
"""simple docstring"""


import random
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():
			import PIL

			from transformers import FlavaImageProcessor
			from transformers.image_utils import PILImageResampling
			from transformers.models.flava.image_processing_flava import (
			    FLAVA_CODEBOOK_MEAN,
			    FLAVA_CODEBOOK_STD,
			    FLAVA_IMAGE_MEAN,
			    FLAVA_IMAGE_STD,
			)
else:
			__A					=			__A					=			__A					=			__A					=			None







class 						lowerCamelCase__   (   unittest.TestCase							):





			'''simple docstring'''



			def __init__(				self	,	__UpperCAmelCase	,	__UpperCAmelCase=7	,	__UpperCAmelCase=3	,	__UpperCAmelCase=30	,	__UpperCAmelCase=4_00	,	__UpperCAmelCase=True	,	__UpperCAmelCase=None	,	__UpperCAmelCase=True	,	__UpperCAmelCase=None	,	__UpperCAmelCase=None	,	__UpperCAmelCase=True	,	__UpperCAmelCase=1 / 2_55	,	__UpperCAmelCase=True	,	__UpperCAmelCase=FLAVA_IMAGE_MEAN	,	__UpperCAmelCase=FLAVA_IMAGE_STD	,	__UpperCAmelCase=14	,	__UpperCAmelCase=75	,	__UpperCAmelCase=None	,	__UpperCAmelCase=16	,	__UpperCAmelCase=0.3	,	__UpperCAmelCase=None	,	__UpperCAmelCase=True	,	__UpperCAmelCase=None	,	__UpperCAmelCase=None	,	__UpperCAmelCase=True	,	__UpperCAmelCase=None	,	__UpperCAmelCase=True	,	__UpperCAmelCase=True	,	__UpperCAmelCase=FLAVA_CODEBOOK_MEAN	,	__UpperCAmelCase=FLAVA_CODEBOOK_STD	,	)					->	Tuple:
				_lowerCAmelCase								=size if size is not None else {"""height""": 2_24, """width""": 2_24}
				_lowerCAmelCase								=crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
				_lowerCAmelCase								=codebook_size if codebook_size is not None else {"""height""": 1_12, """width""": 1_12}
				_lowerCAmelCase								=codebook_crop_size if codebook_crop_size is not None else {"""height""": 1_12, """width""": 1_12}

				_lowerCAmelCase								=parent
				_lowerCAmelCase								=batch_size
				_lowerCAmelCase								=num_channels
				_lowerCAmelCase								=do_resize
				_lowerCAmelCase								=do_rescale
				_lowerCAmelCase								=rescale_factor
				_lowerCAmelCase								=min_resolution
				_lowerCAmelCase								=max_resolution
				_lowerCAmelCase								=size
				_lowerCAmelCase								=resample if resample is not None else PILImageResampling.BICUBIC
				_lowerCAmelCase								=do_normalize
				_lowerCAmelCase								=image_mean
				_lowerCAmelCase								=image_std
				_lowerCAmelCase								=do_center_crop
				_lowerCAmelCase								=crop_size

				_lowerCAmelCase								=input_size_patches
				_lowerCAmelCase								=total_mask_patches
				_lowerCAmelCase								=mask_group_max_patches
				_lowerCAmelCase								=mask_group_min_patches
				_lowerCAmelCase								=mask_group_min_aspect_ratio
				_lowerCAmelCase								=mask_group_max_aspect_ratio

