



'''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


lowercase_		       =  False






class __A			(  unittest.TestCase						):



 '''simple docstring'''







 pass






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



 '''simple docstring'''







 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _a      =  VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' ,   torch_dtype=torch.floataa       )
      pipe.to(A       )
      pipe.set_progress_bar_config(disable=A       )

      _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.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       )
           _a      =  VersatileDiffusionPipeline.from_pretrained(A ,   torch_dtype=torch.floataa       )
      pipe.to(A       )
      pipe.set_progress_bar_config(disable=A       )

      _a      =  generator.manual_seed(0       )
      _a      =  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 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' ,   torch_dtype=torch.floataa       )
      pipe.to(A       )
      pipe.set_progress_bar_config(disable=A       )

      _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.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

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

      assert image.shape == (1, 512, 512, 3)
      _a      =  np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]       )

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

      _a      =  '''A painting of a squirrel eating a burger '''
      _a      =  torch.manual_seed(0       )
      _a      =  pipe.text_to_image(
          prompt=A ,   generator=A ,   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([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]       )

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

      _a      =  pipe.image_variation(A ,   generator=A ,   output_type='''numpy'''       ).images

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

      assert image.shape == (1, 512, 512, 3)
      _a      =  np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]       )

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








'''simple docstring'''





def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  int(__A)

     # Initialize Result
     _a      =  []

     # Traverse through all denomination
     for denomination in reversed(__A):
          # Find denominations
          while int(__A) >= int(__A):
               total_value -= int(__A)
               answer.append(__A)  # Append the "answers" array

     return answer


# Driver Code
if __name__ == "__main__":
  lowercase_		       =  []
  lowercase_		       =  "0"

  if (
      input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
      == "y"
  ):
    lowercase_		       =  int(input("Enter the number of denominations you want to add: ").strip())

    for i in range(0, n):
      denominations.append(int(input(F"""Denomination {i}: """).strip()))
    lowercase_		       =  input("Enter the change you want to make in Indian Currency: ").strip()
  else:
    # All denominations of Indian Currency if user does not enter
    lowercase_		       =  [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
    lowercase_		       =  input("Enter the change you want to make: ").strip()

  if int(value) == 0 or int(value) < 0:
    print("The total value cannot be zero or negative.")

  else:
    print(F"""Following is minimal change for {value}: """)
    lowercase_		       =  find_minimum_change(denominations, value)
    # Print result
    for i in range(len(answer)):
      print(answer[i], end=" ")



