





import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def __init__(      self      :      int		,   _A      :      int		,   _A      :      Tuple=7		,   _A      :      Dict=3		,   _A      :      Union[str, Any]=18		,   _A      :      str=30		,   _A      :      Union[str, Any]=400		,   _A      :      Dict=True		,   _A      :      Any=None		,   _A      :      Optional[int]=True		,   _A      :      List[str]=None		,   _A      :      Optional[int]=True		,   _A      :      str=[0.5, 0.5, 0.5]		,   _A      :      int=[0.5, 0.5, 0.5]		,   _A      :      int=False		,   )							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	size if size is not None else {'height': 20, 'width': 20}
							snake_case_		:			Tuple											=	crop_size if crop_size is not None else {'height': 18, 'width': 18}
							snake_case_		:			Dict											=	parent
							snake_case_		:			int											=	batch_size
							snake_case_		:			Tuple											=	num_channels
							snake_case_		:			Union[str, Any]											=	image_size
							snake_case_		:			List[str]											=	min_resolution
							snake_case_		:			int											=	max_resolution
							snake_case_		:			Optional[Any]											=	do_resize
							snake_case_		:			str											=	size
							snake_case_		:			Optional[Any]											=	do_center_crop
							snake_case_		:			Any											=	crop_size
							snake_case_		:			List[str]											=	do_normalize
							snake_case_		:			Dict											=	image_mean
							snake_case_		:			int											=	image_std
							snake_case_		:			Union[str, Any]											=	do_reduce_labels






						def UpperCAmelCase_				(      self      :      Any	)							->    Dict:







							"""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_reduce_labels": self.do_reduce_labels,
							}


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Tuple											=	load_dataset('hf-internal-testing/fixtures_ade20k'   , split='test'   )

	snake_case_		:			Dict											=	Image.open(dataset[0]['file']   )
	snake_case_		:			Union[str, Any]											=	Image.open(dataset[1]['file']   )

	return image, map


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Tuple											=	load_dataset('hf-internal-testing/fixtures_ade20k'   , split='test'   )

	snake_case_		:			str											=	Image.open(ds[0]['file']   )
	snake_case_		:			Optional[Any]											=	Image.open(ds[1]['file']   )
	snake_case_		:			Tuple											=	Image.open(ds[2]['file']   )
	snake_case_		:			Union[str, Any]											=	Image.open(ds[3]['file']   )

	return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Dict       =       BeitImageProcessor if is_vision_available() else None






						def UpperCAmelCase_				(      self      :      Tuple	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[str]											=	BeitImageProcessingTester(self	)






						@property
						def UpperCAmelCase_				(      self      :      Tuple	)							->    Tuple:







							"""simple docstring"""


							return self.image_processor_tester.prepare_image_processor_dict()






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			int											=	self.image_processing_class(**self.image_processor_dict	)
							self.assertTrue(hasattr(_A		,   'do_resize'	)	)
							self.assertTrue(hasattr(_A		,   'size'	)	)
							self.assertTrue(hasattr(_A		,   'do_center_crop'	)	)
							self.assertTrue(hasattr(_A		,   'center_crop'	)	)
							self.assertTrue(hasattr(_A		,   'do_normalize'	)	)
							self.assertTrue(hasattr(_A		,   'image_mean'	)	)
							self.assertTrue(hasattr(_A		,   'image_std'	)	)






						def UpperCAmelCase_				(      self      :      Any	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	self.image_processing_class.from_dict(self.image_processor_dict	)
							self.assertEqual(image_processor.size		,   {'height': 20, 'width': 20}	)
							self.assertEqual(image_processor.crop_size		,   {'height': 18, 'width': 18}	)
							self.assertEqual(image_processor.do_reduce_labels		,   _A	)

							snake_case_		:			List[Any]											=	self.image_processing_class.from_dict(
							    self.image_processor_dict		,   size=42		,   crop_size=84		,   reduce_labels=_A	)
							self.assertEqual(image_processor.size		,   {'height': 42, 'width': 42}	)
							self.assertEqual(image_processor.crop_size		,   {'height': 84, 'width': 84}	)
							self.assertEqual(image_processor.do_reduce_labels		,   _A	)






						def UpperCAmelCase_				(      self      :      Tuple	)							->    Optional[int]:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      int	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			str											=	self.image_processing_class(**self.image_processor_dict	)
							# create random PIL images
							snake_case_		:			Any											=	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_		:			int											=	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
							snake_case_		:			Any											=	image_processing(_A		,   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 UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    int:







							"""simple docstring"""


							snake_case_		:			str											=	self.image_processing_class(**self.image_processor_dict	)
							# create random numpy tensors
							snake_case_		:			List[Any]											=	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_		:			Any											=	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
							snake_case_		:			int											=	image_processing(_A		,   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 UpperCAmelCase_				(      self      :      List[str]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Any											=	self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							snake_case_		:			Optional[Any]											=	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_		:			int											=	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
							snake_case_		:			int											=	image_processing(_A		,   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 UpperCAmelCase_				(      self      :      Any	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	self.image_processing_class(**self.image_processor_dict	)
							# create random PyTorch tensors
							snake_case_		:			int											=	prepare_image_inputs(self.image_processor_tester		,   equal_resolution=_A		,   torchify=_A	)
							snake_case_		:			Any											=	[]
							for image in image_inputs:
								self.assertIsInstance(_A		,   torch.Tensor	)
								maps.append(torch.zeros(image.shape[-2:]	).long()	)

