"""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 MobileNetVaImageProcessor

class 		UpperCamelCase      (   unittest.TestCase						):



				def __init__(self    :				Optional[int]     ,  _A    :				Dict     ,  _A    :				Dict=7     ,  _A    :				Optional[int]=3     ,  _A    :				List[Any]=18     ,  _A    :				Optional[int]=30     ,  _A    :				int=4_00     ,  _A    :				List[str]=True     ,  _A    :				Tuple=None     ,  _A    :				List[str]=True     ,  _A    :				Dict=None     ,  )   ->     Dict:
					__snake_case      :							str						=			size if size is not None else {'shortest_edge': 20}
					__snake_case      :							str						=			crop_size if crop_size is not None else {'height': 18, 'width': 18}
					__snake_case      :							Any						=			parent
					__snake_case      :							List[str]						=			batch_size
					__snake_case      :							Dict						=			num_channels
					__snake_case      :							int						=			image_size
					__snake_case      :							Union[str, Any]						=			min_resolution
					__snake_case      :							List[str]						=			max_resolution
					__snake_case      :							Dict						=			do_resize
					__snake_case      :							Any						=			size
					__snake_case      :							Any						=			do_center_crop
					__snake_case      :							Dict						=			crop_size






				def  _lowercase					(self    :				Optional[Any])   ->     Any:
					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					}







@require_torch
@require_vision
class 		UpperCamelCase      (   lowercase				,     unittest.TestCase						):
				UpperCAmelCase    :				List[str]   =		MobileNetVaImageProcessor if is_vision_available() else None



				def  _lowercase					(self    :				Optional[Any])   ->     Any:
					__snake_case      :							List[Any]						=			MobileNetVaImageProcessingTester(self)



				@property
				def  _lowercase					(self    :				Dict)   ->     Tuple:
					return self.image_processor_tester.prepare_image_processor_dict()



				def  _lowercase					(self    :				List[str])   ->     Tuple:
					__snake_case      :							str						=			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     ,  'crop_size'))



				def  _lowercase					(self    :				List[Any])   ->     Optional[Any]:
					__snake_case      :							List[Any]						=			self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size     ,  {'shortest_edge': 20})
					self.assertEqual(image_processor.crop_size     ,  {'height': 18, 'width': 18})

					__snake_case      :							int						=			self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42     ,  crop_size=84)
					self.assertEqual(image_processor.size     ,  {'shortest_edge': 42})
					self.assertEqual(image_processor.crop_size     ,  {'height': 84, 'width': 84})



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



				def  _lowercase					(self    :				str)   ->     Optional[int]:
					# Initialize image_processing
					__snake_case      :							Optional[int]						=			self.image_processing_class(**self.image_processor_dict)
					# create random PIL images
					__snake_case      :							Optional[int]						=			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      :							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.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  _lowercase					(self    :				Tuple)   ->     Dict:
					# Initialize image_processing
					__snake_case      :							Tuple						=			self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					__snake_case      :							Dict						=			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      :							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      :							Tuple						=			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  _lowercase					(self    :				Union[str, Any])   ->     List[Any]:
					# Initialize image_processing
					__snake_case      :							Tuple						=			self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					__snake_case      :							Optional[int]						=			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      :							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
					__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'],
					    )     ,  )






"""simple docstring"""







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


_a					:					int=			False

class 		UpperCamelCase      (   unittest.TestCase						):
				pass







@nightly
@require_torch_gpu
class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				Optional[Any])   ->     Tuple:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



				def  _lowercase					(self    :				int)   ->     Tuple:
					__snake_case      :							str						=			VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'     ,  torch_dtype=torch.floataa)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

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

					__snake_case      :							List[Any]						=			torch.manual_seed(0)
					__snake_case      :							Optional[int]						=			pipe.dual_guided(
					    prompt='first prompt'     ,  image=_A     ,  text_to_image_strength=0.75     ,  generator=_A     ,  guidance_scale=7.5     ,  num_inference_steps=2     ,  output_type='numpy'     ,  ).images

					with tempfile.TemporaryDirectory() as tmpdirname:
						pipe.save_pretrained(_A)
						__snake_case      :							Optional[Any]						=			VersatileDiffusionPipeline.from_pretrained(_A     ,  torch_dtype=torch.floataa)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case      :							Tuple						=			generator.manual_seed(0)
					__snake_case      :							int						=			pipe.dual_guided(
					    prompt='first prompt'     ,  image=_A     ,  text_to_image_strength=0.75     ,  generator=_A     ,  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  _lowercase					(self    :				Optional[Any])   ->     Optional[int]:
					__snake_case      :							Optional[Any]						=			VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'     ,  torch_dtype=torch.floataa)
					pipe.to(_A)
					pipe.set_progress_bar_config(disable=_A)

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

					__snake_case      :							Optional[Any]						=			image[0, 2_53:2_56, 2_53:2_56, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case      :							Tuple						=			np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001])

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

					__snake_case      :							List[str]						=			'A painting of a squirrel eating a burger '
					__snake_case      :							str						=			torch.manual_seed(0)
					__snake_case      :							str						=			pipe.text_to_image(
					    prompt=_A     ,  generator=_A     ,  guidance_scale=7.5     ,  num_inference_steps=50     ,  output_type='numpy').images

					__snake_case      :							str						=			image[0, 2_53:2_56, 2_53:2_56, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case      :							Any						=			np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778])

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

					__snake_case      :							List[str]						=			pipe.image_variation(_A     ,  generator=_A     ,  output_type='numpy').images

					__snake_case      :							Dict						=			image[0, 2_53:2_56, 2_53:2_56, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case      :							List[Any]						=			np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456])

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





