"""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__     ( _a						):
   snake_case_				=   CONFIG_NAME


   @register_to_config
   def __init__(  self,					_UpperCAmelCase = True,					_UpperCAmelCase = 8,					_UpperCAmelCase = "lanczos",					_UpperCAmelCase = True,					_UpperCAmelCase = False,					):
        '''simple docstring'''



        super().__init__()


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



        if images.ndim == 3:
             lowercase__								=				images[None, ...]
        lowercase__								=				(images * 255).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(_UpperCAmelCase	) for image in images]

        return pil_images


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



        if not isinstance(_UpperCAmelCase,					_UpperCAmelCase	):
             lowercase__								=				[images]
        lowercase__								=				[np.array(_UpperCAmelCase	).astype(np.floataa	) / 255.0 for image in images]
        lowercase__								=				np.stack(_UpperCAmelCase,					axis=0	)

        return images


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



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

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


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



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


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



        return 2.0 * images - 1.0


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



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


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



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


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None,					_UpperCAmelCase = None,					):
        '''simple docstring'''



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

        lowercase__			,    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 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = None,					_UpperCAmelCase = None,					):
        '''simple docstring'''



        lowercase__								=				(PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(_UpperCAmelCase,					_UpperCAmelCase	):
             lowercase__								=				[image]
        elif not (isinstance(_UpperCAmelCase,					_UpperCAmelCase	) and all(isinstance(_UpperCAmelCase,					_UpperCAmelCase	) for i in image	)):
             raise ValueError(
                 F'''Input is in incorrect format: {[type(_UpperCAmelCase	) for i in image]}. Currently, we only support {', '.join(_UpperCAmelCase	)}'''	)

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

        elif isinstance(image[0],					np.ndarray	):
             lowercase__								=				np.concatenate(_UpperCAmelCase,					axis=0	) if image[0].ndim == 4 else np.stack(_UpperCAmelCase,					axis=0	)
             lowercase__								=				self.numpy_to_pt(_UpperCAmelCase	)
             lowercase__			,    lowercase__			,    lowercase__			,    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(_UpperCAmelCase,					axis=0	) if image[0].ndim == 4 else torch.stack(_UpperCAmelCase,					axis=0	)
             lowercase__			,    lowercase__			,    lowercase__			,    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()}]''',					_UpperCAmelCase,					)
             lowercase__								=				False

        if do_normalize:
             lowercase__								=				self.normalize(_UpperCAmelCase	)

        return image


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = "pil",					_UpperCAmelCase = None,					):
        '''simple docstring'''



        if not isinstance(_UpperCAmelCase,					torch.Tensor	):
             raise ValueError(
                 F'''Input for postprocessing is in incorrect format: {type(_UpperCAmelCase	)}. 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",					_UpperCAmelCase,					standard_warn=_UpperCAmelCase	)
             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(_UpperCAmelCase	)

        if output_type == "np":
             return image

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






class  a__     ( _a						):
   snake_case_				=   CONFIG_NAME


   @register_to_config
   def __init__(  self,					_UpperCAmelCase = True,					_UpperCAmelCase = 8,					_UpperCAmelCase = "lanczos",					_UpperCAmelCase = True,					):
        '''simple docstring'''



        super().__init__()


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



        if images.ndim == 3:
             lowercase__								=				images[None, ...]
        lowercase__								=				(images * 255).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 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



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


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        if images.ndim == 3:
             lowercase__								=				images[None, ...]
        lowercase__								=				images[:, :, :, 3:]
        if images.shape[-1] == 6:
             lowercase__								=				(images_depth * 255).round().astype("uint8"	)
             lowercase__								=				[
                 Image.fromarray(self.rgblike_to_depthmap(_UpperCAmelCase	),					mode="I;16"	) for image_depth in images_depth
             ]
        elif images.shape[-1] == 4:
             lowercase__								=				(images_depth * 65_535.0).astype(np.uintaa	)
             lowercase__								=				[Image.fromarray(_UpperCAmelCase,					mode="I;16"	) for image_depth in images_depth]
        else:
             raise Exception("Not supported"	)

        return pil_images


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase = "pil",					_UpperCAmelCase = None,					):
        '''simple docstring'''



        if not isinstance(_UpperCAmelCase,					torch.Tensor	):
             raise ValueError(
                 F'''Input for postprocessing is in incorrect format: {type(_UpperCAmelCase	)}. 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",					_UpperCAmelCase,					standard_warn=_UpperCAmelCase	)
             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(_UpperCAmelCase	)

