



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								=   logging.getLogger(__name__)



@dataclass
class 						_snake_case :





	"""simple docstring"""



	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.

		_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          =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
		else:
				_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       UpperCAmelCase__	(							__snake_case							)				->	Tuple:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
				main()




import warnings

from .generation import FlaxGenerationMixin



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 warnings.warn(
     '''Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '''
     '''be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'''   ,lowerCamelCase   ,)