



"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A:     Optional[Any]										=	logging.getLogger(__name__)



@dataclass
class    SCREAMING_SNAKE_CASE__						:
			__lowerCAmelCase						:     str		=		field(
			    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
			__lowerCAmelCase						:     Optional[str]		=		field(
			    default=UpperCAmelCase__   ,    metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
			__lowerCAmelCase						:     Optional[str]		=		field(
			    default='NER'   ,    metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}				)
			__lowerCAmelCase						:     Optional[str]		=		field(
			    default=UpperCAmelCase__   ,    metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
			__lowerCAmelCase						:     bool		=		field(default=UpperCAmelCase__   ,    metadata={'help': 'Set this flag to use fast tokenization.'}				)
			# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
			# or just modify its tokenizer_config.json.
			__lowerCAmelCase						:     Optional[str]		=		field(
			    default=UpperCAmelCase__   ,    metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,    )



@dataclass
class    SCREAMING_SNAKE_CASE__						:
			__lowerCAmelCase						:     str		=		field(
			    metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}				)
			__lowerCAmelCase						:     Optional[str]		=		field(
			    default=UpperCAmelCase__   ,    metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}   ,    )
			__lowerCAmelCase						:     int		=		field(
			    default=128   ,    metadata={
			        'help': (
			            'The maximum total input sequence length after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded.'
			        )
			    }   ,    )
			__lowerCAmelCase						:     bool		=		field(
			    default=UpperCAmelCase__   ,    metadata={'help': 'Overwrite the cached training and evaluation sets'}				)

def    _snake_case		(		):
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	UpperCAmelCase			:      Tuple			=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
	if len(sys.argv		) == 2 and sys.argv[1].endswith(""".json"""		):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		UpperCAmelCase			:      Dict			=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
	else:
		UpperCAmelCase			:      Union[str, Any]			=			parser.parse_args_into_dataclasses()

	if (
	    os.path.exists(training_args.output_dir		)
	    and os.listdir(training_args.output_dir		)
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
		    """ --overwrite_output_dir to overcome."""		)

	UpperCAmelCase			:      Tuple			=			import_module("""tasks"""		)
	try:
		UpperCAmelCase			:      int			=			getattr(UpperCamelCase    ,	model_args.task_type		)
		UpperCAmelCase			:      TokenClassificationTask			=			token_classification_task_clazz()
	except AttributeError:
		raise ValueError(
		    F"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
		    F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"		)

	# Setup logging
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""    ,	datefmt="""%m/%d/%Y %H:%M:%S"""    ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN    ,	)
	logger.warning(
	    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""    ,	training_args.local_rank    ,	training_args.device    ,	training_args.n_gpu    ,	bool(training_args.local_rank != -1		)    ,	training_args.fpaa    ,	)
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank		):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info("""Training/evaluation parameters %s"""    ,	UpperCamelCase		)

	# Set seed
	set_seed(training_args.seed		)

	# Prepare CONLL-2003 task
	UpperCAmelCase			:      List[str]			=			token_classification_task.get_labels(data_args.labels		)
	UpperCAmelCase			:      Dict[int, str]			=			dict(enumerate(UpperCamelCase		)		)
	UpperCAmelCase			:      Tuple			=			len(UpperCamelCase		)

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	UpperCAmelCase			:      List[str]			=			AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,	num_labels=UpperCamelCase    ,	idalabel=UpperCamelCase    ,	labelaid={label: i for i, label in enumerate(UpperCamelCase		)}    ,	cache_dir=model_args.cache_dir    ,	)
	UpperCAmelCase			:      Any			=			AutoTokenizer.from_pretrained(
	    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path    ,	cache_dir=model_args.cache_dir    ,	use_fast=model_args.use_fast    ,	)
	UpperCAmelCase			:      Optional[Any]			=			AutoModelForTokenClassification.from_pretrained(
	    model_args.model_name_or_path    ,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path		)    ,	config=UpperCamelCase    ,	cache_dir=model_args.cache_dir    ,	)

