


'''simple docstring'''



import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


__lowercase	:						Union[str, Any]       =	Mapping[str, np.ndarray]
__lowercase	:						Tuple       =	Mapping[str, Any]  # Is a nested dict.
__lowercase	:						Any       =	0.01





@dataclasses.dataclass(frozen=lowerCAmelCase_					)
class 							__UpperCamelCase				:
	A_				=    42  # [num_res, num_atom_type, 3]

	# Amino-acid type for each residue represented as an integer between 0 and
	# 20, where 20 is 'X'.
	A_				=    42  # [num_res]

	# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
	# is present and 0.0 if not. This should be used for loss masking.
	A_				=    42  # [num_res, num_atom_type]

	# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
	A_				=    42  # [num_res]

	# B-factors, or temperature factors, of each residue (in sq. angstroms units),
	# representing the displacement of the residue from its ground truth mean
	# value.
	A_				=    42  # [num_res, num_atom_type]

	# Chain indices for multi-chain predictions
	A_				=    None

	# Optional remark about the protein. Included as a comment in output PDB
	# files
	A_				=    None

	# Templates used to generate this protein (prediction-only)
	A_				=    None

	# Chain corresponding to each parent
	A_				=    None






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					__a			:       List[str]					    =  r'(\[[A-Z]+\]\n)'
					__a			:       List[str]					    =  [tag.strip() for tag in re.split(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) if len(_SCREAMING_SNAKE_CASE   ) > 0]
					__a			:       Iterator[Tuple[str, List[str]]]					    =  zip(tags[0::2]						,      [l.split('\n'   ) for l in tags[1::2]]   )

					__a			:       List[str]					    =  ["N", "CA", "C"]
					__a			:       Dict					    =  None
					__a			:       Optional[int]					    =  None
					__a			:       Optional[Any]					    =  None
					for g in groups:
										if "[PRIMARY]" == g[0]:
															__a			:       str					    =  g[1][0].strip()
															for i in range(len(_SCREAMING_SNAKE_CASE   )   ):
																				if seq[i] not in residue_constants.restypes:
																									__a			:       Union[str, Any]					    =  'X'  # FIXME: strings are immutable
															__a			:       List[Any]					    =  np.array(
															    [residue_constants.restype_order.get(_SCREAMING_SNAKE_CASE						,      residue_constants.restype_num   ) for res_symbol in seq]   )
										elif "[TERTIARY]" == g[0]:
															__a			:       List[List[float]]					    =  []
															for axis in range(3   ):
																				tertiary.append(list(map(_SCREAMING_SNAKE_CASE						,      g[1][axis].split()   )   )   )
															__a			:       List[Any]					    =  np.array(_SCREAMING_SNAKE_CASE   )
															__a			:       str					    =  np.zeros((len(tertiary[0]   ) // 3, residue_constants.atom_type_num, 3)   ).astype(np.floataa   )
															for i, atom in enumerate(_SCREAMING_SNAKE_CASE   ):
																				__a			:       Dict					    =  np.transpose(tertiary_np[:, i::3]   )
															atom_positions *= PICO_TO_ANGSTROM
										elif "[MASK]" == g[0]:
															__a			:       int					    =  np.array(list(map({'-': 0, '+': 1}.get						,      g[1][0].strip()   )   )   )
															__a			:       str					    =  np.zeros(
															    (
															        len(_SCREAMING_SNAKE_CASE   ),
															        residue_constants.atom_type_num,
															    )   ).astype(np.floataa   )
															for i, atom in enumerate(_SCREAMING_SNAKE_CASE   ):
																				__a			:       Optional[Any]					    =  1
															atom_mask *= mask[..., None]

					assert aatype is not None

					return Protein(
					    atom_positions=_SCREAMING_SNAKE_CASE						,      atom_mask=_SCREAMING_SNAKE_CASE						,      aatype=_SCREAMING_SNAKE_CASE						,      residue_index=np.arange(len(_SCREAMING_SNAKE_CASE   )   )						,      b_factors=_SCREAMING_SNAKE_CASE						,      )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Protein						,      _SCREAMING_SNAKE_CASE      :   int = 0   ):
					__a			:       List[str]					    =  []

					__a			:       Any					    =  prot.remark
					if remark is not None:
										pdb_headers.append(F"""REMARK {remark}"""   )

