

def 		UpperCamelCase   (lowercase_:					list[int]       , lowercase_:					str   )	-> list[int]:
	A__				:       int	  =						int(lowercase_   )

	# Initialize Result
	A__				:       List[str]	  =						[]

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

	return answer


# Driver Code
if __name__ == "__main__":
	A_			:					Any    			=     []
	A_			:					Tuple    			=     '0'

	if (
	    input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
	    == "y"
	):
		A_			:					Optional[int]    			=     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()))
		A_			:					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
		A_			:					Optional[Any]    			=     [1, 2, 5, 10, 20, 50, 100, 500, 2000]
		A_			:					str    			=     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}: ''')
		A_			:					Union[str, Any]    			=     find_minimum_change(denominations, value)
		# Print result
		for i in range(len(answer)):
			print(answer[i], end=' ')






import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class        _a		(unittest.TestCase						,  __magic_name__							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__				:       List[str]	  =						load_tool("""text-to-speech"""     )
						self.tool.setup()






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Tuple	  =						self.tool("""hey"""     )
						A__				:       Tuple	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Any	  =						self.tool("""hey"""     )
						A__				:       Optional[Any]	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )



