




'''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	(			lowercase_							):
			lowerCAmelCase_  :   str		 =					CONFIG_NAME




			@register_to_config
			def __init__(      self       ,							a__ = True       ,							a__ = 8       ,							a__ = "lanczos"       ,							a__ = True       ,							a__ = False       ,							)       ->       Tuple:

										'''simple docstring'''




										super().__init__()




			@staticmethod
			def      lowerCAmelCase__       (      a__							)       ->       PIL.Image.Image:

										'''simple docstring'''




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

										return pil_images




			@staticmethod
			def      lowerCAmelCase__       (      a__							)       ->       np.ndarray:

										'''simple docstring'''




										if not isinstance(a__       ,							a__							):
																	snake_case_			      =   [images]
										snake_case_			      =   [np.array(a__							).astype(np.floataa							) / 2_5_5.0 for image in images]
										snake_case_			      =   np.stack(a__       ,							axis=0							)

										return images




			@staticmethod
			def      lowerCAmelCase__       (      a__							)       ->       torch.FloatTensor:

										'''simple docstring'''




										if images.ndim == 3:
																	snake_case_			      =   images[..., None]

										snake_case_			      =   torch.from_numpy(images.transpose(0       ,							3       ,							1       ,							2							)							)
										return images




			@staticmethod
			def      lowerCAmelCase__       (      a__							)       ->       np.ndarray:

										'''simple docstring'''




										snake_case_			      =   images.cpu().permute(0       ,							2       ,							3       ,							1							).float().numpy()
										return images




			@staticmethod
			def      lowerCAmelCase__       (      a__							)       ->       int:

										'''simple docstring'''




										return 2.0 * images - 1.0




			@staticmethod
			def      lowerCAmelCase__       (      a__							)       ->       Tuple:

										'''simple docstring'''




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




			@staticmethod
			def      lowerCAmelCase__       (      a__							)       ->       PIL.Image.Image:

										'''simple docstring'''




										snake_case_			      =   image.convert("RGB"							)
										return image




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__ = None       ,							a__ = None       ,							)       ->       PIL.Image.Image:

										'''simple docstring'''




										if height is None:
																	snake_case_			      =   image.height
										if width is None:
																	snake_case_			      =   image.width

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




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__ = None       ,							a__ = None       ,							)       ->       torch.Tensor:

										'''simple docstring'''




										snake_case_			      =   (PIL.Image.Image, np.ndarray, torch.Tensor)
										if isinstance(a__       ,							a__							):
																	snake_case_			      =   [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:
																								snake_case_			      =   [self.convert_to_rgb(a__							) for i in image]
																	if self.config.do_resize:
																								snake_case_			      =   [self.resize(a__       ,							a__       ,							a__							) for i in image]
																	snake_case_			      =   self.pil_to_numpy(a__							)  # to np
																	snake_case_			      =   self.numpy_to_pt(a__							)  # to pt

										elif isinstance(image[0]       ,							np.ndarray							):
																	snake_case_			      =   np.concatenate(a__       ,							axis=0							) if image[0].ndim == 4 else np.stack(a__       ,							axis=0							)
																	snake_case_			      =   self.numpy_to_pt(a__							)
																	snake_case_      ,   snake_case_      ,   snake_case_      ,   snake_case_			      =   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							):
																	snake_case_			      =   torch.cat(a__       ,							axis=0							) if image[0].ndim == 4 else torch.stack(a__       ,							axis=0							)
																	snake_case_      ,   snake_case_      ,   snake_case_      ,   snake_case_			      =   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]
										snake_case_			      =   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__       ,							)
																	snake_case_			      =   False

										if do_normalize:
																	snake_case_			      =   self.normalize(a__							)

										return image




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__ = "pil"       ,							a__ = 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"]:
																	snake_case_			      =   (
																	    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__							)
																	snake_case_			      =   "np"

										if output_type == "latent":
																	return image

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

										snake_case_			      =   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

										snake_case_			      =   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_							):
			lowerCAmelCase_  :   List[Any]		 =					CONFIG_NAME




			@register_to_config
			def __init__(      self       ,							a__ = True       ,							a__ = 8       ,							a__ = "lanczos"       ,							a__ = True       ,							)       ->       Tuple:

										'''simple docstring'''




										super().__init__()




			@staticmethod
			def      lowerCAmelCase__       (      a__							)       ->       Optional[Any]:

										'''simple docstring'''




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

										return pil_images




			@staticmethod
			def      lowerCAmelCase__       (      a__							)       ->       Any:

										'''simple docstring'''




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




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       Union[str, Any]:

										'''simple docstring'''




										if images.ndim == 3:
																	snake_case_			      =   images[None, ...]
										snake_case_			      =   images[:, :, :, 3:]
										if images.shape[-1] == 6:
																	snake_case_			      =   (images_depth * 255).round().astype("uint8"							)
																	snake_case_			      =   [
																	    Image.fromarray(self.rgblike_to_depthmap(a__							)       ,							mode="I;16"							) for image_depth in images_depth
																	]
										elif images.shape[-1] == 4:
																	snake_case_			      =   (images_depth * 6_5_5_3_5.0).astype(np.uintaa							)
																	snake_case_			      =   [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       ,							)       ->       Union[str, 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"]:
																	snake_case_			      =   (
																	    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__							)
																	snake_case_			      =   "np"

