




"""simple docstring"""

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_lowerCamelCase					:     Union[str, Any]				       =    logging.getLogger(__name__)

@dataclass
class     lowercase :
				__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=__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"""}       ,		)

def 						lowercase_ ( ):




	"""simple docstring"""






	A_					:			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.
		A_					:			Union[str, Any]						=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
	else:
		A_					:			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.'''		)

	# 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		)

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

	A_					:			List[str]						=						AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path					, cache_dir=model_args.cache_dir					, )
	A_					:			Optional[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=_UpperCAmelCase					, )
	A_					:			Optional[Any]						=						AutoModelForQuestionAnswering.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
	A_					:			str						=						hasattr(model.config					, '''lang2id'''		)
	A_					:			List[Any]						=						(
	    SquadDataset(
	        _UpperCAmelCase					, tokenizer=_UpperCAmelCase					, is_language_sensitive=_UpperCAmelCase					, cache_dir=model_args.cache_dir		)
	    if training_args.do_train
	    else None
	)
	A_					:			Dict						=						(
	    SquadDataset(
	        _UpperCAmelCase					, tokenizer=_UpperCAmelCase					, mode='''dev'''					, is_language_sensitive=_UpperCAmelCase					, cache_dir=model_args.cache_dir					, )
	    if training_args.do_eval
	    else None
	)

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

	# Initialize our Trainer
	A_					:			Tuple						=						Trainer(
	    model=_UpperCAmelCase					, args=_UpperCAmelCase					, train_dataset=_UpperCAmelCase					, eval_dataset=_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_master():
			tokenizer.save_pretrained(training_args.output_dir		)

def 						lowercase_ ( _UpperCAmelCase		):




	"""simple docstring"""






	main()


if __name__ == "__main__":
		main()











"""simple docstring"""

from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims





