



'''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


_UpperCamelCase											=			logging.getLogger(__name__)



@dataclass
class _A  :
		_SCREAMING_SNAKE_CASE					:    str						      =			field(
		    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
		_SCREAMING_SNAKE_CASE					:    Optional[str]						      =			field(
		    default=__SCREAMING_SNAKE_CASE	,				metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
		_SCREAMING_SNAKE_CASE					:    Optional[str]						      =			field(
		    default=__SCREAMING_SNAKE_CASE	,				metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
		_SCREAMING_SNAKE_CASE					:    bool						      =			field(default=__SCREAMING_SNAKE_CASE	,				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.
		_SCREAMING_SNAKE_CASE					:    Optional[str]						      =			field(
		    default=__SCREAMING_SNAKE_CASE	,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,				)


def   lowercase_	(   ):

			"""simple docstring"""





			__UpperCAmelCase   :      List[str]										=     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				,  __UpperCAmelCase				,  __UpperCAmelCase   :      List[str]										=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
			else:
						__UpperCAmelCase				,  __UpperCAmelCase				,  __UpperCAmelCase   :      str										=     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"""			,							lowerCAmelCase__ )

			# 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.

			__UpperCAmelCase   :      List[Any]										=     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			,							)
			__UpperCAmelCase   :      str										=     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=lowerCAmelCase__			,							)
			__UpperCAmelCase   :      Any										=     AutoModelForQuestionAnswering.from_pretrained(
			    model_args.model_name_or_path			,							from_tf=bool(""".ckpt""" in model_args.model_name_or_path )			,							config=lowerCAmelCase__			,							cache_dir=model_args.cache_dir			,							)

			# Get datasets
			__UpperCAmelCase   :      List[str]										=     hasattr(model.config			,							"""lang2id""" )
			__UpperCAmelCase   :      List[Any]										=     (
			    SquadDataset(
			        lowerCAmelCase__			,							tokenizer=lowerCAmelCase__			,							is_language_sensitive=lowerCAmelCase__			,							cache_dir=model_args.cache_dir )
			    if training_args.do_train
			    else None
			)
			__UpperCAmelCase   :      str										=     (
			    SquadDataset(
			        lowerCAmelCase__			,							tokenizer=lowerCAmelCase__			,							mode="""dev"""			,							is_language_sensitive=lowerCAmelCase__			,							cache_dir=model_args.cache_dir			,							)
			    if training_args.do_eval
			    else None
			)

			# Data collator
			__UpperCAmelCase   :      Dict										=     DataCollatorWithPadding(lowerCAmelCase__			,							pad_to_multiple_of=8 ) if training_args.fpaa else None

			# Initialize our Trainer
			__UpperCAmelCase   :      int										=     Trainer(
			    model=lowerCAmelCase__			,							args=lowerCAmelCase__			,							train_dataset=lowerCAmelCase__			,							eval_dataset=lowerCAmelCase__			,							data_collator=lowerCAmelCase__			,							)

			# 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_	(   lowerCAmelCase__	:  Dict ):

			"""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




