





'''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    __a (lowerCamelCase       ):
							__a   :				int       =    CONFIG_NAME

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







													"""simple docstring"""
													super().__init__()

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







													"""simple docstring"""
													if images.ndim == 3:
																			UpperCAmelCase_     : List[str]         =					images[None, ...]
													UpperCAmelCase_     : Any         =					(images * 2_55).round().astype('''uint8'''				)
													if images.shape[-1] == 1:
																			# special case for grayscale (single channel) images
																			UpperCAmelCase_     : Optional[Any]         =					[Image.fromarray(image.squeeze()    ,       mode='''L'''				) for image in images]
													else:
																			UpperCAmelCase_     : Optional[Any]         =					[Image.fromarray(__magic_name__				) for image in images]

													return pil_images

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







													"""simple docstring"""
													if not isinstance(__magic_name__    ,       __magic_name__				):
																			UpperCAmelCase_     : Any         =					[images]
													UpperCAmelCase_     : int         =					[np.array(__magic_name__				).astype(np.floataa				) / 2_5_5.0 for image in images]
													UpperCAmelCase_     : List[Any]         =					np.stack(__magic_name__    ,       axis=0				)

													return images

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







													"""simple docstring"""
													if images.ndim == 3:
																			UpperCAmelCase_     : Any         =					images[..., None]

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

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







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					images.cpu().permute(0    ,       2    ,       3    ,       1				).float().numpy()
													return images

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







													"""simple docstring"""
													return 2.0 * images - 1.0

							@staticmethod
							def       UpperCAmelCase__		(							__magic_name__							:       Any				)   ->    Any:







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

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







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					image.convert('''RGB'''				)
													return image

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







													"""simple docstring"""
													if height is None:
																			UpperCAmelCase_     : Union[str, Any]         =					image.height
													if width is None:
																			UpperCAmelCase_     : Dict         =					image.width

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

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







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					(PIL.Image.Image, np.ndarray, torch.Tensor)
													if isinstance(__magic_name__    ,       __magic_name__				):
																			UpperCAmelCase_     : str         =					[image]
													elif not (isinstance(__magic_name__    ,       __magic_name__				) and all(isinstance(__magic_name__    ,       __magic_name__				) for i in image				)):
																			raise ValueError(
																			    F"""Input is in incorrect format: {[type(__magic_name__				) for i in image]}. Currently, we only support {", ".join(__magic_name__				)}"""				)

													if isinstance(image[0]    ,       PIL.Image.Image				):
																			if self.config.do_convert_rgb:
																									UpperCAmelCase_     : Dict         =					[self.convert_to_rgb(__magic_name__				) for i in image]
																			if self.config.do_resize:
																									UpperCAmelCase_     : Tuple         =					[self.resize(__magic_name__    ,       __magic_name__    ,       __magic_name__				) for i in image]
																			UpperCAmelCase_     : int         =					self.pil_to_numpy(__magic_name__				)  # to np
																			UpperCAmelCase_     : Tuple         =					self.numpy_to_pt(__magic_name__				)  # to pt

													elif isinstance(image[0]    ,       np.ndarray				):
																			UpperCAmelCase_     : Dict         =					np.concatenate(__magic_name__    ,       axis=0				) if image[0].ndim == 4 else np.stack(__magic_name__    ,       axis=0				)
																			UpperCAmelCase_     : Dict         =					self.numpy_to_pt(__magic_name__				)
																			UpperCAmelCase_  ,    UpperCAmelCase_  ,    UpperCAmelCase_  ,    UpperCAmelCase_     : 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				):
																			UpperCAmelCase_     : str         =					torch.cat(__magic_name__    ,       axis=0				) if image[0].ndim == 4 else torch.stack(__magic_name__    ,       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_     : 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()}]"""    ,       __magic_name__    ,       )
																			UpperCAmelCase_     : Optional[Any]         =					False

													if do_normalize:
																			UpperCAmelCase_     : str         =					self.normalize(__magic_name__				)

													return image



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







													"""simple docstring"""
													if not isinstance(__magic_name__    ,       torch.Tensor				):
																			raise ValueError(
																			    F"""Input for postprocessing is in incorrect format: {type(__magic_name__				)}. We only support pytorch tensor"""				)
													if output_type not in ["latent", "pt", "np", "pil"]:
																			UpperCAmelCase_     : 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'''    ,       __magic_name__    ,       standard_warn=__magic_name__				)
																			UpperCAmelCase_     : int         =					'''np'''

													if output_type == "latent":
																			return image

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

													UpperCAmelCase_     : 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_     : Optional[Any]         =					self.pt_to_numpy(__magic_name__				)

													if output_type == "np":
																			return image

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





class    __a (lowerCamelCase       ):
							__a   :				Dict       =    CONFIG_NAME

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







													"""simple docstring"""
													super().__init__()

							@staticmethod
							def       UpperCAmelCase__		(							__magic_name__							:       Any				)   ->    int:







													"""simple docstring"""
													if images.ndim == 3:
																			UpperCAmelCase_     : Any         =					images[None, ...]
													UpperCAmelCase_     : Optional[Any]         =					(images * 2_55).round().astype('''uint8'''				)
													if images.shape[-1] == 1:
																			# special case for grayscale (single channel) images
																			UpperCAmelCase_     : Optional[int]         =					[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       UpperCAmelCase__		(							__magic_name__							:       Any				)   ->    Tuple:







