


import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort







class lowerCAmelCase_						(			UpperCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 pass







@nightly
@require_onnxruntime
@require_torch_gpu
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 @property
 def 						UpperCamelCase__					(     self ):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				ort.SessionOptions()
       snake_case_	=				False
       return options



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
       snake_case_	=				OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''		,				revision='''onnx'''		,				safety_checker=__lowerCamelCase		,				feature_extractor=__lowerCamelCase		,				provider=self.gpu_provider		,				sess_options=self.gpu_options		,				)
       pipe.set_progress_bar_config(disable=__lowerCamelCase )

       snake_case_	=				'''A red cat sitting on a park bench'''

       snake_case_	=				np.random.RandomState(0 )
       snake_case_	=				pipe(
           prompt=__lowerCamelCase		,				image=__lowerCamelCase		,				mask_image=__lowerCamelCase		,				guidance_scale=7.5		,				num_inference_steps=10		,				generator=__lowerCamelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images
       snake_case_	=				images[0, 2_55:2_58, 2_55:2_58, -1]

       assert images.shape == (1, 5_12, 5_12, 3)
       snake_case_	=				np.array([0.2_514, 0.3_007, 0.3_517, 0.1_790, 0.2_382, 0.3_167, 0.1_944, 0.2_273, 0.2_464] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
       snake_case_	=				LMSDiscreteScheduler.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''		,				subfolder='''scheduler'''		,				revision='''onnx''' )
       snake_case_	=				OnnxStableDiffusionInpaintPipeline.from_pretrained(
           '''runwayml/stable-diffusion-inpainting'''		,				revision='''onnx'''		,				scheduler=__lowerCamelCase		,				safety_checker=__lowerCamelCase		,				feature_extractor=__lowerCamelCase		,				provider=self.gpu_provider		,				sess_options=self.gpu_options		,				)
       pipe.set_progress_bar_config(disable=__lowerCamelCase )

       snake_case_	=				'''A red cat sitting on a park bench'''

       snake_case_	=				np.random.RandomState(0 )
       snake_case_	=				pipe(
           prompt=__lowerCamelCase		,				image=__lowerCamelCase		,				mask_image=__lowerCamelCase		,				guidance_scale=7.5		,				num_inference_steps=20		,				generator=__lowerCamelCase		,				output_type='''np'''		,				)
       snake_case_	=				output.images
       snake_case_	=				images[0, 2_55:2_58, 2_55:2_58, -1]

       assert images.shape == (1, 5_12, 5_12, 3)
       snake_case_	=				np.array([0.0_086, 0.0_077, 0.0_083, 0.0_093, 0.0_107, 0.0_139, 0.0_094, 0.0_097, 0.0_125] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3



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=""" """)