


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


snake_case__  				=			logging.getLogger(__name__)



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






def 			__SCREAMING_SNAKE_CASE							(					)							->	Optional[Any]:
						# 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.

						__magic_name__					:				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.
												__magic_name__		,			__magic_name__		,			__magic_name__					:				Any				=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
						else:
												__magic_name__		,			__magic_name__		,			__magic_name__					:				str				=   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'   ,    lowercase__					)

						# 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.

						__magic_name__					:				str				=   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   ,    )
						__magic_name__					:				Tuple				=   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=lowercase__   ,    )
						__magic_name__					:				List[Any]				=   AutoModelForQuestionAnswering.from_pretrained(
						    model_args.model_name_or_path   ,    from_tf=bool('.ckpt' in model_args.model_name_or_path					)   ,    config=lowercase__   ,    cache_dir=model_args.cache_dir   ,    )

						# Get datasets
						__magic_name__					:				Optional[int]				=   hasattr(model.config   ,    'lang2id'					)
						__magic_name__					:				Any				=   (
						    SquadDataset(
						        lowercase__   ,    tokenizer=lowercase__   ,    is_language_sensitive=lowercase__   ,    cache_dir=model_args.cache_dir					)
						    if training_args.do_train
						    else None
						)
						__magic_name__					:				Any				=   (
						    SquadDataset(
						        lowercase__   ,    tokenizer=lowercase__   ,    mode='dev'   ,    is_language_sensitive=lowercase__   ,    cache_dir=model_args.cache_dir   ,    )
						    if training_args.do_eval
						    else None
						)

						# Data collator
						__magic_name__					:				int				=   DataCollatorWithPadding(lowercase__   ,    pad_to_multiple_of=8					) if training_args.fpaa else None

						# Initialize our Trainer
						__magic_name__					:				Union[str, Any]				=   Trainer(
						    model=lowercase__   ,    args=lowercase__   ,    train_dataset=lowercase__   ,    eval_dataset=lowercase__   ,    data_collator=lowercase__   ,    )

						# 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							(					lowercase__	:Any					)							->	str:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
			main()






from __future__ import annotations

from collections.abc import Callable
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Callable[[int | float], int | float]   ,    lowercase__	:int | float   ,    lowercase__	:int | float   ,    lowercase__	:int = 1_0_0   ,    )							->	float:
						__magic_name__					:				str				=   x_start
						__magic_name__					:				str				=   fnc(lowercase__					)
						__magic_name__					:				Union[str, Any]				=   0.0

						for _ in range(lowercase__					):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												__magic_name__					:				Optional[int]				=   (x_end - x_start) / steps + xa
												__magic_name__					:				List[Any]				=   fnc(lowercase__					)
												area += abs(fxa + fxa					) * (xa - xa) / 2

												# Increment step
												__magic_name__					:				Optional[Any]				=   xa
												__magic_name__					:				Union[str, Any]				=   fxa
						return area


if __name__ == "__main__":
			def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Dict					)							->	Optional[int]:
									return x**3

			print('f(x) = x^3')
			print('The area between the curve, x = -10, x = 10 and the x axis is:')
			snake_case__  				=			10
			while i <= 10_0000:
						snake_case__  				=			trapezoidal_area(f, -5, 5, i)
						print(F"""with {i} steps: {area}""")
						i *= 10


