



'''simple docstring'''





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class __A			(  A   ,   A   ,   unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[Any]          =       CycleDiffusionPipeline
 __lowerCamelCase    :       str          =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     'negative_prompt',
     'height',
     'width',
     'negative_prompt_embeds',
 }
 __lowerCamelCase    :       Optional[int]          =       PipelineTesterMixin.required_optional_params - {'latents'}
 __lowerCamelCase    :       List[Any]          =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}						)
 __lowerCamelCase    :       List[str]          =       IMAGE_TO_IMAGE_IMAGE_PARAMS
 __lowerCamelCase    :       List[str]          =       IMAGE_TO_IMAGE_IMAGE_PARAMS



 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      torch.manual_seed(0       )
      _a      =  UNetaDConditionModel(
          block_out_channels=(32, 64) ,   layers_per_block=2 ,   sample_size=32 ,   in_channels=4 ,   out_channels=4 ,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,   cross_attention_dim=32 ,   )
      _a      =  DDIMScheduler(
          beta_start=0.00085 ,   beta_end=0.012 ,   beta_schedule='''scaled_linear''' ,   num_train_timesteps=1_000 ,   clip_sample=A ,   set_alpha_to_one=A ,   )
      torch.manual_seed(0       )
      _a      =  AutoencoderKL(
          block_out_channels=[32, 64] ,   in_channels=3 ,   out_channels=3 ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,   latent_channels=4 ,   )
      torch.manual_seed(0       )
      _a      =  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=1_000 ,   )
      _a      =  CLIPTextModel(A       )
      _a      =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

      _a      =  {
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      return components



 def 					a__  (self ,   A ,   A=0       )    ->     List[str]:


      """simple docstring"""



      _a      =  floats_tensor((1, 3, 32, 32) ,   rng=random.Random(A       )       ).to(A       )
      _a      =  image / 2 + 0.5
      if str(A       ).startswith('''mps'''       ):
           _a      =  torch.manual_seed(A       )
      else:
           _a      =  torch.Generator(device=A       ).manual_seed(A       )
      _a      =  {
          '''prompt''': '''An astronaut riding an elephant''',
          '''source_prompt''': '''An astronaut riding a horse''',
          '''image''': image,
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''eta''': 0.1,
          '''strength''': 0.8,
          '''guidance_scale''': 3,
          '''source_guidance_scale''': 1,
          '''output_type''': '''numpy''',
      }
      return inputs



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


      """simple docstring"""



      _a      =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator

      _a      =  self.get_dummy_components()
      _a      =  CycleDiffusionPipeline(**A       )
      _a      =  pipe.to(A       )
      pipe.set_progress_bar_config(disable=A       )

      _a      =  self.get_dummy_inputs(A       )
      _a      =  pipe(**A       )
      _a      =  output.images

      _a      =  images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      _a      =  np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]       )

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



 @unittest.skipIf(torch_device != '''cuda''' ,   '''This test requires a GPU'''       )
 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  self.get_dummy_components()
      for name, module in components.items():
           if hasattr(A ,   '''half'''       ):
                _a      =  module.half()
      _a      =  CycleDiffusionPipeline(**A       )
      _a      =  pipe.to(A       )
      pipe.set_progress_bar_config(disable=A       )

      _a      =  self.get_dummy_inputs(A       )
      _a      =  pipe(**A       )
      _a      =  output.images

      _a      =  images[0, -3:, -3:, -1]

      assert images.shape == (1, 32, 32, 3)
      _a      =  np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]       )

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



 @skip_mps
 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      return super().test_save_load_local()



 @unittest.skip('''non-deterministic pipeline'''       )
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      return super().test_inference_batch_single_identical()



 @skip_mps
 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      return super().test_dict_tuple_outputs_equivalent()



 @skip_mps
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      return super().test_save_load_optional_components()



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


      """simple docstring"""



      return super().test_attention_slicing_forward_pass()






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



 '''simple docstring'''







 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



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



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _a      =  load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/cycle-diffusion/black_colored_car.png'''       )
      _a      =  load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''       )
      _a      =  init_image.resize((512, 512)       )

      _a      =  '''CompVis/stable-diffusion-v1-4'''
      _a      =  DDIMScheduler.from_pretrained(A ,   subfolder='''scheduler'''       )
      _a      =  CycleDiffusionPipeline.from_pretrained(
          A ,   scheduler=A ,   safety_checker=A ,   torch_dtype=torch.floataa ,   revision='''fp16'''       )

      pipe.to(A       )
      pipe.set_progress_bar_config(disable=A       )
      pipe.enable_attention_slicing()

      _a      =  '''A black colored car'''
      _a      =  '''A blue colored car'''

      _a      =  torch.manual_seed(0       )
      _a      =  pipe(
          prompt=A ,   source_prompt=A ,   image=A ,   num_inference_steps=100 ,   eta=0.1 ,   strength=0.85 ,   guidance_scale=3 ,   source_guidance_scale=1 ,   generator=A ,   output_type='''np''' ,   )
      _a      =  output.images

      # the values aren't exactly equal, but the images look the same visually
      assert np.abs(image - expected_image       ).max() < 5E-1



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      _a      =  load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/cycle-diffusion/black_colored_car.png'''       )
      _a      =  load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''       )
      _a      =  init_image.resize((512, 512)       )

      _a      =  '''CompVis/stable-diffusion-v1-4'''
      _a      =  DDIMScheduler.from_pretrained(A ,   subfolder='''scheduler'''       )
      _a      =  CycleDiffusionPipeline.from_pretrained(A ,   scheduler=A ,   safety_checker=A       )

      pipe.to(A       )
      pipe.set_progress_bar_config(disable=A       )
      pipe.enable_attention_slicing()

      _a      =  '''A black colored car'''
      _a      =  '''A blue colored car'''

      _a      =  torch.manual_seed(0       )
      _a      =  pipe(
          prompt=A ,   source_prompt=A ,   image=A ,   num_inference_steps=100 ,   eta=0.1 ,   strength=0.85 ,   guidance_scale=3 ,   source_guidance_scale=1 ,   generator=A ,   output_type='''np''' ,   )
      _a      =  output.images

      assert np.abs(image - expected_image       ).max() < 2E-2








'''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=" ")



