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			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[]
						SCREAMING_SNAKE_CASE							:					int           =							[]
						SCREAMING_SNAKE_CASE							:					str           =							[]

						for rt in rc.restypes:
												SCREAMING_SNAKE_CASE							:					str           =							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]		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							{name: i for i, name in enumerate(_lowercase		)}
												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		)

						SCREAMING_SNAKE_CASE							:					Dict           =							torch.tensor(
						    _lowercase	,						dtype=torch.intaa	,						device=protein['''aatype'''].device	,						)
						SCREAMING_SNAKE_CASE							:					int           =							torch.tensor(
						    _lowercase	,						dtype=torch.intaa	,						device=protein['''aatype'''].device	,						)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.tensor(
						    _lowercase	,						dtype=torch.floataa	,						device=protein['''aatype'''].device	,						)
						SCREAMING_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
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							restype_atomaa_to_atomaa[protein_aatype]
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							restype_atomaa_mask[protein_aatype]

						SCREAMING_SNAKE_CASE							:					List[Any]           =							residx_atomaa_mask
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							residx_atomaa_to_atomaa.long()

						# create the gather indices for mapping back
						SCREAMING_SNAKE_CASE							:					List[str]           =							restype_atomaa_to_atomaa[protein_aatype]
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							residx_atomaa_to_atomaa.long()

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

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							restype_atomaa_mask[protein_aatype]
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							residx_atomaa_mask

						return protein



def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Tuple           =							tree_map(lambda _lowercase		: torch.tensor(_lowercase	,						device=batch['''aatype'''].device		)	,						_lowercase	,						np.ndarray		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							tensor_tree_map(lambda _lowercase		: np.array(_lowercase		)	,						make_atomaa_masks(_lowercase		)		)
						return out






import os

try:
					from .build_directory_md import good_file_paths
except ImportError:
					from build_directory_md import good_file_paths  # type: ignore

__UpperCamelCase	: Dict    		=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCamelCase	: Tuple    		=  [file for file in filepaths if file != file.lower()]
if upper_files:
					print(f"""{len(upper_files)} files contain uppercase characters:""")
					print('\n'.join(upper_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if ' ' in file]
if space_files:
					print(f"""{len(space_files)} files contain space characters:""")
					print('\n'.join(space_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if '-' in file]
if hyphen_files:
					print(f"""{len(hyphen_files)} files contain hyphen characters:""")
					print('\n'.join(hyphen_files) + '\n')

__UpperCamelCase	: List[Any]    		=  [file for file in filepaths if os.sep not in file]
if nodir_files:
					print(f"""{len(nodir_files)} files are not in a directory:""")
					print('\n'.join(nodir_files) + '\n')

__UpperCamelCase	: Optional[Any]    		=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
					import sys

					sys.exit(bad_files)





