


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()






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


