


"""simple docstring"""







import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a__       :							List[str]        =  logging.getLogger(__name__)




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					return (preds == labels).mean()




@dataclass
class 				UpperCamelCase_			:

	"""simple docstring"""





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




@dataclass
class 				UpperCamelCase_			:

	"""simple docstring"""





	snake_case__       :						str					      =    field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys())})
	snake_case__       :						str					      =    field(metadata={"help": "Should contain the data files for the task."})
	snake_case__       :						int					      =    field(
	    default=128    ,						metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Overwrite the cached training and evaluation sets"})




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
					__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"						,		lowerCAmelCase_     )

					# Set seed
					set_seed(training_args.seed     )

					try:
										__SCREAMING_SNAKE_CASE		     =       processors[data_args.task_name]()
										__SCREAMING_SNAKE_CASE		     =       processor.get_labels()
										__SCREAMING_SNAKE_CASE		     =       len(lowerCAmelCase_     )
					except KeyError:
										raise ValueError("Task not found: %s" % (data_args.task_name)     )

					# 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						,		num_labels=lowerCAmelCase_						,		finetuning_task=data_args.task_name						,		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						,		)
					__SCREAMING_SNAKE_CASE		     =       AutoModelForMultipleChoice.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
					__SCREAMING_SNAKE_CASE		     =       (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir						,		tokenizer=lowerCAmelCase_						,		task=data_args.task_name						,		max_seq_length=data_args.max_seq_length						,		overwrite_cache=data_args.overwrite_cache						,		mode=Split.train						,		)
					    if training_args.do_train
					    else None
					)
					__SCREAMING_SNAKE_CASE		     =       (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir						,		tokenizer=lowerCAmelCase_						,		task=data_args.task_name						,		max_seq_length=data_args.max_seq_length						,		overwrite_cache=data_args.overwrite_cache						,		mode=Split.dev						,		)
					    if training_args.do_eval
					    else None
					)

					def compute_metrics(lowerCAmelCase_     ) -> Dict:
										__SCREAMING_SNAKE_CASE		     =       np.argmax(p.predictions						,		axis=1     )
										return {"acc": simple_accuracy(lowerCAmelCase_						,		p.label_ids     )}

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

					# Initialize our Trainer
					__SCREAMING_SNAKE_CASE		     =       Trainer(
					    model=lowerCAmelCase_						,		args=lowerCAmelCase_						,		train_dataset=lowerCAmelCase_						,		eval_dataset=lowerCAmelCase_						,		compute_metrics=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     )

    # Evaluation
					__SCREAMING_SNAKE_CASE		     =       {}
					if training_args.do_eval:
										logger.info("*** Evaluate ***"     )

										__SCREAMING_SNAKE_CASE		     =       trainer.evaluate()

										__SCREAMING_SNAKE_CASE		     =       os.path.join(training_args.output_dir						,		"eval_results.txt"     )
										if trainer.is_world_master():
															with open(lowerCAmelCase_						,		"w"     ) as writer:
																				logger.info("***** Eval results *****"     )
																				for key, value in result.items():
																									logger.info("  %s = %s"						,		lowerCAmelCase_						,		lowerCAmelCase_     )
																									writer.write("%s = %s\n" % (key, value)     )

																				results.update(lowerCAmelCase_     )

					return results




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					main()


if __name__ == "__main__":
		main()




"""simple docstring"""







from math import asin, atan, cos, radians, sin, sqrt, tan

a__       :							List[str]        =  6_37_81_37.0
a__       :							Tuple        =  6_35_67_52.31_42_45
a__       :							str        =  6_3_7_8_1_3_7




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       (AXIS_A - AXIS_B) / AXIS_A
					__SCREAMING_SNAKE_CASE		     =       atan((1 - flattening) * tan(radians(lowerCAmelCase_     )     )     )
					__SCREAMING_SNAKE_CASE		     =       atan((1 - flattening) * tan(radians(lowerCAmelCase_     )     )     )
					__SCREAMING_SNAKE_CASE		     =       radians(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       radians(lowerCAmelCase_     )
					# Equation
					__SCREAMING_SNAKE_CASE		     =       sin((phi_a - phi_a) / 2     )
					__SCREAMING_SNAKE_CASE		     =       sin((lambda_a - lambda_a) / 2     )
					# Square both values
					sin_sq_phi *= sin_sq_phi
					sin_sq_lambda *= sin_sq_lambda
					__SCREAMING_SNAKE_CASE		     =       sqrt(sin_sq_phi + (cos(lowerCAmelCase_     ) * cos(lowerCAmelCase_     ) * sin_sq_lambda)     )
					return 2 * RADIUS * asin(lowerCAmelCase_     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
