



"""simple docstring"""






import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def __init__(	self							:  int,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Union[str, Any]=7,  lowerCamelCase							:  Union[str, Any]=3,  lowerCamelCase							:  Union[str, Any]=18,  lowerCamelCase							:  List[str]=30,  lowerCamelCase							:  int=400,  lowerCamelCase							:  Optional[Any]=True,  lowerCamelCase							:  Dict=None,  lowerCamelCase							:  Optional[int]=True,  lowerCamelCase							:  Tuple=[0.5, 0.5, 0.5],  lowerCamelCase							:  Dict=[0.5, 0.5, 0.5],  )-> Union[str, Any]:
								lowerCamelCase__		:		Optional[int]		     =size if size is not None else {'''height''': 18, '''width''': 18}
								lowerCamelCase__		:		Union[str, Any]		     =parent
								lowerCamelCase__		:		Tuple		     =batch_size
								lowerCamelCase__		:		Union[str, Any]		     =num_channels
								lowerCamelCase__		:		List[str]		     =image_size
								lowerCamelCase__		:		List[str]		     =min_resolution
								lowerCamelCase__		:		Tuple		     =max_resolution
								lowerCamelCase__		:		Tuple		     =do_resize
								lowerCamelCase__		:		List[Any]		     =size
								lowerCamelCase__		:		str		     =do_normalize
								lowerCamelCase__		:		Union[str, Any]		     =image_mean
								lowerCamelCase__		:		Union[str, Any]		     =image_std




					def 		snake_case	(	self							:  Any					)-> Union[str, 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,
								}





@require_torch
@require_vision
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							DPTImageProcessor if is_vision_available() else None



					def 		snake_case	(	self							:  str					)-> Optional[Any]:
								lowerCamelCase__		:		int		     =DPTImageProcessingTester(self					)



					@property
					def 		snake_case	(	self							:  Any					)-> Optional[Any]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 		snake_case	(	self							:  Tuple					)-> Optional[Any]:
								lowerCamelCase__		:		str		     =self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(lowerCamelCase,  '''image_mean'''					)					)
								self.assertTrue(hasattr(lowerCamelCase,  '''image_std'''					)					)
								self.assertTrue(hasattr(lowerCamelCase,  '''do_normalize'''					)					)
								self.assertTrue(hasattr(lowerCamelCase,  '''do_resize'''					)					)
								self.assertTrue(hasattr(lowerCamelCase,  '''size'''					)					)



					def 		snake_case	(	self							:  str					)-> Union[str, Any]:
								lowerCamelCase__		:		List[Any]		     =self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size,  {'''height''': 18, '''width''': 18}					)

								lowerCamelCase__		:		List[Any]		     =self.image_processing_class.from_dict(self.image_processor_dict,  size=42					)
								self.assertEqual(image_processor.size,  {'''height''': 42, '''width''': 42}					)



					def 		snake_case	(	self							:  Dict					)-> Dict:
								# Initialize image_processing
								lowerCamelCase__		:		str		     =self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								lowerCamelCase__		:		Union[str, Any]		     =prepare_image_inputs(self.image_processor_tester,  equal_resolution=lowerCamelCase					)
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,  Image.Image					)

								# Test not batched input
								lowerCamelCase__		:		str		     =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['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ),  )

								# Test batched
								lowerCamelCase__		:		List[str]		     =image_processing(lowerCamelCase,  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['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ),  )



					def 		snake_case	(	self							:  int					)-> str:
								# Initialize image_processing
								lowerCamelCase__		:		Dict		     =self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								lowerCamelCase__		:		int		     =prepare_image_inputs(self.image_processor_tester,  equal_resolution=lowerCamelCase,  numpify=lowerCamelCase					)
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,  np.ndarray					)

								# Test not batched input
								lowerCamelCase__		:		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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ),  )

								# Test batched
								lowerCamelCase__		:		Any		     =image_processing(lowerCamelCase,  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['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ),  )




					def 		snake_case	(	self							:  Dict					)-> Optional[int]:
								# Initialize image_processing
								lowerCamelCase__		:		Dict		     =self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								lowerCamelCase__		:		Optional[int]		     =prepare_image_inputs(self.image_processor_tester,  equal_resolution=lowerCamelCase,  torchify=lowerCamelCase					)
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,  torch.Tensor					)

								# Test not batched input
								lowerCamelCase__		:		Optional[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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ),  )

								# Test batched
								lowerCamelCase__		:		int		     =image_processing(lowerCamelCase,  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['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ),  )









"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_lowercase			:			Tuple										=						False

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					pass





@slow
@require_torch_gpu
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					def 		snake_case	(	self							:  List[Any]					)-> Dict:
								lowerCamelCase__		:		str		     =VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''					)
								pipe.to(lowerCamelCase					)
								pipe.set_progress_bar_config(disable=lowerCamelCase					)

								lowerCamelCase__		:		int		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''					)
								lowerCamelCase__		:		Dict		     =torch.manual_seed(0					)
								lowerCamelCase__		:		str		     =pipe(
								    image=lowerCamelCase,  generator=lowerCamelCase,  guidance_scale=7.5,  num_inference_steps=50,  output_type='''numpy''',  ).images

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

								assert image.shape == (1, 512, 512, 3)
								lowerCamelCase__		:		List[Any]		     =np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]					)

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




