code: 
  
  
  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
  
  
  lowercase_								=    logging.getLogger(__name__)
  
  
  
  
  @dataclass
  class 					_UpperCamelCase :
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  field(
  							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}	)
  							_A  				=  field(
  							    default=lowerCamelCase__			,							metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
  							_A  				=  field(
  							    default=lowerCamelCase__			,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
  							_A  				=  field(default=lowerCamelCase__			,							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.
  							_A  				=  field(
  							    default=lowerCamelCase__			,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,							)
  
  
  
  
  def 							SCREAMING_SNAKE_CASE					(  )    ->  Dict:
  						# 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.
  
  						_a     =			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 ,	_a ,	_a     =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
  						else:
  												_a ,	_a ,	_a     =			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     =			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     =			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     =			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     =			hasattr(model.config							,     'lang2id'				)
  						_a     =			(
  						    SquadDataset(
  						        _UpperCAmelCase							,     tokenizer=_UpperCAmelCase							,     is_language_sensitive=_UpperCAmelCase							,     cache_dir=model_args.cache_dir				)
  						    if training_args.do_train
  						    else None
  						)
  						_a     =			(
  						    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     =			DataCollatorWithPadding(_UpperCAmelCase							,     pad_to_multiple_of=8				) if training_args.fpaa else None
  
  						# Initialize our Trainer
  						_a     =			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 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[Any]:
  						# For xla_spawn (TPUs)
  						main()
  
  
  if __name__ == "__main__":
  		main()
  
  
  
style_context: 
  
  
  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
  
  
  