'''simple docstring'''

import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image







def 					UpperCamelCase_						( snake_case_	:	List[Any]						,		snake_case_	:	List[Any]=2_56				) ->					int:






 '''simple docstring'''

 __lowerCAmelCase    							=						min(img.size				)

 if s < target_image_size:
  raise ValueError(f"""min dim for image {s} < {target_image_size}"""				)

 __lowerCAmelCase    							=						target_image_size / s
 __lowerCAmelCase    							=						(round(r * img.size[1]				), round(r * img.size[0]				))
 __lowerCAmelCase    							=						TF.resize(snake_case_						,		snake_case_						,		interpolation=PIL.Image.LANCZOS				)
 __lowerCAmelCase    							=						TF.center_crop(snake_case_						,		output_size=2 * [target_image_size]				)
 __lowerCAmelCase    							=						torch.unsqueeze(T.ToTensor()(snake_case_				)						,		0				)
 return img







def 					UpperCamelCase_						( snake_case_	:	str				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						2.0 * x - 1.0
 return x







def 					UpperCamelCase_						( snake_case_	:	Union[str, Any]						,		snake_case_	:	Tuple=True						,		snake_case_	:	Any="RGB"				) ->					Any:






 '''simple docstring'''

 __lowerCAmelCase    							=						x.detach().cpu()
 if process:
  __lowerCAmelCase    							=						post_process_tensor(snake_case_				)
 __lowerCAmelCase    							=						x.numpy()
 if process:
  __lowerCAmelCase    							=						(2_55 * x).astype(np.uinta				)
 __lowerCAmelCase    							=						Image.fromarray(snake_case_				)
 if not x.mode == mode:
  __lowerCAmelCase    							=						x.convert(snake_case_				)
 return x







def 					UpperCamelCase_						( snake_case_	:	Dict				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						torch.clamp(snake_case_						,		-1.0						,		1.0				)
 __lowerCAmelCase    							=						(x + 1.0) / 2.0
 __lowerCAmelCase    							=						x.permute(1						,		2						,		0				)
 return x







def 					UpperCamelCase_						( snake_case_	:	int				) ->					Any:






 '''simple docstring'''

 __lowerCAmelCase    							=						post_process_tensor(x.squeeze()				)
 return x.permute(2						,		0						,		1				).unsqueeze(0				)





'''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


_A      :				Any 				=    False







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


 '''simple docstring'''






 def a (				self       :     Tuple							)  ->   Dict:
  # clean up the VRAM after each test
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()
 def a (				self       :     Optional[int]							)  ->   Optional[Any]:
  __lowerCAmelCase    							=						VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							)
  # remove text_unet
  pipe.remove_unused_weights()
  pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

  __lowerCAmelCase    							=						torch.manual_seed(0							)
  __lowerCAmelCase    							=						pipe(
      prompt="""first prompt"""				,	image=SCREAMING_SNAKE_CASE__				,	text_to_image_strength=0.7_5				,	generator=SCREAMING_SNAKE_CASE__				,	guidance_scale=7.5				,	num_inference_steps=2				,	output_type="""numpy"""				,	).images

  with tempfile.TemporaryDirectory() as tmpdirname:
   pipe.save_pretrained(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						VersatileDiffusionDualGuidedPipeline.from_pretrained(SCREAMING_SNAKE_CASE__							)

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

  __lowerCAmelCase    							=						generator.manual_seed(0							)
  __lowerCAmelCase    							=						pipe(
      prompt="""first prompt"""				,	image=SCREAMING_SNAKE_CASE__				,	text_to_image_strength=0.7_5				,	generator=SCREAMING_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 a (				self       :     str							)  ->   Tuple:
  __lowerCAmelCase    							=						VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							)
  pipe.remove_unused_weights()
  pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						"""cyberpunk 2077"""
  __lowerCAmelCase    							=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
  __lowerCAmelCase    							=						torch.manual_seed(0							)
  __lowerCAmelCase    							=						pipe(
      prompt=SCREAMING_SNAKE_CASE__				,	image=SCREAMING_SNAKE_CASE__				,	text_to_image_strength=0.7_5				,	generator=SCREAMING_SNAKE_CASE__				,	guidance_scale=7.5				,	num_inference_steps=50				,	output_type="""numpy"""				,	).images

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

  assert image.shape == (1, 5_12, 5_12, 3)
  __lowerCAmelCase    							=						np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]							)

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






