





"""simple docstring"""





import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor

class 		lowercase ( unittest.TestCase ):





      @property
      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	1
            lowerCAmelCase  						=	3
            lowerCAmelCase  						=	8
            lowerCAmelCase  						=	8

            lowerCAmelCase  						=	torch.rand((batch_size, num_channels, height, width)			)

            return sample





      def    _snake_case      (	self				,						lowercase			)		->					List[Any]:
            if isinstance(image[0]				,						PIL.Image.Image			):
                  return np.stack([np.array(lowercase			) for i in image]				,						axis=0			)
            elif isinstance(lowercase				,						torch.Tensor			):
                  return image.cpu().numpy().transpose(0				,						2				,						3				,						1			)
            return image





      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	VaeImageProcessor(do_resize=lowercase				,						do_normalize=lowercase			)

            lowerCAmelCase  						=	self.dummy_sample
            lowerCAmelCase  						=	self.to_np(lowercase			)

            for output_type in ["pt", "np", "pil"]:
                  lowerCAmelCase  						=	image_processor.postprocess(
                      image_processor.preprocess(lowercase			)				,						output_type=lowercase				,						)
                  lowerCAmelCase  						=	self.to_np(lowercase			)
                  lowerCAmelCase  						=	(input_np * 255).round() if output_type == """pil""" else input_np
                  assert (
                      np.abs(in_np - out_np			).max() < 1e-6
                  ), f'decoded output does not match input for output_type {output_type}'





      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	VaeImageProcessor(do_resize=lowercase				,						do_normalize=lowercase			)
            lowerCAmelCase  						=	self.dummy_sample.cpu().numpy().transpose(0				,						2				,						3				,						1			)

            for output_type in ["pt", "np", "pil"]:
                  lowerCAmelCase  						=	image_processor.postprocess(image_processor.preprocess(lowercase			)				,						output_type=lowercase			)

                  lowerCAmelCase  						=	self.to_np(lowercase			)
                  lowerCAmelCase  						=	(input_np * 255).round() if output_type == """pil""" else input_np
                  assert (
                      np.abs(in_np - out_np			).max() < 1e-6
                  ), f'decoded output does not match input for output_type {output_type}'





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	VaeImageProcessor(do_resize=lowercase				,						do_normalize=lowercase			)

            lowerCAmelCase  						=	self.dummy_sample.cpu().numpy().transpose(0				,						2				,						3				,						1			)
            lowerCAmelCase  						=	image_processor.numpy_to_pil(lowercase			)

            for output_type in ["pt", "np", "pil"]:
                  lowerCAmelCase  						=	image_processor.postprocess(image_processor.preprocess(lowercase			)				,						output_type=lowercase			)
                  for i, o in zip(lowercase				,						lowercase			):
                        lowerCAmelCase  						=	np.array(lowercase			)
                        lowerCAmelCase  						=	self.to_np(lowercase			) if output_type == """pil""" else (self.to_np(lowercase			) * 255).round()
                        assert (
                            np.abs(in_np - out_np			).max() < 1e-6
                        ), f'decoded output does not match input for output_type {output_type}'





      def    _snake_case      (	self			)		->					str:
            lowerCAmelCase  						=	VaeImageProcessor(do_resize=lowercase				,						do_normalize=lowercase			)

            lowerCAmelCase  						=	self.dummy_sample
            lowerCAmelCase  						=	input_pt_ad.squeeze(0			)

            lowerCAmelCase  						=	image_processor.postprocess(
                image_processor.preprocess(lowercase			)				,						output_type="""np"""				,						)
            lowerCAmelCase  						=	image_processor.postprocess(
                image_processor.preprocess(lowercase			)				,						output_type="""np"""				,						)

            lowerCAmelCase  						=	self.to_np(self.dummy_sample			)
            lowerCAmelCase  						=	input_np_ad.squeeze(0			)

            lowerCAmelCase  						=	image_processor.postprocess(
                image_processor.preprocess(lowercase			)				,						output_type="""np"""				,						)
            lowerCAmelCase  						=	image_processor.postprocess(
                image_processor.preprocess(lowercase			)				,						output_type="""np"""				,						)

            assert np.abs(out_pt_ad - out_pt_ad			).max() < 1e-6
            assert np.abs(out_np_ad - out_np_ad			).max() < 1e-6





