


'''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


_UpperCAmelCase					:			Union[str, Any]				     =					logging.getLogger(__name__)


@dataclass
class    a__					:







				"""simple docstring"""



				__UpperCamelCase     :		str						=						field(
				    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}   )
				__UpperCamelCase     :		Optional[str]						=						field(
				    default=__A				,    metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
				__UpperCamelCase     :		Optional[str]						=						field(
				    default=__A				,    metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}   )
				__UpperCamelCase     :		bool						=						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.
				__UpperCamelCase     :		Optional[str]						=						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.

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

						# 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.

						__lowerCAmelCase											=					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,					)
						__lowerCAmelCase											=					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=lowerCamelCase,					)
						__lowerCAmelCase											=					AutoModelForQuestionAnswering.from_pretrained(
						    model_args.model_name_or_path,					from_tf=bool('''.ckpt''' in model_args.model_name_or_path),					config=lowerCamelCase,					cache_dir=model_args.cache_dir,					)

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

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

						# Initialize our Trainer
						__lowerCAmelCase											=					Trainer(
						    model=lowerCamelCase,					args=lowerCamelCase,					train_dataset=lowerCamelCase,					eval_dataset=lowerCamelCase,					data_collator=lowerCamelCase,					)

						# 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__(    lowerCamelCase):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
	main()





'''simple docstring'''

import math

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					math.log(2,					1_0) * number
						__lowerCAmelCase											=					round((log_number - int(lowerCamelCase)),					1_5)

						return difference

def 				__magic_name__(    lowerCamelCase = 6_7_8_9_1_0):
						__lowerCAmelCase											=					9_0
						__lowerCAmelCase											=					0

						__lowerCAmelCase											=					math.log(1.23,					1_0)
						__lowerCAmelCase											=					math.log(1.24,					1_0)
						__lowerCAmelCase											=					0

						while position < number:
												__lowerCAmelCase											=					log_difference(lowerCamelCase)

												if difference >= upper_limit:
																		power_iterator += 9_3

												elif difference < lower_limit:
																		power_iterator += 1_9_6

												else:
																		__lowerCAmelCase											=					power_iterator
																		power_iterator += 1_9_6
																		position += 1

						return previous_power


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(f"""{solution() = }""")