        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(_UpperCAmelCase	), self.numpy_to_depth(_UpperCAmelCase	)
        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 OwlViTImageProcessor






class  a__     ( unittest.TestCase						):


   def __init__(  self,					_UpperCAmelCase,					_UpperCAmelCase=7,					_UpperCAmelCase=3,					_UpperCAmelCase=18,					_UpperCAmelCase=30,					_UpperCAmelCase=400,					_UpperCAmelCase=True,					_UpperCAmelCase=None,					_UpperCAmelCase=True,					_UpperCAmelCase=None,					_UpperCAmelCase=True,					_UpperCAmelCase=[0.48_145_466, 0.4_578_275, 0.40_821_073],					_UpperCAmelCase=[0.26_862_954, 0.26_130_258, 0.27_577_711],					_UpperCAmelCase=True,					):
        '''simple docstring'''



        lowercase__								=				parent
        lowercase__								=				batch_size
        lowercase__								=				num_channels
        lowercase__								=				image_size
        lowercase__								=				min_resolution
        lowercase__								=				max_resolution
        lowercase__								=				do_resize
        lowercase__								=				size if size is not None else {"height": 18, "width": 18}
        lowercase__								=				do_center_crop
        lowercase__								=				crop_size if crop_size is not None else {"height": 18, "width": 18}
        lowercase__								=				do_normalize
        lowercase__								=				image_mean
        lowercase__								=				image_std
        lowercase__								=				do_convert_rgb


   def 						snake_case__						(  self	):
        '''simple docstring'''



        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
        }






@require_torch
@require_vision
class  a__     ( _a       ,       unittest.TestCase						):
   snake_case_				=   OwlViTImageProcessor if is_vision_available() else None


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				OwlViTImageProcessingTester(self	)


   @property
   def 						snake_case__						(  self	):
        '''simple docstring'''



        return self.image_processor_tester.prepare_image_processor_dict()


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_resize"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"size"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_center_crop"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"center_crop"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_normalize"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"image_mean"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"image_std"	)	)
        self.assertTrue(hasattr(_UpperCAmelCase,					"do_convert_rgb"	)	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size,					{"height": 18, "width": 18}	)
        self.assertEqual(image_processor.crop_size,					{"height": 18, "width": 18}	)

        lowercase__								=				self.image_processing_class.from_dict(self.image_processor_dict,					size=42,					crop_size=84	)
        self.assertEqual(image_processor.size,					{"height": 42, "width": 42}	)
        self.assertEqual(image_processor.crop_size,					{"height": 84, "width": 84}	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        lowercase__								=				prepare_image_inputs(self.image_processor_tester,					equal_resolution=_UpperCAmelCase	)

        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,					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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)

        # Test batched
        lowercase__								=				image_processing(_UpperCAmelCase,					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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        lowercase__								=				prepare_image_inputs(self.image_processor_tester,					equal_resolution=_UpperCAmelCase,					numpify=_UpperCAmelCase	)
        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,					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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)

        # Test batched
        lowercase__								=				image_processing(_UpperCAmelCase,					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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        lowercase__								=				prepare_image_inputs(self.image_processor_tester,					equal_resolution=_UpperCAmelCase,					torchify=_UpperCAmelCase	)
        for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase,					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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)

        # Test batched
        lowercase__								=				image_processing(_UpperCAmelCase,					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.crop_size["height"],
                self.image_processor_tester.crop_size["width"],
            ),					)