      def    _snake_case      (	self			)		->					Optional[int]:
            lowerCAmelCase  						=	VaeImageProcessor(do_resize=lowercase				,						do_normalize=lowercase			)

            lowerCAmelCase  						=	self.dummy_sample
            lowerCAmelCase  						=	list(lowercase			)

            lowerCAmelCase  						=	image_processor.postprocess(
                image_processor.preprocess(lowercase			)				,						output_type="""np"""				,						)

            lowerCAmelCase  						=	image_processor.postprocess(
                image_processor.preprocess(lowercase			)				,						output_type="""np"""				,						)

            lowerCAmelCase  						=	self.to_np(self.dummy_sample			)
            list(lowercase			)

            lowerCAmelCase  						=	image_processor.postprocess(
                image_processor.preprocess(lowercase			)				,						output_type="""np"""				,						)

            lowerCAmelCase  						=	image_processor.postprocess(
                image_processor.preprocess(lowercase			)				,						output_type="""np"""				,						)

            assert np.abs(out_pt_ad - out_pt_list			).max() < 1e-6
            assert np.abs(out_np_ad - out_np_list			).max() < 1e-6













"""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 		lowercase ( unittest.TestCase ):





      def __init__(	self				,						lowercase				,						lowercase=7				,						lowercase=3				,						lowercase=18				,						lowercase=30				,						lowercase=400				,						lowercase=True				,						lowercase=None				,						lowercase=0.875				,						lowercase=True				,						lowercase=[0.5, 0.5, 0.5]				,						lowercase=[0.5, 0.5, 0.5]				,						)		->					List[Any]:
            lowerCAmelCase  						=	size if size is not None else {"""shortest_edge""": 20}
            lowerCAmelCase  						=	parent
            lowerCAmelCase  						=	batch_size
            lowerCAmelCase  						=	num_channels
            lowerCAmelCase  						=	image_size
            lowerCAmelCase  						=	min_resolution
            lowerCAmelCase  						=	max_resolution
            lowerCAmelCase  						=	do_resize
            lowerCAmelCase  						=	size
            lowerCAmelCase  						=	crop_pct
            lowerCAmelCase  						=	do_normalize
            lowerCAmelCase  						=	image_mean
            lowerCAmelCase  						=	image_std





      def    _snake_case      (	self			)		->					Optional[Any]:
            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 		lowercase ( _UpperCAmelCase					,  unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				ConvNextImageProcessor if is_vision_available() else None





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	ConvNextImageProcessingTester(self			)





      @property
      def    _snake_case      (	self			)		->					List[str]:
            return self.image_processor_tester.prepare_image_processor_dict()





      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            self.assertTrue(hasattr(lowercase				,						"""do_resize"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""size"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""crop_pct"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""do_normalize"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""image_mean"""			)			)
            self.assertTrue(hasattr(lowercase				,						"""image_std"""			)			)





      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	self.image_processing_class.from_dict(self.image_processor_dict			)
            self.assertEqual(image_processor.size				,						{"""shortest_edge""": 20}			)

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





      def    _snake_case      (	self			)		->					Optional[int]:
            pass





      def    _snake_case      (	self			)		->					Optional[int]:
            # Initialize image_processing
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            # create random PIL images
            lowerCAmelCase  						=	prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						Image.Image			)

            # Test not batched input
            lowerCAmelCase  						=	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
            lowerCAmelCase  						=	image_processing(lowercase				,						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    _snake_case      (	self			)		->					str:
            # Initialize image_processing
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            # create random numpy tensors
            lowerCAmelCase  						=	prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowercase				,						numpify=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						np.ndarray			)

            # Test not batched input
            lowerCAmelCase  						=	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
            lowerCAmelCase  						=	image_processing(lowercase				,						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    _snake_case      (	self			)		->					Optional[int]:
            # Initialize image_processing
            lowerCAmelCase  						=	self.image_processing_class(**self.image_processor_dict			)
            # create random PyTorch tensors
            lowerCAmelCase  						=	prepare_image_inputs(self.image_processor_tester				,						equal_resolution=lowercase				,						torchify=lowercase			)
            for image in image_inputs:
                  self.assertIsInstance(lowercase				,						torch.Tensor			)

            # Test not batched input
            lowerCAmelCase  						=	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
            lowerCAmelCase  						=	image_processing(lowercase				,						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"""],
                )				,						)






