
def   lowerCamelCase__							(					a		,		a      )      ->   list[int]:
   _A:  Union[str, Any]					=       int(a      )

   # Initialize Result
   _A:  Union[str, Any]					=       []

   # 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__		:     Tuple     			=      []
   UpperCAmelCase__		:     List[Any]     			=      '0'

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





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
   import jax
   import jax.numpy as jnp






@slow
@require_flax
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							Optional[int]     ):

      """simple docstring"""
      return F"""gaussian_noise_s={seed}_shape={'_'.join([str(lowerCAmelCase_     ) for s in shape]     )}.npy"""


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Any=0			,			lowerCAmelCase_     :							Tuple=(4, 4, 6_4, 6_4)			,			lowerCAmelCase_     :							List[str]=False     ):

      """simple docstring"""
      _A:  List[str]					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  Optional[int]					=       jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_			,			lowerCAmelCase_     )     )			,			dtype=lowerCAmelCase_     )
      return image


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							Optional[Any]="CompVis/stable-diffusion-v1-4"     ):

      """simple docstring"""
      _A:  Tuple					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  str					=       '''bf16''' if fpaa else None

      _A			,  _A:  Union[str, Any]					=       FlaxUNetaDConditionModel.from_pretrained(
          lowerCAmelCase_			,			subfolder='''unet'''			,			dtype=lowerCAmelCase_			,			revision=lowerCAmelCase_     )
      return model, params


   def  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							Optional[int]=0			,			lowerCAmelCase_     :							str=(4, 7_7, 7_6_8)			,			lowerCAmelCase_     :							Dict=False     ):

      """simple docstring"""
      _A:  Optional[int]					=       jnp.bfloataa if fpaa else jnp.floataa
      _A:  Union[str, Any]					=       jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase_			,			lowerCAmelCase_     )     )			,			dtype=lowerCAmelCase_     )
      return hidden_states


   @parameterized.expand(
       [
           # fmt: off
           [8_3, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
           [1_7, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
           [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
           [3, 1_0_0_0, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
           # fmt: on
       ]     )
   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Any     ):

      """simple docstring"""
      _A			,  _A:  Optional[Any]					=       self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''			,			fpaa=lowerCAmelCase_     )
      _A:  List[str]					=       self.get_latents(lowerCAmelCase_			,			fpaa=lowerCAmelCase_     )
      _A:  Optional[int]					=       self.get_encoder_hidden_states(lowerCAmelCase_			,			fpaa=lowerCAmelCase_     )

      _A:  List[str]					=       model.apply(
          {'''params''': params}			,			lowerCAmelCase_			,			jnp.array(lowerCAmelCase_			,			dtype=jnp.intaa     )			,			encoder_hidden_states=lowerCAmelCase_			,			).sample

      assert sample.shape == latents.shape

      _A:  Any					=       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )			,			dtype=jnp.floataa     )
      _A:  Tuple					=       jnp.array(lowerCAmelCase_			,			dtype=jnp.floataa     )

      # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
      assert jnp.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-2     )


   @parameterized.expand(
       [
           # fmt: off
           [8_3, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
           [1_7, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
           [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
           [3, 1_0_0_0, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
           # fmt: on
       ]     )
   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Optional[Any]			,			lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""
      _A			,  _A:  Union[str, Any]					=       self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''			,			fpaa=lowerCAmelCase_     )
      _A:  Dict					=       self.get_latents(lowerCAmelCase_			,			shape=(4, 4, 9_6, 9_6)			,			fpaa=lowerCAmelCase_     )
      _A:  Dict					=       self.get_encoder_hidden_states(lowerCAmelCase_			,			shape=(4, 7_7, 1_0_2_4)			,			fpaa=lowerCAmelCase_     )

      _A:  Optional[int]					=       model.apply(
          {'''params''': params}			,			lowerCAmelCase_			,			jnp.array(lowerCAmelCase_			,			dtype=jnp.intaa     )			,			encoder_hidden_states=lowerCAmelCase_			,			).sample

      assert sample.shape == latents.shape

      _A:  List[str]					=       jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())     )			,			dtype=jnp.floataa     )
      _A:  List[str]					=       jnp.array(lowerCAmelCase_			,			dtype=jnp.floataa     )

      # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
      assert jnp.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-2     )