				_lowerCAmelCase								=codebook_do_resize
				_lowerCAmelCase								=codebook_size
				_lowerCAmelCase								=codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
				_lowerCAmelCase								=codebook_do_center_crop
				_lowerCAmelCase								=codebook_crop_size
				_lowerCAmelCase								=codebook_do_map_pixels
				_lowerCAmelCase								=codebook_do_normalize
				_lowerCAmelCase								=codebook_image_mean
				_lowerCAmelCase								=codebook_image_std



			def    _lowerCAmelCase						(				self    )					->	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,
				    "resample": self.resample,
				    "do_rescale": self.do_rescale,
				    "rescale_factor": self.rescale_factor,
				    "do_center_crop": self.do_center_crop,
				    "crop_size": self.crop_size,
				    "input_size_patches": self.input_size_patches,
				    "total_mask_patches": self.total_mask_patches,
				    "mask_group_max_patches": self.mask_group_max_patches,
				    "mask_group_min_patches": self.mask_group_min_patches,
				    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
				    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
				    "codebook_do_resize": self.codebook_do_resize,
				    "codebook_size": self.codebook_size,
				    "codebook_resample": self.codebook_resample,
				    "codebook_do_center_crop": self.codebook_do_center_crop,
				    "codebook_crop_size": self.codebook_crop_size,
				    "codebook_do_map_pixels": self.codebook_do_map_pixels,
				    "codebook_do_normalize": self.codebook_do_normalize,
				    "codebook_image_mean": self.codebook_image_mean,
				    "codebook_image_std": self.codebook_image_std,
				}



			def    _lowerCAmelCase						(				self    )					->	Tuple:
				return (self.size["height"], self.size["width"])



			def    _lowerCAmelCase						(				self    )					->	Dict:
				return (
				    (self.input_size_patches, self.input_size_patches)
				    if not isinstance(self.input_size_patches	,	__UpperCAmelCase    )
				    else self.input_size_patches
				)



			def    _lowerCAmelCase						(				self    )					->	Any:
				return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class 						lowerCamelCase__   (   __magic_name__					,      unittest.TestCase							):





			'''simple docstring'''
			lowerCamelCase       =						FlavaImageProcessor if is_vision_available() else None
			lowerCamelCase       =						None



			def    _lowerCAmelCase						(				self    )					->	Any:
				_lowerCAmelCase								=FlavaImageProcessingTester(self    )



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



			def    _lowerCAmelCase						(				self    )					->	str:
				_lowerCAmelCase								=self.image_processing_class(**self.image_processor_dict    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""image_mean"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""image_std"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""do_normalize"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""do_resize"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""resample"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""crop_size"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""do_center_crop"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""do_rescale"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""rescale_factor"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""masking_generator"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""codebook_do_resize"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""codebook_size"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""codebook_resample"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""codebook_do_center_crop"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""codebook_crop_size"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""codebook_do_map_pixels"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""codebook_do_normalize"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""codebook_image_mean"""    )    )
				self.assertTrue(hasattr(__UpperCAmelCase	,	"""codebook_image_std"""    )    )



			def    _lowerCAmelCase						(				self    )					->	Optional[Any]:
				_lowerCAmelCase								=self.image_processing_class.from_dict(self.image_processor_dict    )
				self.assertEqual(image_processor.size	,	{"""height""": 2_24, """width""": 2_24}    )
				self.assertEqual(image_processor.crop_size	,	{"""height""": 2_24, """width""": 2_24}    )
				self.assertEqual(image_processor.codebook_size	,	{"""height""": 1_12, """width""": 1_12}    )
				self.assertEqual(image_processor.codebook_crop_size	,	{"""height""": 1_12, """width""": 1_12}    )

				_lowerCAmelCase								=self.image_processing_class.from_dict(
				    self.image_processor_dict	,	size=42	,	crop_size=84	,	codebook_size=33	,	codebook_crop_size=66    )
				self.assertEqual(image_processor.size	,	{"""height""": 42, """width""": 42}    )
				self.assertEqual(image_processor.crop_size	,	{"""height""": 84, """width""": 84}    )
				self.assertEqual(image_processor.codebook_size	,	{"""height""": 33, """width""": 33}    )
				self.assertEqual(image_processor.codebook_crop_size	,	{"""height""": 66, """width""": 66}    )



			def    _lowerCAmelCase						(				self    )					->	Union[str, Any]:
				pass



			def    _lowerCAmelCase						(				self    )					->	Optional[Any]:
				# 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=__UpperCAmelCase    )
				for image in image_inputs:
					self.assertIsInstance(__UpperCAmelCase	,	PIL.Image.Image    )

