


"""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


_lowerCamelCase  =	logging.getLogger(__name__)


@dataclass
class       lowerCamelCase_		:





				"""simple docstring"""







				_lowerCAmelCase   :   str   				=       field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
				_lowerCAmelCase   :   Optional[str]   				=       field(
				    default=lowerCAmelCase_							,	metadata={"help": "Pretrained config name or path if not the same as model_name"} )
				_lowerCAmelCase   :   Optional[str]   				=       field(
				    default=lowerCAmelCase_							,	metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
				_lowerCAmelCase   :   bool   				=       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   :   Optional[str]   				=       field(
				    default=lowerCAmelCase_							,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,	)


def __lowercase  (   ):
				# 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.

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

				SCREAMING_SNAKE_CASE__  				=					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    ,       )
				SCREAMING_SNAKE_CASE__  				=					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_    ,       )
				SCREAMING_SNAKE_CASE__  				=					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
				SCREAMING_SNAKE_CASE__  				=					hasattr(model.config    ,       "lang2id"	)
				SCREAMING_SNAKE_CASE__  				=					(
				    SquadDataset(
				        UpperCamelCase_    ,       tokenizer=UpperCamelCase_    ,       is_language_sensitive=UpperCamelCase_    ,       cache_dir=model_args.cache_dir	)
				    if training_args.do_train
				    else None
				)
				SCREAMING_SNAKE_CASE__  				=					(
				    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
				SCREAMING_SNAKE_CASE__  				=					DataCollatorWithPadding(UpperCamelCase_    ,       pad_to_multiple_of=8	) if training_args.fpaa else None

				# Initialize our Trainer
				SCREAMING_SNAKE_CASE__  				=					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 __lowercase  (   lowerCamelCase_	:   Tuple	):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
	main()







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}



class 				_UpperCamelCase       (						lowerCAmelCase_  ):
       _UpperCamelCase					:       Union[str, Any]       = '''qdqbert'''

       def __init__(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			Any=30522       , _SCREAMING_SNAKE_CASE:			List[str]=768       , _SCREAMING_SNAKE_CASE:			int=12       , _SCREAMING_SNAKE_CASE:			List[Any]=12       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=3072       , _SCREAMING_SNAKE_CASE:			List[str]="gelu"       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Dict=512       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=2       , _SCREAMING_SNAKE_CASE:			int=0.02       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=1e-12       , _SCREAMING_SNAKE_CASE:			str=True       , _SCREAMING_SNAKE_CASE:			Any=1       , _SCREAMING_SNAKE_CASE:			int=0       , _SCREAMING_SNAKE_CASE:			Tuple=2       , **_SCREAMING_SNAKE_CASE:			List[str]       , )							->			Tuple:




              """simple docstring"""


              super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       , bos_token_id=_SCREAMING_SNAKE_CASE       , eos_token_id=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	vocab_size
              UpperCamelCase_ 							=	max_position_embeddings
              UpperCamelCase_ 							=	hidden_size
              UpperCamelCase_ 							=	num_hidden_layers
              UpperCamelCase_ 							=	num_attention_heads
              UpperCamelCase_ 							=	intermediate_size
              UpperCamelCase_ 							=	hidden_act
              UpperCamelCase_ 							=	hidden_dropout_prob
              UpperCamelCase_ 							=	attention_probs_dropout_prob
              UpperCamelCase_ 							=	initializer_range
              UpperCamelCase_ 							=	type_vocab_size
              UpperCamelCase_ 							=	layer_norm_eps
              UpperCamelCase_ 							=	use_cache

