



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 ConvNextImageProcessor

class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     def __init__(							self			:				Dict  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				List[str]=7  ,	UpperCamelCase			:				Union[str, Any]=3  ,	UpperCamelCase			:				Tuple=1_8  ,	UpperCamelCase			:				Tuple=3_0  ,	UpperCamelCase			:				List[Any]=4_0_0  ,	UpperCamelCase			:				str=True  ,	UpperCamelCase			:				List[Any]=None  ,	UpperCamelCase			:				List[str]=0.8_7_5  ,	UpperCamelCase			:				List[str]=True  ,	UpperCamelCase			:				Dict=[0.5, 0.5, 0.5]  ,	UpperCamelCase			:				int=[0.5, 0.5, 0.5]  ,	)->str:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						size if size is not None else {"shortest_edge": 2_0}
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						parent
      __SCREAMING_SNAKE_CASE							:     Tuple							=						batch_size
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						num_channels
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_size
      __SCREAMING_SNAKE_CASE							:     str							=						min_resolution
      __SCREAMING_SNAKE_CASE							:     Dict							=						max_resolution
      __SCREAMING_SNAKE_CASE							:     Any							=						do_resize
      __SCREAMING_SNAKE_CASE							:     Tuple							=						size
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						crop_pct
      __SCREAMING_SNAKE_CASE							:     str							=						do_normalize
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_mean
      __SCREAMING_SNAKE_CASE							:     Any							=						image_std






     def 						__snake_case     (							self			:				List[str]					)->List[str]:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "crop_pct": self.crop_pct,
      }






@require_torch
@require_vision
class    _SCREAMING_SNAKE_CASE						(UpperCamelCase      ,     unittest.TestCase			):
     lowerCAmelCase       =      ConvNextImageProcessor if is_vision_available() else None





     def 						__snake_case     (							self			:				List[str]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Any							=						ConvNextImageProcessingTester(self					)





     @property
     def 						__snake_case     (							self			:				int					)->int:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"do_resize"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"size"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"crop_pct"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"do_normalize"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"image_mean"					)					)
      self.assertTrue(hasattr(UpperCamelCase  ,	"image_std"					)					)





     def 						__snake_case     (							self			:				Tuple					)->Dict:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size  ,	{"shortest_edge": 2_0}					)

      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class.from_dict(self.image_processor_dict  ,	size=4_2					)
      self.assertEqual(image_processor.size  ,	{"shortest_edge": 4_2}					)





     def 						__snake_case     (							self			:				int					)->List[Any]:
      pass





     def 						__snake_case     (							self			:				List[Any]					)->List[str]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase					)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase  ,	Image.Image					)

      # 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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     Dict							=						image_processing(UpperCamelCase  ,	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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)





     def 						__snake_case     (							self			:				Dict					)->int:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      __SCREAMING_SNAKE_CASE							:     int							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase  ,	numpify=UpperCamelCase					)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase  ,	np.ndarray					)

      # Test not batched input
      __SCREAMING_SNAKE_CASE							:     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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						image_processing(UpperCamelCase  ,	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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)






     def 						__snake_case     (							self			:				str					)->List[Any]:
      # Initialize image_processing
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase  ,	torchify=UpperCamelCase					)
      for image in image_inputs:
       self.assertIsInstance(UpperCamelCase  ,	torch.Tensor					)

      # Test not batched input
      __SCREAMING_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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)

      # Test batched
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processing(UpperCamelCase  ,	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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,	)










"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

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


__snake_case												=							False


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

     '''simple docstring'''


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


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

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

          _a     						=					torch.manual_seed(0				)
          _a     						=					pipe(
              prompt='''first prompt'''     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					guidance_scale=7.5     ,					num_inference_steps=2     ,					output_type='''numpy'''     ,					).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCAmelCase				)
               _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCAmelCase				)

          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					generator.manual_seed(0				)
          _a     						=					pipe(
              prompt='''first prompt'''     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					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 				_UpperCAmelCase   (   self				)							->				Dict:
          _a     						=					VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
          pipe.remove_unused_weights()
          pipe.to(__UpperCAmelCase				)
          pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

          _a     						=					'''cyberpunk 2077'''
          _a     						=					load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
          _a     						=					torch.manual_seed(0				)
          _a     						=					pipe(
              prompt=__UpperCAmelCase     ,					image=__UpperCAmelCase     ,					text_to_image_strength=0.75     ,					generator=__UpperCAmelCase     ,					guidance_scale=7.5     ,					num_inference_steps=50     ,					output_type='''numpy'''     ,					).images

          _a     						=					image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a     						=					np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]				)

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