


'''simple docstring'''

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 							__magic_name__(						lowerCamelCase):
      __lowerCAmelCase							       =				[]
      __lowerCAmelCase							       =				[]
      __lowerCAmelCase							       =				[]

      for rt in rc.restypes:
            __lowerCAmelCase							       =				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])
            __lowerCAmelCase							       =				{name: i for i, name in enumerate(SCREAMING_SNAKE_CASE_)}
            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] * 1_4)
      restype_atomaa_to_atomaa_list.append([0] * 3_7)
      restype_atomaa_mask_list.append([0.0] * 1_4)

      __lowerCAmelCase							       =				torch.tensor(
          SCREAMING_SNAKE_CASE_,				dtype=torch.intaa,				device=protein['''aatype'''].device,				)
      __lowerCAmelCase							       =				torch.tensor(
          SCREAMING_SNAKE_CASE_,				dtype=torch.intaa,				device=protein['''aatype'''].device,				)
      __lowerCAmelCase							       =				torch.tensor(
          SCREAMING_SNAKE_CASE_,				dtype=torch.floataa,				device=protein['''aatype'''].device,				)
      __lowerCAmelCase							       =				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
      __lowerCAmelCase							       =				restype_atomaa_to_atomaa[protein_aatype]
      __lowerCAmelCase							       =				restype_atomaa_mask[protein_aatype]

      __lowerCAmelCase							       =				residx_atomaa_mask
      __lowerCAmelCase							       =				residx_atomaa_to_atomaa.long()

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

      # create the corresponding mask
      __lowerCAmelCase							       =				torch.zeros([2_1, 3_7],				dtype=torch.floataa,				device=protein['''aatype'''].device)
      for restype, restype_letter in enumerate(rc.restypes):
            __lowerCAmelCase							       =				rc.restype_atoa[restype_letter]
            __lowerCAmelCase							       =				rc.residue_atoms[restype_name]
            for atom_name in atom_names:
                  __lowerCAmelCase							       =				rc.atom_order[atom_name]
                  __lowerCAmelCase							       =				1

      __lowerCAmelCase							       =				restype_atomaa_mask[protein_aatype]
      __lowerCAmelCase							       =				residx_atomaa_mask

      return protein




def 							__magic_name__(						lowerCamelCase):
      __lowerCAmelCase							       =				tree_map(lambda lowerCamelCase: torch.tensor(SCREAMING_SNAKE_CASE_,				device=batch['''aatype'''].device),				SCREAMING_SNAKE_CASE_,				np.ndarray)
      __lowerCAmelCase							       =				tensor_tree_map(lambda lowerCamelCase: np.array(SCREAMING_SNAKE_CASE_),				make_atomaa_masks(SCREAMING_SNAKE_CASE_))
      return out





'''simple docstring'''

from PIL import Image

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):

						def brightness(lowerCamelCase) -> float:
												return 1_2_8 + level + (c - 1_2_8)

						if not -2_55.0 <= level <= 2_55.0:
												raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''')
						return img.point(lowerCamelCase)


if __name__ == "__main__":
	# Load image
	with Image.open("""image_data/lena.jpg""") as img:
		# Change brightness to 100
		_UpperCAmelCase				: str							    =				change_brightness(img, 1_0_0)
		brigt_img.save("""image_data/lena_brightness.png""", format="""png""")