													"""simple docstring"""
													return image[:, :, 1] * 2**8 + image[:, :, 2]

							def       UpperCAmelCase__		(							self							:       Tuple    ,       __magic_name__							:       Dict				)   ->    str:







													"""simple docstring"""
													if images.ndim == 3:
																			UpperCAmelCase_     : Union[str, Any]         =					images[None, ...]
													UpperCAmelCase_     : Union[str, Any]         =					images[:, :, :, 3:]
													if images.shape[-1] == 6:
																			UpperCAmelCase_     : List[Any]         =					(images_depth * 2_55).round().astype('''uint8'''				)
																			UpperCAmelCase_     : Any         =					[
																			    Image.fromarray(self.rgblike_to_depthmap(__magic_name__				)    ,       mode='''I;16'''				) for image_depth in images_depth
																			]
													elif images.shape[-1] == 4:
																			UpperCAmelCase_     : List[Any]         =					(images_depth * 6_5_5_3_5.0).astype(np.uintaa				)
																			UpperCAmelCase_     : List[Any]         =					[Image.fromarray(__magic_name__    ,       mode='''I;16'''				) for image_depth in images_depth]
													else:
																			raise Exception('''Not supported'''				)

													return pil_images



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







													"""simple docstring"""
													if not isinstance(__magic_name__    ,       torch.Tensor				):
																			raise ValueError(
																			    F"""Input for postprocessing is in incorrect format: {type(__magic_name__				)}. 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'''    ,       __magic_name__    ,       standard_warn=__magic_name__				)
																			UpperCAmelCase_     : str         =					'''np'''

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

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

													UpperCAmelCase_     : Dict         =					self.pt_to_numpy(__magic_name__				)

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

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












'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor


class    __a (unittest.TestCase       ):

							def __init__(							self							:       Dict    ,       __magic_name__							:       List[str]    ,       __magic_name__							:       str=7    ,       __magic_name__							:       Optional[int]=3    ,       __magic_name__							:       Union[str, Any]=18    ,       __magic_name__							:       Dict=30    ,       __magic_name__							:       List[Any]=4_00    ,       __magic_name__							:       Dict=True    ,       __magic_name__							:       Union[str, Any]=None    ,       __magic_name__							:       Optional[Any]=0.8_7_5    ,       __magic_name__							:       List[Any]=True    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       __magic_name__							:       Optional[int]=[0.5, 0.5, 0.5]    ,       )   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					size if size is not None else {'''shortest_edge''': 20}
													UpperCAmelCase_     : Optional[Any]         =					parent
													UpperCAmelCase_     : Any         =					batch_size
													UpperCAmelCase_     : Optional[Any]         =					num_channels
													UpperCAmelCase_     : Optional[Any]         =					image_size
													UpperCAmelCase_     : Optional[int]         =					min_resolution
													UpperCAmelCase_     : Optional[Any]         =					max_resolution
													UpperCAmelCase_     : Optional[int]         =					do_resize
													UpperCAmelCase_     : Optional[Any]         =					size
													UpperCAmelCase_     : int         =					crop_pct
													UpperCAmelCase_     : Optional[int]         =					do_normalize
													UpperCAmelCase_     : Dict         =					image_mean
													UpperCAmelCase_     : int         =					image_std



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







													"""simple docstring"""
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "crop_pct": self.crop_pct,
													}





@require_torch
@require_vision
class    __a (lowerCamelCase    ,						unittest.TestCase       ):
							__a   :				Dict       =    ConvNextImageProcessor if is_vision_available() else None

							def       UpperCAmelCase__		(							self							:       Optional[int]				)   ->    Dict:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					ConvNextImageProcessingTester(self				)

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







													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()

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







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_resize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''size'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''crop_pct'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''do_normalize'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_mean'''				)				)
													self.assertTrue(hasattr(__magic_name__    ,       '''image_std'''				)				)

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







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					self.image_processing_class.from_dict(self.image_processor_dict				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 20}				)

													UpperCAmelCase_     : List[Any]         =					self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42				)
													self.assertEqual(image_processor.size    ,       {'''shortest_edge''': 42}				)

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







													"""simple docstring"""
													pass

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







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : int         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       Image.Image				)

													# Test not batched input
													UpperCAmelCase_     : int         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Tuple         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

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







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Optional[Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													UpperCAmelCase_     : Tuple         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       numpify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       np.ndarray				)

													# Test not batched input
													UpperCAmelCase_     : int         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : Union[str, Any]         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )



							def       UpperCAmelCase__		(							self							:       str				)   ->    int:







													"""simple docstring"""
													# Initialize image_processing
													UpperCAmelCase_     : Union[str, Any]         =					self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													UpperCAmelCase_     : List[Any]         =					prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__magic_name__    ,       torchify=__magic_name__				)
													for image in image_inputs:
																			self.assertIsInstance(__magic_name__    ,       torch.Tensor				)

													# Test not batched input
													UpperCAmelCase_     : List[Any]         =					image_processing(image_inputs[0]    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )

													# Test batched
													UpperCAmelCase_     : str         =					image_processing(__magic_name__    ,       return_tensors='''pt'''				).pixel_values
													self.assertEqual(
													    encoded_images.shape    ,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''shortest_edge'''],
													        self.image_processor_tester.size['''shortest_edge'''],
													    )    ,       )





