'''simple docstring'''

from __future__ import annotations







def 					UpperCamelCase_						( snake_case_	:	list[list[int]]						,		snake_case_	:	list[list[int]]				) ->					list[int]:






 '''simple docstring'''

 assert len(snake_case_				) == len(snake_case_				)

 __lowerCAmelCase    							=						len(snake_case_				)
 __lowerCAmelCase    							=						list(range(snake_case_				)				)
 __lowerCAmelCase    							=						[-1] * n  # who the donor has donated to
 __lowerCAmelCase    							=						[-1] * n  # who the recipient has received from
 __lowerCAmelCase    							=						[0] * n

 while unmatched_donors:
  __lowerCAmelCase    							=						unmatched_donors[0]
  __lowerCAmelCase    							=						donor_pref[donor]
  __lowerCAmelCase    							=						donor_preference[num_donations[donor]]
  num_donations[donor] += 1
  __lowerCAmelCase    							=						recipient_pref[recipient]
  __lowerCAmelCase    							=						rec_record[recipient]

  if prev_donor != -1:
   if rec_preference.index(snake_case_				) > rec_preference.index(snake_case_				):
    __lowerCAmelCase    							=						donor
    __lowerCAmelCase    							=						recipient
    unmatched_donors.append(snake_case_				)
    unmatched_donors.remove(snake_case_				)
  else:
   __lowerCAmelCase    							=						donor
   __lowerCAmelCase    							=						recipient
   unmatched_donors.remove(snake_case_				)
 return donor_record





'''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






