from ..utils import DummyObject, requires_backends


lowerCAmelCase__					:       str		=       None
def 			UpperCamelCase__						(    *A__      ,			**A__   )       -> Tuple:
				requires_backends(A__      ,			['sentencepiece', 'tokenizers']   )



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__					:       Tuple		=       logging.getLogger(__name__)





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




def 			UpperCamelCase__						(    )       -> Optional[Any]:
				# 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.

				snake_case__ :     Optional[Any] 							=				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.
								snake_case__							,  snake_case__							,  snake_case__ :     Tuple 							=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
				else:
								snake_case__							,  snake_case__							,  snake_case__ :     Optional[int] 							=				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.

				snake_case__ :     Optional[int] 							=				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      ,			)
				snake_case__ :     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=A__      ,			)
				snake_case__ :     Optional[int] 							=				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
				snake_case__ :     Dict 							=				hasattr(model.config      ,			'lang2id'   )
				snake_case__ :     List[str] 							=				(
				    SquadDataset(
				        A__      ,			tokenizer=A__      ,			is_language_sensitive=A__      ,			cache_dir=model_args.cache_dir   )
				    if training_args.do_train
				    else None
				)
				snake_case__ :     str 							=				(
				    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
				snake_case__ :     Dict 							=				DataCollatorWithPadding(A__      ,			pad_to_multiple_of=8   ) if training_args.fpaa else None

				# Initialize our Trainer
				snake_case__ :     str 							=				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 			UpperCamelCase__						(    A__   )       -> Tuple:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
				main()