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

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

										snake_case_			      =   self.pt_to_numpy(a__							)

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





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 ViltImageProcessor




class 				_snake_case	(			unittest.TestCase							):




			def __init__(      self       ,							a__       ,							a__=7       ,							a__=3       ,							a__=18       ,							a__=30       ,							a__=400       ,							a__=True       ,							a__=None       ,							a__=2       ,							a__=True       ,							a__=[0.5, 0.5, 0.5]       ,							a__=[0.5, 0.5, 0.5]       ,							)       ->       Any:

										'''simple docstring'''




										snake_case_			      =   size if size is not None else {"shortest_edge": 30}
										snake_case_			      =   parent
										snake_case_			      =   batch_size
										snake_case_			      =   num_channels
										snake_case_			      =   image_size
										snake_case_			      =   min_resolution
										snake_case_			      =   max_resolution
										snake_case_			      =   do_resize
										snake_case_			      =   size
										snake_case_			      =   size_divisor
										snake_case_			      =   do_normalize
										snake_case_			      =   image_mean
										snake_case_			      =   image_std




			def      lowerCAmelCase__       (      self							)       ->       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,
										    "size_divisor": self.size_divisor,
										}




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__=False							)       ->       str:

										'''simple docstring'''




										if not batched:
																	snake_case_			      =   self.size["shortest_edge"]
																	snake_case_			      =   image_inputs[0]
																	if isinstance(a__       ,							Image.Image							):
																								snake_case_      ,   snake_case_			      =   image.size
																	else:
																								snake_case_      ,   snake_case_			      =   image.shape[1], image.shape[2]
																	snake_case_			      =   size / min(a__       ,							a__							)
																	if h < w:
																								snake_case_      ,   snake_case_			      =   size, scale * w
																	else:
																								snake_case_      ,   snake_case_			      =   scale * h, size

																	snake_case_			      =   int((1_333 / 800) * size							)
																	if max(a__       ,							a__							) > max_size:
																								snake_case_			      =   max_size / max(a__       ,							a__							)
																								snake_case_			      =   newh * scale
																								snake_case_			      =   neww * scale

																	snake_case_      ,   snake_case_			      =   int(newh + 0.5							), int(neww + 0.5							)
																	snake_case_      ,   snake_case_			      =   (
																	    newh // self.size_divisor * self.size_divisor,
																	    neww // self.size_divisor * self.size_divisor,
																	)

										else:
																	snake_case_			      =   []
																	for image in image_inputs:
																								snake_case_      ,   snake_case_			      =   self.get_expected_values([image]							)
																								expected_values.append((expected_height, expected_width)							)
																	snake_case_			      =   max(a__       ,							key=lambda a__							: item[0]							)[0]
																	snake_case_			      =   max(a__       ,							key=lambda a__							: item[1]							)[1]

										return expected_height, expected_width




@require_torch
@require_vision
class 				_snake_case	(			lowercase_		,		unittest.TestCase							):
			lowerCAmelCase_  :   Union[str, Any]		 =					ViltImageProcessor if is_vision_available() else None




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   ViltImageProcessingTester(self							)




			@property
			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()




			def      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(a__       ,							"image_mean"							)							)
										self.assertTrue(hasattr(a__       ,							"image_std"							)							)
										self.assertTrue(hasattr(a__       ,							"do_normalize"							)							)
										self.assertTrue(hasattr(a__       ,							"do_resize"							)							)
										self.assertTrue(hasattr(a__       ,							"size"							)							)
										self.assertTrue(hasattr(a__       ,							"size_divisor"							)							)




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size       ,							{"shortest_edge": 30}							)

										snake_case_			      =   self.image_processing_class.from_dict(self.image_processor_dict       ,							size=42							)
										self.assertEqual(image_processor.size       ,							{"shortest_edge": 42}							)




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

										'''simple docstring'''




										pass




			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										snake_case_			      =   prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__       ,							Image.Image							)

										# Test not batched input
										snake_case_			      =   image_processing(image_inputs[0]       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

										# Test batched
										snake_case_			      =   image_processing(a__       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__       ,							batched=a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )       ,							)




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										snake_case_			      =   prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=a__       ,							numpify=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__       ,							np.ndarray							)

										# Test not batched input
										snake_case_			      =   image_processing(image_inputs[0]       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

										# Test batched
										snake_case_			      =   image_processing(a__       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__       ,							batched=a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )       ,							)




			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										snake_case_			      =   prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=a__       ,							torchify=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__       ,							torch.Tensor							)

										# Test not batched input
										snake_case_			      =   image_processing(image_inputs[0]       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

										# Test batched
										snake_case_			      =   image_processing(a__       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__       ,							batched=a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )       ,							)




