


import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				UNetaDModel(
           block_out_channels=(32, 64)		,				layers_per_block=2		,				sample_size=32		,				in_channels=3		,				out_channels=3		,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,				)
       return model



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				VQModel(
           block_out_channels=[32, 64]		,				in_channels=3		,				out_channels=3		,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,				latent_channels=3		,				)
       return model



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				CLIPTextConfig(
           bos_token_id=0		,				eos_token_id=2		,				hidden_size=32		,				intermediate_size=37		,				layer_norm_eps=1E-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=10_00		,				)
       return CLIPTextModel(UpperCamelCase__ )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.dummy_uncond_unet
       snake_case_	=				DDIMScheduler()
       snake_case_	=				self.dummy_vq_model

       snake_case_	=				LDMPipeline(unet=UpperCamelCase__		,				vqvae=UpperCamelCase__		,				scheduler=UpperCamelCase__ )
       ldm.to(UpperCamelCase__ )
       ldm.set_progress_bar_config(disable=UpperCamelCase__ )

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				ldm(generator=UpperCamelCase__		,				num_inference_steps=2		,				output_type='''numpy''' ).images

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				ldm(generator=UpperCamelCase__		,				num_inference_steps=2		,				output_type='''numpy'''		,				return_dict=UpperCamelCase__ )[0]

       snake_case_	=				image[0, -3:, -3:, -1]
       snake_case_	=				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       snake_case_	=				np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172] )
       snake_case_	=				1E-2 if torch_device != '''mps''' else 3E-2

       assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance







@slow
@require_torch
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       snake_case_	=				LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256''' )
       ldm.to(UpperCamelCase__ )
       ldm.set_progress_bar_config(disable=UpperCamelCase__ )

       snake_case_	=				torch.manual_seed(0 )
       snake_case_	=				ldm(generator=UpperCamelCase__		,				num_inference_steps=5		,				output_type='''numpy''' ).images

       snake_case_	=				image[0, -3:, -3:, -1]

       assert image.shape == (1, 2_56, 2_56, 3)
       snake_case_	=				np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447] )
       snake_case_	=				1E-2 if torch_device != '''mps''' else 3E-2

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



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  list[int]:




      """simple docstring"""

      snake_case_	=				int(SCREAMING_SNAKE_CASE )

      # Initialize Result
      snake_case_	=				[]

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

      return answer


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

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