


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


A :	Tuple												=     logging.getLogger(__name__)



@dataclass
class 							A					:
					'''simple docstring'''






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






def 				UpperCamelCase      (			)     ->						int:
		"""simple docstring"""




		lowercase__      =	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.
				lowercase__    ,			lowercase__    ,			lowercase__      =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
		else:
				lowercase__    ,			lowercase__    ,			lowercase__      =	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"""					,					__magic_name__  )

		# 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.

		lowercase__      =	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					,					)
		lowercase__      =	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=__magic_name__					,					)
		lowercase__      =	AutoModelForQuestionAnswering.from_pretrained(
		    model_args.model_name_or_path					,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )					,					config=__magic_name__					,					cache_dir=model_args.cache_dir					,					)

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

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

		# Initialize our Trainer
		lowercase__      =	Trainer(
		    model=__magic_name__					,					args=__magic_name__					,					train_dataset=__magic_name__					,					eval_dataset=__magic_name__					,					data_collator=__magic_name__					,					)

		# 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      (			__magic_name__  :							List[Any]  )     ->						Optional[Any]:
		"""simple docstring"""




		main()


if __name__ == "__main__":
	main()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A :	str												=     {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A :	Optional[int]												=     [
	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'NystromformerForMaskedLM',
	    'NystromformerForMultipleChoice',
	    'NystromformerForQuestionAnswering',
	    'NystromformerForSequenceClassification',
	    'NystromformerForTokenClassification',
	    'NystromformerLayer',
	    'NystromformerModel',
	    'NystromformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nystromformer import (
		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NystromformerForMaskedLM,
		    NystromformerForMultipleChoice,
		    NystromformerForQuestionAnswering,
		    NystromformerForSequenceClassification,
		    NystromformerForTokenClassification,
		    NystromformerLayer,
		    NystromformerModel,
		    NystromformerPreTrainedModel,
		)


else:
	import sys

	A :	Optional[int]												=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

