






"""simple docstring"""


import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


_UpperCamelCase    :   Union[str, Any]   			=						False






class 						snake_case  (  unittest.TestCase	):
    pass






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





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      a					:      List[Any]    =       VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'      )
      # remove text_unet
      pipe.remove_unused_weights()
      pipe.to(A      )
      pipe.set_progress_bar_config(disable=A      )

      a					:      Dict    =       'A painting of a squirrel eating a burger '
      a					:      List[str]    =       torch.manual_seed(0      )
      a					:      Optional[Any]    =       pipe(
          prompt=A    ,		generator=A    ,		guidance_scale=7.5    ,		num_inference_steps=2    ,		output_type='numpy'      ).images

      with tempfile.TemporaryDirectory() as tmpdirname:
        pipe.save_pretrained(A      )
        a					:      List[str]    =       VersatileDiffusionTextToImagePipeline.from_pretrained(A      )
      pipe.to(A      )
      pipe.set_progress_bar_config(disable=A      )

      a					:      Dict    =       generator.manual_seed(0      )
      a					:      Tuple    =       pipe(
          prompt=A    ,		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     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       VersatileDiffusionTextToImagePipeline.from_pretrained(
          'shi-labs/versatile-diffusion'    ,		torch_dtype=torch.floataa      )
      pipe.to(A      )
      pipe.set_progress_bar_config(disable=A      )

      a					:      int    =       'A painting of a squirrel eating a burger '
      a					:      Any    =       torch.manual_seed(0      )
      a					:      Optional[Any]    =       pipe(
          prompt=A    ,		generator=A    ,		guidance_scale=7.5    ,		num_inference_steps=5_0    ,		output_type='numpy'      ).images

      a					:      Optional[Any]    =       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      a					:      List[str]    =       np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]      )

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












"""simple docstring"""


def       snake_case  (A_				:list[int]     ,      A_				:str				):







  '''simple docstring'''

  a					:      Optional[int]    =       int(A_				)

  # Initialize Result
  a					:      int    =       []

  # 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__":
   _UpperCamelCase    :   Dict   			=						[]
   _UpperCamelCase    :   str   			=						'0'

   if (
       input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
       == "y"
   ):
      _UpperCamelCase    :   Dict   			=						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()))
      _UpperCamelCase    :   Optional[int]   			=						input('Enter the change you want to make in Indian Currency: ').strip()
   else:
      # All denominations of Indian Currency if user does not enter
      _UpperCamelCase    :   Union[str, Any]   			=						[1, 2, 5, 10, 20, 50, 100, 500, 2000]
      _UpperCamelCase    :   Dict   			=						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}: ''')
      _UpperCamelCase    :   Tuple   			=						find_minimum_change(denominations, value)
      # Print result
      for i in range(len(answer)):
         print(answer[i], end=' ')




