																]
											elif images.shape[-1] == 4:
																__a				:	Optional[Any]			=				(images_depth * 65535.0).astype(np.uintaa      )
																__a				:	Any			=				[Image.fromarray(_A   ,				mode="I;16"      ) for image_depth in images_depth]
											else:
																raise Exception("Not supported"      )

											return pil_images



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




											'''simple docstring'''



											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"]:
																__a				:	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      )
																__a				:	List[str]			=				'np'

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

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

											__a				:	Optional[int]			=				self.pt_to_numpy(_A      )

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













import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput




@dataclass
class        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						snake_case__   					=			42
						snake_case__   					=			None
						snake_case__   					=			None




class        snake_case_					(						unittest.TestCase			):

						"""simple docstring"""



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Tuple:




											'''simple docstring'''



											__a				:	List[str]			=				ModelOutputTest(a=30      )
											self.assertEqual(x.a   ,				30      )
											self.assertIsNone(x.b      )
											self.assertIsNone(x.c      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Tuple			=				x.d



						def 					UpperCAmelCase__				(self:				Dict      )						->    Union[str, Any]:




											'''simple docstring'''



											__a				:	Union[str, Any]			=				ModelOutputTest(a=30   ,				b=10      )
											self.assertEqual(x[0]   ,				30      )
											self.assertEqual(x[1]   ,				10      )
											self.assertEqual(x[:2]   ,				(30, 10)      )
											self.assertEqual(x[:]   ,				(30, 10)      )

											__a				:	int			=				ModelOutputTest(a=30   ,				c=10      )
											self.assertEqual(x[0]   ,				30      )
											self.assertEqual(x[1]   ,				10      )
											self.assertEqual(x[:2]   ,				(30, 10)      )
											self.assertEqual(x[:]   ,				(30, 10)      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    List[Any]:




											'''simple docstring'''



											__a				:	Optional[Any]			=				ModelOutputTest(a=30   ,				b=10      )
											self.assertEqual(x["a"]   ,				30      )
											self.assertEqual(x["b"]   ,				10      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	List[str]			=				x["c"]

											__a				:	Union[str, Any]			=				ModelOutputTest(a=30   ,				c=10      )
											self.assertEqual(x["a"]   ,				30      )
											self.assertEqual(x["c"]   ,				10      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	int			=				x["b"]



						def 					UpperCAmelCase__				(self:				Union[str, Any]      )						->    Optional[int]:




											'''simple docstring'''



											__a				:	Union[str, Any]			=				ModelOutputTest(a=30      )
											self.assertEqual(list(x.keys()      )   ,				["a"]      )
											self.assertEqual(list(x.values()      )   ,				[30]      )
											self.assertEqual(list(x.items()      )   ,				[("a", 30)]      )
											self.assertEqual(list(__UpperCAmelCase      )   ,				["a"]      )

											__a				:	List[str]			=				ModelOutputTest(a=30   ,				b=10      )
											self.assertEqual(list(x.keys()      )   ,				["a", "b"]      )
											self.assertEqual(list(x.values()      )   ,				[30, 10]      )
											self.assertEqual(list(x.items()      )   ,				[("a", 30), ("b", 10)]      )
											self.assertEqual(list(__UpperCAmelCase      )   ,				["a", "b"]      )

											__a				:	str			=				ModelOutputTest(a=30   ,				c=10      )
											self.assertEqual(list(x.keys()      )   ,				["a", "c"]      )
											self.assertEqual(list(x.values()      )   ,				[30, 10]      )
											self.assertEqual(list(x.items()      )   ,				[("a", 30), ("c", 10)]      )
											self.assertEqual(list(__UpperCAmelCase      )   ,				["a", "c"]      )

											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Dict			=				x.update({"d": 20}      )
											with self.assertRaises(__UpperCAmelCase      ):
																del x["a"]
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Any			=				x.pop("a"      )
											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	Any			=				x.setdefault("d"   ,				32      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Optional[Any]:




											'''simple docstring'''



											__a				:	Optional[Any]			=				ModelOutputTest(a=30      )
											__a				:	int			=				10
											self.assertEqual(x.a   ,				10      )
											self.assertEqual(x["a"]   ,				10      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Any:




											'''simple docstring'''



											__a				:	Optional[int]			=				ModelOutputTest(a=30      )
											__a				:	int			=				10
											self.assertEqual(x.a   ,				10      )
											self.assertEqual(x["a"]   ,				10      )



						def 					UpperCAmelCase__				(self:				List[str]      )						->    Any:




											'''simple docstring'''



											__a				:	Dict			=				ModelOutputTest({"a": 30, "b": 10}      )
											self.assertEqual(list(x.keys()      )   ,				["a", "b"]      )
											self.assertEqual(x.a   ,				30      )
											self.assertEqual(x.b   ,				10      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    List[str]:




											'''simple docstring'''



											__a				:	List[Any]			=				ModelOutputTest([("a", 30), ("b", 10)]      )
											self.assertEqual(list(x.keys()      )   ,				["a", "b"]      )
											self.assertEqual(x.a   ,				30      )
											self.assertEqual(x.b   ,				10      )

											with self.assertRaises(__UpperCAmelCase      ):
																__a				:	List[Any]			=				ModelOutputTest([("a", 30), (10, 10)]      )

											__a				:	Optional[Any]			=				ModelOutputTest(a=(30, 30)      )
											self.assertEqual(list(x.keys()      )   ,				["a"]      )
											self.assertEqual(x.a   ,				(30, 30)      )





