



'''simple docstring'''






def  snake_case_     (					_lowerCAmelCase  :						list[int]   ,      _lowerCAmelCase  :						str  )      ->   list[int]:
							UpperCAmelCase :       Optional[Any] 							=		int(_lowerCAmelCase  )

							# Initialize Result
							UpperCAmelCase :       List[Any] 							=		[]

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

							return answer


# Driver Code
if __name__ == "__main__":
						UpperCamelCase__:   int								=			[]
						UpperCamelCase__:   Optional[Any]								=			"0"

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













from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			[]
				lowercase__    =			[]
				lowercase__    =			[]

				for rt in rc.restypes:
								lowercase__    =			rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
								restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]				)
								lowercase__    =			{name: i for i, name in enumerate(lowerCamelCase_				)}
								restype_atomaa_to_atomaa_list.append(
								    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]				)

								restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]				)

				# Add dummy mapping for restype 'UNK'
				restype_atomaa_to_atomaa_list.append([0] * 14				)
				restype_atomaa_to_atomaa_list.append([0] * 37				)
				restype_atomaa_mask_list.append([0.0] * 14				)

				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.intaa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.intaa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			torch.tensor(
				    lowerCamelCase_		,   dtype=torch.floataa		,   device=protein['''aatype'''].device		,   )
				lowercase__    =			protein['''aatype'''].to(torch.long				)

				# create the mapping for (residx, atom14) --> atom37, i.e. an array
				# with shape (num_res, 14) containing the atom37 indices for this protein
				lowercase__    =			restype_atomaa_to_atomaa[protein_aatype]
				lowercase__    =			restype_atomaa_mask[protein_aatype]

				lowercase__    =			residx_atomaa_mask
				lowercase__    =			residx_atomaa_to_atomaa.long()

				# create the gather indices for mapping back
				lowercase__    =			restype_atomaa_to_atomaa[protein_aatype]
				lowercase__    =			residx_atomaa_to_atomaa.long()

				# create the corresponding mask
				lowercase__    =			torch.zeros([21, 37]		,   dtype=torch.floataa		,   device=protein['''aatype'''].device				)
				for restype, restype_letter in enumerate(rc.restypes				):
								lowercase__    =			rc.restype_atoa[restype_letter]
								lowercase__    =			rc.residue_atoms[restype_name]
								for atom_name in atom_names:
												lowercase__    =			rc.atom_order[atom_name]
												lowercase__    =			1

				lowercase__    =			restype_atomaa_mask[protein_aatype]
				lowercase__    =			residx_atomaa_mask

				return protein






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			tree_map(lambda lowerCamelCase_				: torch.tensor(lowerCamelCase_		,   device=batch['''aatype'''].device				)		,   lowerCamelCase_		,   np.ndarray				)
				lowercase__    =			tensor_tree_map(lambda lowerCamelCase_				: np.array(lowerCamelCase_				)		,   make_atomaa_masks(lowerCamelCase_				)				)
				return out