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

				# Test no bool masked pos
				self.assertFalse("""bool_masked_pos""" in encoded_images    )

				_lowerCAmelCase		,				_lowerCAmelCase								=self.image_processor_tester.get_expected_image_size()

				self.assertEqual(
				    encoded_images.pixel_values.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

				# Test batched
				_lowerCAmelCase								=image_processing(__UpperCAmelCase	,	return_tensors="""pt"""    )
				_lowerCAmelCase		,				_lowerCAmelCase								=self.image_processor_tester.get_expected_image_size()

				# Test no bool masked pos
				self.assertFalse("""bool_masked_pos""" in encoded_images    )

				self.assertEqual(
				    encoded_images.pixel_values.shape	,	(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,	)



			def    _lowerCAmelCase						(				self	,	__UpperCAmelCase	,	__UpperCAmelCase    )					->	List[Any]:
				# Initialize image_processing
				_lowerCAmelCase								=self.image_processing_class(**self.image_processor_dict    )
				# create random tensors
				_lowerCAmelCase								=prepare_image_inputs(self.image_processor_tester	,	equal_resolution=__UpperCAmelCase	,	**__UpperCAmelCase    )
				for image in image_inputs:
					self.assertIsInstance(__UpperCAmelCase	,	__UpperCAmelCase    )

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

				_lowerCAmelCase		,				_lowerCAmelCase								=self.image_processor_tester.get_expected_image_size()
				self.assertEqual(
				    encoded_images.pixel_values.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

				_lowerCAmelCase								=image_processing(__UpperCAmelCase	,	return_image_mask=__UpperCAmelCase	,	return_tensors="""pt"""    )

				_lowerCAmelCase		,				_lowerCAmelCase								=self.image_processor_tester.get_expected_image_size()
				self.assertEqual(
				    encoded_images.pixel_values.shape	,	(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,	)

				_lowerCAmelCase		,				_lowerCAmelCase								=self.image_processor_tester.get_expected_mask_size()
				self.assertEqual(
				    encoded_images.bool_masked_pos.shape	,	(
				        self.image_processor_tester.batch_size,
				        expected_height,
				        expected_width,
				    )	,	)

				# Test batched
				_lowerCAmelCase								=image_processing(__UpperCAmelCase	,	return_tensors="""pt"""    ).pixel_values

				_lowerCAmelCase		,				_lowerCAmelCase								=self.image_processor_tester.get_expected_image_size()
				self.assertEqual(
				    encoded_images.shape	,	(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,	)

				# Test masking
				_lowerCAmelCase								=image_processing(__UpperCAmelCase	,	return_image_mask=__UpperCAmelCase	,	return_tensors="""pt"""    )

				_lowerCAmelCase		,				_lowerCAmelCase								=self.image_processor_tester.get_expected_image_size()
				self.assertEqual(
				    encoded_images.pixel_values.shape	,	(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,	)

				_lowerCAmelCase		,				_lowerCAmelCase								=self.image_processor_tester.get_expected_mask_size()
				self.assertEqual(
				    encoded_images.bool_masked_pos.shape	,	(
				        self.image_processor_tester.batch_size,
				        expected_height,
				        expected_width,
				    )	,	)



			def    _lowerCAmelCase						(				self    )					->	Optional[Any]:
				self._test_call_framework(np.ndarray	,	prepare_kwargs={"""numpify""": True}    )



			def    _lowerCAmelCase						(				self    )					->	str:
				self._test_call_framework(torch.Tensor	,	prepare_kwargs={"""torchify""": True}    )



			def    _lowerCAmelCase						(				self    )					->	Union[str, Any]:
				# Initialize image_processing
				random.seed(12_34    )
				_lowerCAmelCase								=self.image_processing_class(**self.image_processor_dict    )
				_lowerCAmelCase								=prepare_image_inputs(self.image_processor_tester	,	equal_resolution=__UpperCAmelCase	,	torchify=__UpperCAmelCase    )

