


from __future__ import annotations

from PIL import Image

# Define glider example
_snake_case					:		Any      =    [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
_snake_case					:		Any      =    [[0, 1, 0], [0, 1, 0], [0, 1, 0]]







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Optional[Any]										=       []
							for i in range(len(__lowerCamelCase     )     ):
														__snake_case		:      List[str]										=       []
														for j in range(len(cells[i]     )     ):
																					# Get the number of live neighbours
																					__snake_case		:      List[Any]										=       0
																					if i > 0 and j > 0:
																												neighbour_count += cells[i - 1][j - 1]
																					if i > 0:
																												neighbour_count += cells[i - 1][j]
																					if i > 0 and j < len(cells[i]     ) - 1:
																												neighbour_count += cells[i - 1][j + 1]
																					if j > 0:
																												neighbour_count += cells[i][j - 1]
																					if j < len(cells[i]     ) - 1:
																												neighbour_count += cells[i][j + 1]
																					if i < len(__lowerCamelCase     ) - 1 and j > 0:
																												neighbour_count += cells[i + 1][j - 1]
																					if i < len(__lowerCamelCase     ) - 1:
																												neighbour_count += cells[i + 1][j]
																					if i < len(__lowerCamelCase     ) - 1 and j < len(cells[i]     ) - 1:
																												neighbour_count += cells[i + 1][j + 1]

																					# Rules of the game of life (excerpt from Wikipedia):
																					# 1. Any live cell with two or three live neighbours survives.
																					# 2. Any dead cell with three live neighbours becomes a live cell.
																					# 3. All other live cells die in the next generation.
																					#    Similarly, all other dead cells stay dead.
																					__snake_case		:      Tuple										=       cells[i][j] == 1
																					if (
																					    (alive and 2 <= neighbour_count <= 3)
																					    or not alive
																					    and neighbour_count == 3
																					):
																												next_generation_row.append(1     )
																					else:
																												next_generation_row.append(0     )

														next_generation.append(__lowerCamelCase     )
							return next_generation







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       []
							for _ in range(__lowerCamelCase     ):
														# Create output image
														__snake_case		:      List[Any]										=       Image.new("RGB"							,     (len(cells[0]     ), len(__lowerCamelCase     ))     )
														__snake_case		:      Dict										=       img.load()

														# Save cells to image
														for x in range(len(__lowerCamelCase     )     ):
																					for y in range(len(cells[0]     )     ):
																												__snake_case		:      Any										=       2_5_5 - cells[y][x] * 2_5_5
																												__snake_case		:      Optional[int]										=       (colour, colour, colour)

        # Save image
														images.append(__lowerCamelCase     )
														__snake_case		:      Any										=       new_generation(__lowerCamelCase     )
							return images


if __name__ == "__main__":
			_snake_case					:		Optional[int]      =    generate_images(GLIDER, 16)
			images[0].save("out.gif", save_all=True, append_images=images[1:])





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     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Any										=       []
							__snake_case		:      Optional[Any]										=       []
							__snake_case		:      List[Any]										=       []

							for rt in rc.restypes:
														__snake_case		:      Any										=       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]     )
														__snake_case		:      Tuple										=       {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] * 1_4     )
							restype_atomaa_to_atomaa_list.append([0] * 3_7     )
							restype_atomaa_mask_list.append([0.0] * 1_4     )

							__snake_case		:      int										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      List[str]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[Any]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.floataa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[int]										=       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
							__snake_case		:      Optional[Any]										=       restype_atomaa_to_atomaa[protein_aatype]
							__snake_case		:      Tuple										=       restype_atomaa_mask[protein_aatype]

							__snake_case		:      Optional[Any]										=       residx_atomaa_mask
							__snake_case		:      Union[str, Any]										=       residx_atomaa_to_atomaa.long()

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

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

							__snake_case		:      List[str]										=       restype_atomaa_mask[protein_aatype]
							__snake_case		:      List[str]										=       residx_atomaa_mask

							return protein







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      str										=       tree_map(lambda __lowerCamelCase     : torch.tensor(__lowerCamelCase							,     device=batch["aatype"].device     )							,     __lowerCamelCase							,     np.ndarray     )
							__snake_case		:      str										=       tensor_tree_map(lambda __lowerCamelCase     : np.array(__lowerCamelCase     )							,     make_atomaa_masks(__lowerCamelCase     )     )
							return out