					__a			:       Any					    =  prot.parents
					__a			:       Tuple					    =  prot.parents_chain_index
					if parents is not None and parents_chain_index is not None:
										__a			:       Tuple					    =  [p for i, p in zip(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) if i == chain_id]

					if parents is None or len(_SCREAMING_SNAKE_CASE   ) == 0:
										__a			:       List[str]					    =  ['N/A']

					pdb_headers.append(F"""PARENT {" ".join(_SCREAMING_SNAKE_CASE   )}"""   )

					return pdb_headers






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Protein						,      _SCREAMING_SNAKE_CASE      :   str   ):
					__a			:       List[str]					    =  []
					__a			:       List[str]					    =  pdb_str.split('\n'   )

					__a			:       List[Any]					    =  prot.remark
					if remark is not None:
										out_pdb_lines.append(F"""REMARK {remark}"""   )

					__a			:       List[List[str]]
					if prot.parents is not None and len(prot.parents   ) > 0:
										__a			:       List[Any]					    =  []
										if prot.parents_chain_index is not None:
															__a			:       Dict[str, List[str]]					    =  {}
															for p, i in zip(prot.parents						,      prot.parents_chain_index   ):
																				parent_dict.setdefault(str(_SCREAMING_SNAKE_CASE   )						,      []   )
																				parent_dict[str(_SCREAMING_SNAKE_CASE   )].append(_SCREAMING_SNAKE_CASE   )

															__a			:       Any					    =  max([int(_SCREAMING_SNAKE_CASE   ) for chain_idx in parent_dict]   )
															for i in range(max_idx + 1   ):
																				__a			:       str					    =  parent_dict.get(str(_SCREAMING_SNAKE_CASE   )						,      ['N/A']   )
																				parents_per_chain.append(_SCREAMING_SNAKE_CASE   )
										else:
															parents_per_chain.append(list(prot.parents   )   )
					else:
										__a			:       List[str]					    =  [['N/A']]

					def make_parent_line(_SCREAMING_SNAKE_CASE      :   Sequence[str]   ) -> str:
										return F"""PARENT {" ".join(_SCREAMING_SNAKE_CASE   )}"""

					out_pdb_lines.append(make_parent_line(parents_per_chain[0]   )   )

					__a			:       int					    =  0
					for i, l in enumerate(_SCREAMING_SNAKE_CASE   ):
										if "PARENT" not in l and "REMARK" not in l:
															out_pdb_lines.append(_SCREAMING_SNAKE_CASE   )
										if "TER" in l and "END" not in lines[i + 1]:
															chain_counter += 1
															if not chain_counter >= len(_SCREAMING_SNAKE_CASE   ):
																				__a			:       Optional[int]					    =  parents_per_chain[chain_counter]
															else:
																				__a			:       int					    =  ['N/A']

															out_pdb_lines.append(make_parent_line(_SCREAMING_SNAKE_CASE   )   )

					return "\n".join(_SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Protein   ):
					__a			:       Union[str, Any]					    =  residue_constants.restypes + ['X']

					def res_atoa(_SCREAMING_SNAKE_CASE      :   int   ) -> str:
										return residue_constants.restype_atoa.get(restypes[r]						,      'UNK'   )

					__a			:       Any					    =  residue_constants.atom_types

					__a			:       List[str]					    =  []

					__a			:       str					    =  prot.atom_mask
					__a			:       str					    =  prot.aatype
					__a			:       str					    =  prot.atom_positions
					__a			:       Union[str, Any]					    =  prot.residue_index.astype(np.intaa   )
					__a			:       int					    =  prot.b_factors
					__a			:       Tuple					    =  prot.chain_index

					if np.any(aatype > residue_constants.restype_num   ):
										raise ValueError('Invalid aatypes.'   )

					__a			:       Tuple					    =  get_pdb_headers(_SCREAMING_SNAKE_CASE   )
					if len(_SCREAMING_SNAKE_CASE   ) > 0:
										pdb_lines.extend(_SCREAMING_SNAKE_CASE   )

					__a			:       List[Any]					    =  aatype.shape[0]
					__a			:       Any					    =  1
					__a			:       List[Any]					    =  0
					__a			:       Optional[Any]					    =  string.ascii_uppercase
					__a			:       Any					    =  None
					# Add all atom sites.
					for i in range(_SCREAMING_SNAKE_CASE   ):
										__a			:       Optional[Any]					    =  res_atoa(aatype[i]   )
										for atom_name, pos, mask, b_factor in zip(_SCREAMING_SNAKE_CASE						,      atom_positions[i]						,      atom_mask[i]						,      b_factors[i]   ):
															if mask < 0.5:
																				continue