	# Get datasets
	UpperCAmelCase			:      List[str]			=			(
	    TokenClassificationDataset(
	        token_classification_task=UpperCamelCase    ,	data_dir=data_args.data_dir    ,	tokenizer=UpperCamelCase    ,	labels=UpperCamelCase    ,	model_type=config.model_type    ,	max_seq_length=data_args.max_seq_length    ,	overwrite_cache=data_args.overwrite_cache    ,	mode=Split.train    ,	)
	    if training_args.do_train
	    else None
	)
	UpperCAmelCase			:      List[Any]			=			(
	    TokenClassificationDataset(
	        token_classification_task=UpperCamelCase    ,	data_dir=data_args.data_dir    ,	tokenizer=UpperCamelCase    ,	labels=UpperCamelCase    ,	model_type=config.model_type    ,	max_seq_length=data_args.max_seq_length    ,	overwrite_cache=data_args.overwrite_cache    ,	mode=Split.dev    ,	)
	    if training_args.do_eval
	    else None
	)

	def align_predictions(UpperCamelCase		:	np.ndarray    ,	UpperCamelCase		:	np.ndarray		) -> Tuple[List[int], List[int]]:
		UpperCAmelCase			:      Tuple			=			np.argmax(UpperCamelCase    ,	axis=2		)

		UpperCAmelCase			:      str			=			preds.shape

		UpperCAmelCase			:      int			=			[[] for _ in range(UpperCamelCase		)]
		UpperCAmelCase			:      Optional[Any]			=			[[] for _ in range(UpperCamelCase		)]

		for i in range(UpperCamelCase		):
			for j in range(UpperCamelCase		):
				if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
					out_label_list[i].append(label_map[label_ids[i][j]]		)
					preds_list[i].append(label_map[preds[i][j]]		)

		return preds_list, out_label_list

	def compute_metrics(UpperCamelCase		:	EvalPrediction		) -> Dict:
		UpperCAmelCase			:      Tuple			=			align_predictions(p.predictions    ,	p.label_ids		)
		return {
		    "accuracy_score": accuracy_score(UpperCamelCase    ,	UpperCamelCase		),
		    "precision": precision_score(UpperCamelCase    ,	UpperCamelCase		),
		    "recall": recall_score(UpperCamelCase    ,	UpperCamelCase		),
		    "f1": fa_score(UpperCamelCase    ,	UpperCamelCase		),
		}

	# Data collator
	UpperCAmelCase			:      Optional[int]			=			DataCollatorWithPadding(UpperCamelCase    ,	pad_to_multiple_of=8		) if training_args.fpaa else None

	# Initialize our Trainer
	UpperCAmelCase			:      List[Any]			=			Trainer(
	    model=UpperCamelCase    ,	args=UpperCamelCase    ,	train_dataset=UpperCamelCase    ,	eval_dataset=UpperCamelCase    ,	compute_metrics=UpperCamelCase    ,	data_collator=UpperCamelCase    ,	)

	# Training
	if training_args.do_train:
		trainer.train(
		    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
		trainer.save_model()
		# For convenience, we also re-save the tokenizer to the same directory,
		# so that you can share your model easily on huggingface.co/models =)
		if trainer.is_world_process_zero():
			tokenizer.save_pretrained(training_args.output_dir		)

			 # Evaluation
	UpperCAmelCase			:      List[Any]			=			{}
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""		)

		UpperCAmelCase			:      str			=			trainer.evaluate()

		UpperCAmelCase			:      List[str]			=			os.path.join(training_args.output_dir    ,	"""eval_results.txt"""		)
		if trainer.is_world_process_zero():
			with open(UpperCamelCase    ,	"""w"""		) as writer:
				logger.info("""***** Eval results *****"""		)
				for key, value in result.items():
					logger.info("""  %s = %s"""    ,	UpperCamelCase    ,	UpperCamelCase		)
					writer.write("""%s = %s\n""" % (key, value)		)

			results.update(UpperCamelCase		)