							# Test not batched input
							snake_case_		:			Union[str, Any]											=	image_processing(image_inputs[0]		,   maps[0]		,   return_tensors='pt'	)
							self.assertEqual(
							    encoding['pixel_values'].shape		,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )
							self.assertEqual(
							    encoding['labels'].shape		,   (
							        1,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )
							self.assertEqual(encoding['labels'].dtype		,   torch.long	)
							self.assertTrue(encoding['labels'].min().item() >= 0	)
							self.assertTrue(encoding['labels'].max().item() <= 255	)

							# Test batched
							snake_case_		:			Tuple											=	image_processing(_A		,   _A		,   return_tensors='pt'	)
							self.assertEqual(
							    encoding['pixel_values'].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'],
							    )		,   )
							self.assertEqual(
							    encoding['labels'].shape		,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )
							self.assertEqual(encoding['labels'].dtype		,   torch.long	)
							self.assertTrue(encoding['labels'].min().item() >= 0	)
							self.assertTrue(encoding['labels'].max().item() <= 255	)

							# Test not batched input (PIL images)
							snake_case_						,snake_case_		:			Optional[Any]											=	prepare_semantic_single_inputs()

							snake_case_		:			Optional[int]											=	image_processing(_A		,   _A		,   return_tensors='pt'	)
							self.assertEqual(
							    encoding['pixel_values'].shape		,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )
							self.assertEqual(
							    encoding['labels'].shape		,   (
							        1,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )
							self.assertEqual(encoding['labels'].dtype		,   torch.long	)
							self.assertTrue(encoding['labels'].min().item() >= 0	)
							self.assertTrue(encoding['labels'].max().item() <= 255	)

							# Test batched input (PIL images)
							snake_case_						,snake_case_		:			Dict											=	prepare_semantic_batch_inputs()

							snake_case_		:			Optional[Any]											=	image_processing(_A		,   _A		,   return_tensors='pt'	)
							self.assertEqual(
							    encoding['pixel_values'].shape		,   (
							        2,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )
							self.assertEqual(
							    encoding['labels'].shape		,   (
							        2,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )		,   )
							self.assertEqual(encoding['labels'].dtype		,   torch.long	)
							self.assertTrue(encoding['labels'].min().item() >= 0	)
							self.assertTrue(encoding['labels'].max().item() <= 255	)






						def UpperCAmelCase_				(      self      :      Any	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			str											=	self.image_processing_class(**self.image_processor_dict	)

							# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
							snake_case_						,snake_case_		:			int											=	prepare_semantic_single_inputs()
							snake_case_		:			Any											=	image_processing(_A		,   _A		,   return_tensors='pt'	)
							self.assertTrue(encoding['labels'].min().item() >= 0	)
							self.assertTrue(encoding['labels'].max().item() <= 150	)

							snake_case_		:			int											=	True
							snake_case_		:			List[Any]											=	image_processing(_A		,   _A		,   return_tensors='pt'	)
							self.assertTrue(encoding['labels'].min().item() >= 0	)
							self.assertTrue(encoding['labels'].max().item() <= 255	)








import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						@property
						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							torch.manual_seed(0	)
							snake_case_		:			List[Any]											=	UNetaDModel(
							    block_out_channels=(32, 64)		,   layers_per_block=2		,   sample_size=32		,   in_channels=3		,   out_channels=3		,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,   )
							return model






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.dummy_uncond_unet
							snake_case_		:			Optional[Any]											=	ScoreSdeVeScheduler()

							snake_case_		:			Tuple											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Optional[Any]											=	torch.manual_seed(0	)
							snake_case_		:			Tuple											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			int											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=2		,   output_type='numpy'		,   generator=_A		,   return_dict=_A	)[
							    0
							]

							snake_case_		:			List[str]											=	image[0, -3:, -3:, -1]
							snake_case_		:			Dict											=	image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_		:			List[Any]											=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
@slow
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Dict	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Dict											=	'google/ncsnpp-church-256'
							snake_case_		:			List[Any]											=	UNetaDModel.from_pretrained(_A	)

							snake_case_		:			str											=	ScoreSdeVeScheduler.from_pretrained(_A	)

							snake_case_		:			Optional[Any]											=	ScoreSdeVePipeline(unet=_A		,   scheduler=_A	)
							sde_ve.to(_A	)
							sde_ve.set_progress_bar_config(disable=_A	)

							snake_case_		:			Any											=	torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	sde_ve(num_inference_steps=10		,   output_type='numpy'		,   generator=_A	).images

							snake_case_		:			Union[str, Any]											=	image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							snake_case_		:			Dict											=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]	)

							assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2

