



'''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


_lowercase:				List[str]       =	logging.getLogger(__name__)



@dataclass
class 			_lowercase							:





	"""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 					a(   )	-> Optional[Any]:
					"""simple docstring"""





					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"				,  A )

					# 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=A				,  )
					a        =     AutoModelForQuestionAnswering.from_pretrained(
					    model_args.model_name_or_path				,  from_tf=bool(".ckpt" in model_args.model_name_or_path )				,  config=A				,  cache_dir=model_args.cache_dir				,  )

					# Get datasets
					a        =     hasattr(model.config				,  "lang2id" )
					a        =     (
					    SquadDataset(
					        A				,  tokenizer=A				,  is_language_sensitive=A				,  cache_dir=model_args.cache_dir )
					    if training_args.do_train
					    else None
					)
					a        =     (
					    SquadDataset(
					        A				,  tokenizer=A				,  mode="dev"				,  is_language_sensitive=A				,  cache_dir=model_args.cache_dir				,  )
					    if training_args.do_eval
					    else None
					)

					# Data collator
					a        =     DataCollatorWithPadding(A				,  pad_to_multiple_of=8 ) if training_args.fpaa else None

					# Initialize our Trainer
					a        =     Trainer(
					    model=A				,  args=A				,  train_dataset=A				,  eval_dataset=A				,  data_collator=A				,  )

					# 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 					a(   A :					Any )	-> Dict:
					"""simple docstring"""





					main()


if __name__ == "__main__":
				main()





# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls




	@slow
	@require_torch_gpu
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.get_auto_remove_tmp_dir()
						a        =     F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

						# we want this test to run even if there is only one GPU, but if there are more we use them all
						a        =     get_gpu_count()
						a        =     F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
						a        =     [sys.executable] + distributed_args + args
						execute_subprocess_async(lowerCamelCase_	, env=self.get_env()	)

						a        =     os.path.join(lowerCamelCase_	, "test_bleu.json"	)
						a        =     load_json(lowerCamelCase_	)
						# print(metrics)
						self.assertGreaterEqual(metrics["bleu"]	, 25	)