			 # Predict
	if training_args.do_predict:
		UpperCAmelCase			:      int			=			TokenClassificationDataset(
		    token_classification_task=UpperCamelCase    ,	data_dir=data_args.data_dir    ,	tokenizer=UpperCamelCase    ,	labels=UpperCamelCase    ,	model_type=config.model_type    ,	max_seq_length=data_args.max_seq_length    ,	overwrite_cache=data_args.overwrite_cache    ,	mode=Split.test    ,	)

		UpperCAmelCase			:      Union[str, Any]			=			trainer.predict(UpperCamelCase		)
		UpperCAmelCase			:      Any			=			align_predictions(UpperCamelCase    ,	UpperCamelCase		)

		UpperCAmelCase			:      int			=			os.path.join(training_args.output_dir    ,	"""test_results.txt"""		)
		if trainer.is_world_process_zero():
			with open(UpperCamelCase    ,	"""w"""		) as writer:
				for key, value in metrics.items():
					logger.info("""  %s = %s"""    ,	UpperCamelCase    ,	UpperCamelCase		)
					writer.write("""%s = %s\n""" % (key, value)		)

					   # Save predictions
		UpperCAmelCase			:      Tuple			=			os.path.join(training_args.output_dir    ,	"""test_predictions.txt"""		)
		if trainer.is_world_process_zero():
			with open(UpperCamelCase    ,	"""w"""		) as writer:
				with open(os.path.join(data_args.data_dir    ,	"""test.txt"""		)    ,	"""r"""		) as f:
					token_classification_task.write_predictions_to_file(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)

	return results

def    _snake_case		(		UpperCamelCase		:	Optional[int]		):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
						main()





"""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


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



@dataclasses.dataclass(frozen=UpperCAmelCase__				)
class    SCREAMING_SNAKE_CASE__						:
   __lowerCAmelCase						:     np.ndarray  # [num_res, num_atom_type, 3]

   # Amino-acid type for each residue represented as an integer between 0 and
   # 20, where 20 is 'X'.
   __lowerCAmelCase						:     np.ndarray  # [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.
   __lowerCAmelCase						:     np.ndarray  # [num_res, num_atom_type]

   # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
   __lowerCAmelCase						:     np.ndarray  # [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.
   __lowerCAmelCase						:     np.ndarray  # [num_res, num_atom_type]

   # Chain indices for multi-chain predictions
   __lowerCAmelCase						:     Optional[np.ndarray]		=		None

   # Optional remark about the protein. Included as a comment in output PDB
   # files
   __lowerCAmelCase						:     Optional[str]		=		None

   # Templates used to generate this protein (prediction-only)
   __lowerCAmelCase						:     Optional[Sequence[str]]		=		None

   # Chain corresponding to each parent
   __lowerCAmelCase						:     Optional[Sequence[int]]		=		None

def    _snake_case		(		UpperCamelCase		:	str		):
 UpperCAmelCase			:      str			=			R"""(\[[A-Z]+\]\n)"""
 UpperCAmelCase			:      List[str]			=			[tag.strip() for tag in re.split(UpperCamelCase    ,	UpperCamelCase		) if len(UpperCamelCase		) > 0]
 UpperCAmelCase			:      Iterator[Tuple[str, List[str]]]			=			zip(tags[0::2]    ,	[l.split("""\n"""		) for l in tags[1::2]]		)

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

 assert aatype is not None

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

def    _snake_case		(		UpperCamelCase		:	Protein    ,	UpperCamelCase		:	int = 0		):
 UpperCAmelCase			:      List[str]			=			[]

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

 UpperCAmelCase			:      List[str]			=			prot.parents
 UpperCAmelCase			:      List[Any]			=			prot.parents_chain_index
 if parents is not None and parents_chain_index is not None:
  UpperCAmelCase			:      int			=			[p for i, p in zip(UpperCamelCase    ,	UpperCamelCase		) if i == chain_id]

 if parents is None or len(UpperCamelCase		) == 0:
  UpperCAmelCase			:      Tuple			=			["""N/A"""]

 pdb_headers.append(F"PARENT {' '.join(UpperCamelCase		)}"		)

 return pdb_headers

def    _snake_case		(		UpperCamelCase		:	Protein    ,	UpperCamelCase		:	str		):
 UpperCAmelCase			:      List[str]			=			[]
 UpperCAmelCase			:      Tuple			=			pdb_str.split("""\n"""		)

