
'''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


__SCREAMING_SNAKE_CASE  =			logging.getLogger(__name__)




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







def 				__magic_name__(					):
						# 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.

						UpperCamelCase_    :  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.
												UpperCamelCase_    :  List[Any]				    =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
						else:
												UpperCamelCase_    :  List[Any]				    =    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"""       ,							UpperCAmelCase__  )

						# 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_    :  str				    =    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_    :  Optional[int]				    =    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=UpperCAmelCase__       ,							)
						UpperCamelCase_    :  Tuple				    =    AutoModelForQuestionAnswering.from_pretrained(
						    model_args.model_name_or_path       ,							from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )       ,							config=UpperCAmelCase__       ,							cache_dir=model_args.cache_dir       ,							)

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

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

						# Initialize our Trainer
						UpperCamelCase_    :  str				    =    Trainer(
						    model=UpperCAmelCase__       ,							args=UpperCAmelCase__       ,							train_dataset=UpperCAmelCase__       ,							eval_dataset=UpperCAmelCase__       ,							data_collator=UpperCAmelCase__       ,							)

						# 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 				__magic_name__(					UpperCAmelCase__   :					List[str]  ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()






'''simple docstring'''


from __future__ import annotations

from typing import Any




class 						__snake_case :




		def __init__(     self				:int ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:Any)				-> None:  # noqa: A002






								'''simple docstring'''






								UpperCamelCase_    :  Optional[Any]				    =    item
								UpperCamelCase_    :  int				    =    next







class 						__snake_case :




		def __init__(     self				:Optional[Any])				-> None:






								'''simple docstring'''






								UpperCamelCase_    :  Node | None				    =    None
								UpperCamelCase_    :  Tuple				    =    0




		def   _snake_case  (     self				:List[Any] ,						_SCREAMING_SNAKE_CASE				:Any)				-> None:






								'''simple docstring'''






								UpperCamelCase_    :  List[Any]				    =    Node(_SCREAMING_SNAKE_CASE ,						self.head)
								self.size += 1




		def   _snake_case  (     self				:int)				-> Any:






								'''simple docstring'''






								if self.head is None:
														return None
								else:
														UpperCamelCase_    :  Any				    =    self.head.item
														UpperCamelCase_    :  List[str]				    =    self.head.next
														self.size -= 1
														return item




		def   _snake_case  (     self				:int)				-> bool:






								'''simple docstring'''






								return self.head is None




		def __str__(     self				:Any)				-> str:






								'''simple docstring'''






								if self.is_empty():
														return ""
								else:
														UpperCamelCase_    :  List[str]				    =    self.head
														UpperCamelCase_    :  Tuple				    =    """"""
														UpperCamelCase_    :  list[str]				    =    []
														while iterate:
																				item_list.append(str(iterate.item))
																				UpperCamelCase_    :  Tuple				    =    iterate.next

														UpperCamelCase_    :  Optional[int]				    =    """ --> """.join(_SCREAMING_SNAKE_CASE)

														return item_str





		def __len__(     self				:List[Any])				-> int:






								'''simple docstring'''






								return self.size