															__a			:       int					    =  'ATOM'
															__a			:       Tuple					    =  atom_name if len(_SCREAMING_SNAKE_CASE   ) == 4 else F""" {atom_name}"""
															__a			:       Union[str, Any]					    =  ''
															__a			:       Tuple					    =  ''
															__a			:       str					    =  1.0_0
															__a			:       Union[str, Any]					    =  atom_name[0]  # Protein supports only C, N, O, S, this works.
															__a			:       str					    =  ''

															__a			:       List[Any]					    =  'A'
															if chain_index is not None:
																				__a			:       Any					    =  chain_tags[chain_index[i]]

															# PDB is a columnar format, every space matters here!
															__a			:       int					    =  (
															    F"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
															    F"""{res_name_a:>3} {chain_tag:>1}"""
															    F"""{residue_index[i]:>4}{insertion_code:>1}   """
															    F"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
															    F"""{occupancy:>6.2f}{b_factor:>6.2f}          """
															    F"""{element:>2}{charge:>2}"""
															)
															pdb_lines.append(_SCREAMING_SNAKE_CASE   )
															atom_index += 1

										__a			:       Union[str, Any]					    =  i == n - 1
										if chain_index is not None:
															if i != n - 1 and chain_index[i + 1] != prev_chain_index:
																				__a			:       Tuple					    =  True
																				__a			:       Dict					    =  chain_index[i + 1]

										if should_terminate:
															# Close the chain.
															__a			:       int					    =  'TER'
															__a			:       List[Any]					    =  (
															    F"""{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]   ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
															)
															pdb_lines.append(_SCREAMING_SNAKE_CASE   )
															atom_index += 1

															if i != n - 1:
																				# "prev" is a misnomer here. This happens at the beginning of
																				# each new chain.
																				pdb_lines.extend(get_pdb_headers(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )   )

					pdb_lines.append('END'   )
					pdb_lines.append(''   )
					return "\n".join(_SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Protein   ):
					return residue_constants.STANDARD_ATOM_MASK[prot.aatype]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   FeatureDict						,      _SCREAMING_SNAKE_CASE      :   ModelOutput						,      _SCREAMING_SNAKE_CASE      :   Optional[np.ndarray] = None						,      _SCREAMING_SNAKE_CASE      :   Optional[np.ndarray] = None						,      _SCREAMING_SNAKE_CASE      :   Optional[str] = None						,      _SCREAMING_SNAKE_CASE      :   Optional[Sequence[str]] = None						,      _SCREAMING_SNAKE_CASE      :   Optional[Sequence[int]] = None						,      ):
					return Protein(
					    aatype=features['aatype']						,      atom_positions=result['final_atom_positions']						,      atom_mask=result['final_atom_mask']						,      residue_index=features['residue_index'] + 1						,      b_factors=b_factors if b_factors is not None else np.zeros_like(result['final_atom_mask']   )						,      chain_index=_SCREAMING_SNAKE_CASE						,      remark=_SCREAMING_SNAKE_CASE						,      parents=_SCREAMING_SNAKE_CASE						,      parents_chain_index=_SCREAMING_SNAKE_CASE						,      )







'''simple docstring'''



#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist






def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int   ):
					with open(_SCREAMING_SNAKE_CASE						,      'r'   ) as fh:
										fcntl.flock(_SCREAMING_SNAKE_CASE						,      fcntl.LOCK_EX   )
										try:
															print(*_SCREAMING_SNAKE_CASE   )
										finally:
															fcntl.flock(_SCREAMING_SNAKE_CASE						,      fcntl.LOCK_UN   )


__lowercase	:						Dict       =	int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__lowercase	:						Tuple       =	torch.device('cuda', local_rank)
__lowercase	:						Optional[int]       =	socket.gethostname()

__lowercase	:						List[str]       =	f'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	__lowercase	:						str       =	dist.get_rank()
	__lowercase	:						Union[str, Any]       =	dist.get_world_size()

	printflock(f'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(f'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(f'''{gpu} is broken''')
	raise



