



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


_lowercase	:   Dict    	=logging.getLogger(__name__)

@dataclass
class      snake_case__       :







						"""simple docstring"""




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




def 			lowerCAmelCase_  (						)							-> Dict:







			"""simple docstring"""
			# 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.

			a__     :		List[str]		 =						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__     :		Dict		 =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
			else:
						a__ ,		a__ ,		a__     :		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.

			a__     :		Dict		 =						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__     :		List[str]		 =						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		,     )
			a__     :		Optional[Any]		 =						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
			a__     :		Dict		 =						hasattr(model.config		,     """lang2id""")
			a__     :		Optional[int]		 =						(
			    SquadDataset(
			        _UpperCAmelCase		,     tokenizer=_UpperCAmelCase		,     is_language_sensitive=_UpperCAmelCase		,     cache_dir=model_args.cache_dir)
			    if training_args.do_train
			    else None
			)
			a__     :		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
			a__     :		Dict		 =						DataCollatorWithPadding(_UpperCAmelCase		,     pad_to_multiple_of=8) if training_args.fpaa else None

			# Initialize our Trainer
			a__     :		Union[str, Any]		 =						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 			lowerCAmelCase_  (						_lowercase       :     Union[str, Any])							-> Optional[Any]:







			"""simple docstring"""
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
	main()









from collections.abc import Callable

_lowercase	:   int    	=Callable[[float], float]  # type alias for a real -> real function

def 			lowerCAmelCase_  (						_lowercase       :     RealFunc		,     _lowercase       :     RealFunc		,     _lowercase       :     int		,     )							-> float:







			"""simple docstring"""
			a__     :		Optional[int]		 =						float(_lowercase)
			while True:
						try:
									a__     :		Union[str, Any]		 =						prev_guess - function(_lowercase) / derivative(_lowercase)
						except ZeroDivisionError:
									raise ZeroDivisionError("""Could not find root""") from None
						if abs(prev_guess - next_guess) < 10**-5:
									return next_guess
						a__     :		Optional[Any]		 =						next_guess

def 			lowerCAmelCase_  (						_lowercase       :     float)							-> float:







			"""simple docstring"""
			return (x**3) - (2 * x) - 5

def 			lowerCAmelCase_  (						_lowercase       :     float)							-> float:







			"""simple docstring"""
			return 3 * (x**2) - 2


if __name__ == "__main__":
		print(newton(f, fa, 3))




