






"""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__     ):
    UpperCAmelCase_			:					List[Any]     					=CONFIG_NAME







    @register_to_config
    def __init__(      self							,		_lowerCamelCase = True							,		_lowerCamelCase = 8							,		_lowerCamelCase = "lanczos"							,		_lowerCamelCase = True							,		_lowerCamelCase = False							,		):
      super().__init__()







    @staticmethod
    def       UpperCamelCase_				(      _lowerCamelCase	):




      if images.ndim == 3:
        lowercase							   =	images[None, ...]
      lowercase							   =	(images * 2_5_5).round().astype('uint8'	)
      if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        lowercase							   =	[Image.fromarray(image.squeeze()							,		mode='L'	) for image in images]
      else:
        lowercase							   =	[Image.fromarray(_A	) for image in images]

      return pil_images







    @staticmethod
    def       UpperCamelCase_				(      _lowerCamelCase	):




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

      return images







    @staticmethod
    def       UpperCamelCase_				(      _lowerCamelCase	):




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

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







    @staticmethod
    def       UpperCamelCase_				(      _lowerCamelCase	):
      lowercase							   =	images.cpu().permute(0							,		2							,		3							,		1	).float().numpy()
      return images







    @staticmethod
    def       UpperCamelCase_				(      _lowerCamelCase	):
      return 2.0 * images - 1.0







    @staticmethod
    def       UpperCamelCase_				(      _lowerCamelCase	):
      return (images / 2 + 0.5).clamp(0							,		1	)







    @staticmethod
    def       UpperCamelCase_				(      _lowerCamelCase	):
      lowercase							   =	image.convert('RGB'	)
      return image







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		):




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

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







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = None							,		):
      lowercase							   =	(PIL.Image.Image, np.ndarray, torch.Tensor)
      if isinstance(_A							,		_A	):
        lowercase							   =	[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:
          lowercase							   =	[self.convert_to_rgb(_A	) for i in image]
        if self.config.do_resize:
          lowercase							   =	[self.resize(_A							,		_A							,		_A	) for i in image]
        lowercase							   =	self.pil_to_numpy(_A	)  # to np
        lowercase							   =	self.numpy_to_pt(_A	)  # to pt

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

      if do_normalize:
        lowercase							   =	self.normalize(_A	)

      return image







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = None							,		):




      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"]:
        lowercase							   =	(
            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	)
        lowercase							   =	'''np'''

      if output_type == "latent":
        return image

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

      lowercase							   =	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

      lowercase							   =	self.pt_to_numpy(_A	)

      if output_type == "np":
        return image

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


class 	a					(       lowerCAmelCase__     ):
    UpperCAmelCase_			:					str     					=CONFIG_NAME







    @register_to_config
    def __init__(      self							,		_lowerCamelCase = True							,		_lowerCamelCase = 8							,		_lowerCamelCase = "lanczos"							,		_lowerCamelCase = True							,		):
      super().__init__()







    @staticmethod
    def       UpperCamelCase_				(      _lowerCamelCase	):




      if images.ndim == 3:
        lowercase							   =	images[None, ...]
      lowercase							   =	(images * 2_5_5).round().astype('uint8'	)
      if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        lowercase							   =	[Image.fromarray(image.squeeze()							,		mode='L'	) for image in images]
      else:
        lowercase							   =	[Image.fromarray(image[:, :, :3]	) for image in images]

      return pil_images







    @staticmethod
    def       UpperCamelCase_				(      _lowerCamelCase	):
      return image[:, :, 1] * 2**8 + image[:, :, 2]







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):




      if images.ndim == 3:
        lowercase							   =	images[None, ...]
      lowercase							   =	images[:, :, :, 3:]
      if images.shape[-1] == 6:
        lowercase							   =	(images_depth * 2_5_5).round().astype('uint8'	)
        lowercase							   =	[
            Image.fromarray(self.rgblike_to_depthmap(_A	)							,		mode='I;16'	) for image_depth in images_depth
        ]
      elif images.shape[-1] == 4:
        lowercase							   =	(images_depth * 6_5_5_3_5.0).astype(np.uintaa	)
        lowercase							   =	[Image.fromarray(_A							,		mode='I;16'	) for image_depth in images_depth]
      else:
        raise Exception('Not supported'	)

      return pil_images







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = "pil"							,		_lowerCamelCase = None							,		):




      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"]:
        lowercase							   =	(
            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	)
        lowercase							   =	'''np'''

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

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

      lowercase							   =	self.pt_to_numpy(_A	)

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

class 	a					(       unittest.TestCase     ):







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		):
      lowercase							   =	size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase							   =	parent
      lowercase							   =	batch_size
      lowercase							   =	num_channels
      lowercase							   =	image_size
      lowercase							   =	min_resolution
      lowercase							   =	max_resolution
      lowercase							   =	do_resize
      lowercase							   =	size
      lowercase							   =	do_normalize
      lowercase							   =	image_mean
      lowercase							   =	image_std







    def       UpperCamelCase_				(      self	):
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }


@require_torch
@require_vision
class 	a					(       a_,    unittest.TestCase     ):
    UpperCAmelCase_			:					int     					=ViTImageProcessor if is_vision_available() else None







    def       UpperCamelCase_				(      self	):
      lowercase							   =	ViTImageProcessingTester(self	)







    @property
    def       UpperCamelCase_				(      self	):
      return self.image_processor_tester.prepare_image_processor_dict()







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_mean'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_std'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_normalize'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_resize'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'size'	)	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size							,		{'height': 1_8, 'width': 1_8}	)

      lowercase							   =	self.image_processing_class.from_dict(self.image_processor_dict							,		size=4_2	)
      self.assertEqual(image_processor.size							,		{'height': 4_2, 'width': 4_2}	)







    def       UpperCamelCase_				(      self	):
      pass







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		Image.Image	)

      # Test not batched input
      lowercase							   =	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['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	image_processing(_lowerCamelCase							,		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['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		numpify=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		np.ndarray	)

      # Test not batched input
      lowercase							   =	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['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	image_processing(_lowerCamelCase							,		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['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		torchify=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		torch.Tensor	)

      # Test not batched input
      lowercase							   =	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['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	image_processing(_lowerCamelCase							,		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['height'],
              self.image_processor_tester.size['width'],
          )							,		)


