



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 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


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



											'''simple docstring'''


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







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''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        __a			(   )     ->     int:
					SCREAMING_SNAKE_CASE							:     str						=				load_dataset('hf-internal-testing/fixtures_ade20k'      ,	split='test'   )

					SCREAMING_SNAKE_CASE							:     int						=				Image.open(dataset[0]['file']   )
					SCREAMING_SNAKE_CASE							:     Dict						=				Image.open(dataset[1]['file']   )

					return image, map

def        __a			(   )     ->     Optional[int]:
					SCREAMING_SNAKE_CASE							:     Dict						=				load_dataset('hf-internal-testing/fixtures_ade20k'      ,	split='test'   )

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

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



@require_torch
@require_vision
class 						lowercase    (							SCREAMING_SNAKE_CASE_      ,    unittest.TestCase):
						'''simple docstring'''


						UpperCAmelCase			:		Union[str, Any]		=		BeitImageProcessor if is_vision_available() else None







						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				BeitImageProcessingTester(self	)







						@property
						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											return self.image_processor_tester.prepare_image_processor_dict()







						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				self.image_processing_class(**self.image_processor_dict	)
											self.assertTrue(hasattr(snake_case       ,	'do_resize'	)	)
											self.assertTrue(hasattr(snake_case       ,	'size'	)	)
											self.assertTrue(hasattr(snake_case       ,	'do_center_crop'	)	)
											self.assertTrue(hasattr(snake_case       ,	'center_crop'	)	)
											self.assertTrue(hasattr(snake_case       ,	'do_normalize'	)	)
											self.assertTrue(hasattr(snake_case       ,	'image_mean'	)	)
											self.assertTrue(hasattr(snake_case       ,	'image_std'	)	)







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				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       ,	snake_case	)

											SCREAMING_SNAKE_CASE							:     Any						=				self.image_processing_class.from_dict(
											    self.image_processor_dict       ,	size=42       ,	crop_size=84       ,	reduce_labels=snake_case	)
											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       ,	snake_case	)







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											pass







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.image_processing_class(**self.image_processor_dict	)
											# create random PIL images
											SCREAMING_SNAKE_CASE							:     Tuple						=				prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=snake_case	)
											for image in image_inputs:
																self.assertIsInstance(snake_case       ,	Image.Image	)

											# Test not batched input
											SCREAMING_SNAKE_CASE							:     Tuple						=				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
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				image_processing(snake_case       ,	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       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.image_processing_class(**self.image_processor_dict	)
											# create random numpy tensors
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=snake_case       ,	numpify=snake_case	)
											for image in image_inputs:
																self.assertIsInstance(snake_case       ,	np.ndarray	)

											# Test not batched input
											SCREAMING_SNAKE_CASE							:     Dict						=				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
											SCREAMING_SNAKE_CASE							:     Any						=				image_processing(snake_case       ,	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       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				self.image_processing_class(**self.image_processor_dict	)
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE							:     Any						=				prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=snake_case       ,	torchify=snake_case	)
											for image in image_inputs:
																self.assertIsInstance(snake_case       ,	torch.Tensor	)

											# Test not batched input
											SCREAMING_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
											SCREAMING_SNAKE_CASE							:     int						=				image_processing(snake_case       ,	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       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


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

											# Test not batched input
											SCREAMING_SNAKE_CASE							:     Optional[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
											SCREAMING_SNAKE_CASE							:     List[Any]						=				image_processing(snake_case       ,	snake_case       ,	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)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				prepare_semantic_single_inputs()

											SCREAMING_SNAKE_CASE							:     List[Any]						=				image_processing(snake_case       ,	snake_case       ,	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)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     int						=				prepare_semantic_batch_inputs()

											SCREAMING_SNAKE_CASE							:     List[Any]						=				image_processing(snake_case       ,	snake_case       ,	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       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				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
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     List[Any]						=				prepare_semantic_single_inputs()
											SCREAMING_SNAKE_CASE							:     List[str]						=				image_processing(snake_case       ,	snake_case       ,	return_tensors='pt'	)
											self.assertTrue(encoding['labels'].min().item() >= 0	)
											self.assertTrue(encoding['labels'].max().item() <= 150	)

											SCREAMING_SNAKE_CASE							:     Tuple						=				True
											SCREAMING_SNAKE_CASE							:     List[str]						=				image_processing(snake_case       ,	snake_case       ,	return_tensors='pt'	)
											self.assertTrue(encoding['labels'].min().item() >= 0	)
											self.assertTrue(encoding['labels'].max().item() <= 255	)




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCamelCase    :		Any		      =    False


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						pass


@nightly
@require_torch_gpu
class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Dict						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											with tempfile.TemporaryDirectory() as tmpdirname:
																pipe.save_pretrained(snake_case	)
																SCREAMING_SNAKE_CASE							:     Any						=				VersatileDiffusionPipeline.from_pretrained(snake_case       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				generator.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				pipe.dual_guided(
											    prompt='first prompt'       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='numpy'       ,	).images

											assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'       ,	torch_dtype=torch.floataa	)
											pipe.to(snake_case	)
											pipe.set_progress_bar_config(disable=snake_case	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'cyberpunk 2077'
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Any						=				pipe.dual_guided(
											    prompt=snake_case       ,	image=snake_case       ,	text_to_image_strength=0.75       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'       ,	).images

											SCREAMING_SNAKE_CASE							:     List[Any]						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Tuple						=				np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]	)

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

											SCREAMING_SNAKE_CASE							:     Any						=				'A painting of a squirrel eating a burger '
											SCREAMING_SNAKE_CASE							:     Any						=				torch.manual_seed(0	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				pipe.text_to_image(
											    prompt=snake_case       ,	generator=snake_case       ,	guidance_scale=7.5       ,	num_inference_steps=50       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     Dict						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

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

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				pipe.image_variation(snake_case       ,	generator=snake_case       ,	output_type='numpy'	).images

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]	)

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