

'''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      :    str						       =    logging.getLogger(__name__)






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






def  UpperCamelCase_(							):




			"""simple docstring"""






			__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   ,					__a   ,					__a						   =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
			else:
						__a   ,					__a   ,					__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' ,  _snake_case			)

			# 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=_snake_case ,  )
			__a						   =AutoModelForQuestionAnswering.from_pretrained(
			    model_args.model_name_or_path ,  from_tf=bool('.ckpt' in model_args.model_name_or_path			) ,  config=_snake_case ,  cache_dir=model_args.cache_dir ,  )

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

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

			# Initialize our Trainer
			__a						   =Trainer(
			    model=_snake_case ,  args=_snake_case ,  train_dataset=_snake_case ,  eval_dataset=_snake_case ,  data_collator=_snake_case ,  )

			# 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     :						Any			):




			"""simple docstring"""






			main()


if __name__ == "__main__":
						main()









from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


_lowerCAmelCase      :    List[Any]						       =    [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

_lowerCAmelCase      :    List[str]						       =    get_logger(__name__)


_lowerCAmelCase      :    Tuple						       =    {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}






def  UpperCamelCase_(							_snake_case     :						dict			):




			"""simple docstring"""






			__a						   =task_template_dict.get('task'			)
			if task_name is None:
						logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(_snake_case			)}'			)
						return None
			__a						   =NAMEaTEMPLATE.get(_snake_case			)
			return template.from_dict(_snake_case			)