				# Test not batched input
				_lowerCAmelCase								=image_processing(image_inputs[0]	,	return_image_mask=__UpperCAmelCase	,	return_tensors="""pt"""    )
				self.assertEqual(encoded_images.bool_masked_pos.sum().item()	,	75    )



			def    _lowerCAmelCase						(				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=__UpperCAmelCase    )
				for image in image_inputs:
					self.assertIsInstance(__UpperCAmelCase	,	PIL.Image.Image    )

				# Test not batched input
				_lowerCAmelCase								=image_processing(image_inputs[0]	,	return_codebook_pixels=__UpperCAmelCase	,	return_tensors="""pt"""    )
				_lowerCAmelCase		,				_lowerCAmelCase								=self.image_processor_tester.get_expected_codebook_image_size()
				self.assertEqual(
				    encoded_images.codebook_pixel_values.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

				# Test batched
				_lowerCAmelCase								=image_processing(__UpperCAmelCase	,	return_codebook_pixels=__UpperCAmelCase	,	return_tensors="""pt"""    )
				_lowerCAmelCase		,				_lowerCAmelCase								=self.image_processor_tester.get_expected_codebook_image_size()
				self.assertEqual(
				    encoded_images.codebook_pixel_values.shape	,	(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,	)





"""simple docstring"""


import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 						lowerCamelCase__   (   unittest.TestCase							):





			'''simple docstring'''



			@property
			def    _lowerCAmelCase						(				self    )					->	List[str]:
				_lowerCAmelCase								=1
				_lowerCAmelCase								=3
				_lowerCAmelCase								=8
				_lowerCAmelCase								=8

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

				return sample



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



			def    _lowerCAmelCase						(				self    )					->	Tuple:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )

				_lowerCAmelCase								=self.dummy_sample
				_lowerCAmelCase								=self.to_np(__UpperCAmelCase    )

				for output_type in ["pt", "np", "pil"]:
					_lowerCAmelCase								=image_processor.postprocess(
					    image_processor.preprocess(__UpperCAmelCase    )	,	output_type=__UpperCAmelCase	,	)
					_lowerCAmelCase								=self.to_np(__UpperCAmelCase    )
					_lowerCAmelCase								=(input_np * 2_55).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    _lowerCAmelCase						(				self    )					->	Union[str, Any]:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )
				_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(__UpperCAmelCase    )	,	output_type=__UpperCAmelCase    )

					_lowerCAmelCase								=self.to_np(__UpperCAmelCase    )
					_lowerCAmelCase								=(input_np * 2_55).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    _lowerCAmelCase						(				self    )					->	List[str]:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )

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

				for output_type in ["pt", "np", "pil"]:
					_lowerCAmelCase								=image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )	,	output_type=__UpperCAmelCase    )
					for i, o in zip(__UpperCAmelCase	,	__UpperCAmelCase    ):
						_lowerCAmelCase								=np.array(__UpperCAmelCase    )
						_lowerCAmelCase								=self.to_np(__UpperCAmelCase    ) if output_type == """pil""" else (self.to_np(__UpperCAmelCase    ) * 2_55).round()
						assert (
						    np.abs(in_np - out_np    ).max() < 1e-6
						), f'''decoded output does not match input for output_type {output_type}'''



			def    _lowerCAmelCase						(				self    )					->	Tuple:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )

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

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)
				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

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

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)
				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	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    _lowerCAmelCase						(				self    )					->	Optional[Any]:
				_lowerCAmelCase								=VaeImageProcessor(do_resize=__UpperCAmelCase	,	do_normalize=__UpperCAmelCase    )

				_lowerCAmelCase								=self.dummy_sample
				_lowerCAmelCase								=list(__UpperCAmelCase    )

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				_lowerCAmelCase								=self.to_np(self.dummy_sample    )
				list(__UpperCAmelCase    )

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	output_type="""np"""	,	)

				_lowerCAmelCase								=image_processor.postprocess(
				    image_processor.preprocess(__UpperCAmelCase    )	,	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