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

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

   UpperCAmelCase			:      Tuple			=			max([int(UpperCamelCase		) for chain_idx in parent_dict]		)
   for i in range(max_idx + 1		):
    UpperCAmelCase			:      int			=			parent_dict.get(str(UpperCamelCase		)    ,	["""N/A"""]		)
    parents_per_chain.append(UpperCamelCase		)
  else:
   parents_per_chain.append(list(prot.parents		)		)
 else:
  UpperCAmelCase			:      Union[str, Any]			=			[["""N/A"""]]

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

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

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

   out_pdb_lines.append(make_parent_line(UpperCamelCase		)		)

 return "\n".join(UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	Protein		):
 UpperCAmelCase			:      int			=			residue_constants.restypes + ["""X"""]

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

 UpperCAmelCase			:      List[str]			=			residue_constants.atom_types

 UpperCAmelCase			:      List[str]			=			[]

 UpperCAmelCase			:      Dict			=			prot.atom_mask
 UpperCAmelCase			:      Optional[int]			=			prot.aatype
 UpperCAmelCase			:      Optional[int]			=			prot.atom_positions
 UpperCAmelCase			:      str			=			prot.residue_index.astype(np.intaa		)
 UpperCAmelCase			:      Tuple			=			prot.b_factors
 UpperCAmelCase			:      str			=			prot.chain_index

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

 UpperCAmelCase			:      Any			=			get_pdb_headers(UpperCamelCase		)
 if len(UpperCamelCase		) > 0:
  pdb_lines.extend(UpperCamelCase		)

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

   UpperCAmelCase			:      List[Any]			=			"""ATOM"""
   UpperCAmelCase			:      Dict			=			atom_name if len(UpperCamelCase		) == 4 else F" {atom_name}"
   UpperCAmelCase			:      Union[str, Any]			=			""""""
   UpperCAmelCase			:      Dict			=			""""""
   UpperCAmelCase			:      Optional[int]			=			1.00
   UpperCAmelCase			:      Union[str, Any]			=			atom_name[0]  # Protein supports only C, N, O, S, this works.
   UpperCAmelCase			:      Any			=			""""""

   UpperCAmelCase			:      List[Any]			=			"""A"""
   if chain_index is not None:
    UpperCAmelCase			:      List[str]			=			chain_tags[chain_index[i]]

   # PDB is a columnar format, every space matters here!
   UpperCAmelCase			:      Dict			=			(
       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(UpperCamelCase		)
   atom_index += 1

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

  if should_terminate:
   # Close the chain.
   UpperCAmelCase			:      Optional[int]			=			"""TER"""
   UpperCAmelCase			:      Union[str, Any]			=			(
       F"{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]		):>3} {chain_tag:>1}{residue_index[i]:>4}"
   )
   pdb_lines.append(UpperCamelCase		)
   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(UpperCamelCase    ,	UpperCamelCase		)		)

 pdb_lines.append("""END"""		)
 pdb_lines.append(""""""		)
 return "\n".join(UpperCamelCase		)

def    _snake_case		(		UpperCamelCase		:	Protein		):
 return residue_constants.STANDARD_ATOM_MASK[prot.aatype]

def    _snake_case		(		UpperCamelCase		:	FeatureDict    ,	UpperCamelCase		:	ModelOutput    ,	UpperCamelCase		:	Optional[np.ndarray] = None    ,	UpperCamelCase		:	Optional[np.ndarray] = None    ,	UpperCamelCase		:	Optional[str] = None    ,	UpperCamelCase		:	Optional[Sequence[str]] = None    ,	UpperCamelCase		:	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=UpperCamelCase    ,	remark=UpperCamelCase    ,	parents=UpperCamelCase    ,	parents_chain_index=UpperCamelCase    ,	)
