





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






def 			SCREAMING_SNAKE_CASE__							(    ):
		# 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 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Optional[int]      ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
	main()






from __future__ import annotations






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							list[int | float]      ):
		if len(UpperCamelCase__      ) == 0:
				raise ValueError("""find_min() arg is an empty sequence"""      )
		SCREAMING_SNAKE_CASE__								= nums[0]
		for num in nums:
				SCREAMING_SNAKE_CASE__								= min(UpperCamelCase__   ,				UpperCamelCase__      )
		return min_num


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)