


"""simple docstring"""


import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)







def  __snake_case       (   )      ->		str:







		"""simple docstring"""





		A					       = argparse.ArgumentParser()
		parser.add_argument('--model_ckpt' ,			type=UpperCamelCase__ ,			default='microsoft/unixcoder-base-nine'	)
		parser.add_argument('--num_epochs' ,			type=UpperCamelCase__ ,			default=5	)
		parser.add_argument('--batch_size' ,			type=UpperCamelCase__ ,			default=6	)
		parser.add_argument('--gradient_accumulation_steps' ,			type=UpperCamelCase__ ,			default=1	)
		parser.add_argument('--freeze' ,			type=UpperCamelCase__ ,			default=UpperCamelCase__	)
		parser.add_argument('--learning_rate' ,			type=UpperCamelCase__ ,			default=5E-4	)
		parser.add_argument('--seed' ,			type=UpperCamelCase__ ,			default=0	)
		parser.add_argument('--lr_scheduler_type' ,			type=UpperCamelCase__ ,			default='cosine'	)
		parser.add_argument('--num_warmup_steps' ,			type=UpperCamelCase__ ,			default=10	)
		parser.add_argument('--weight_decay' ,			type=UpperCamelCase__ ,			default=0.0_1	)
		parser.add_argument('--output_dir' ,			type=UpperCamelCase__ ,			default='./results'	)
		return parser.parse_args()


UpperCamelCase						:				Dict             =      load("accuracy")







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		A       , A					       = eval_pred
		A					       = np.argmax(UpperCamelCase__ ,			axis=1	)
		return metric.compute(predictions=UpperCamelCase__ ,			references=UpperCamelCase__	)



class 			lowerCamelCase__						(   UpperCAmelCase_			):







						def __init__(							self   :    Tuple			,			_lowercase   :    Union[str, Any]		):
								super().__init__()
								A					       = trainer







						def __a						(							self   :    List[Any]			,			_lowercase   :    Optional[int]			,			_lowercase   :    str			,			_lowercase   :    Any			,			**_lowercase   :    str		):
								if control.should_evaluate:
										A					       = deepcopy(_lowercase		)
										self._trainer.evaluate(eval_dataset=self._trainer.train_dataset			,			metric_key_prefix='train'		)
										return control_copy







def  __snake_case       (   )      ->		int:







		"""simple docstring"""





		A					       = get_args()
		set_seed(args.seed	)

		A					       = load_dataset('codeparrot/codecomplex' ,			split='train'	)
		A					       = dataset.train_test_split(test_size=0.2	)
		A					       = train_test['test'].train_test_split(test_size=0.5	)
		A					       = DatasetDict(
		    {
		        'train': train_test['train'],
		        'test': test_validation['train'],
		        'valid': test_validation['test'],
		    }	)

		print('Loading tokenizer and model'	)
		A					       = AutoTokenizer.from_pretrained(args.model_ckpt	)
		A					       = tokenizer.eos_token
		A					       = AutoModelForSequenceClassification.from_pretrained(args.model_ckpt ,			num_labels=7	)
		A					       = model.config.eos_token_id

		if args.freeze:
				for param in model.roberta.parameters():
						A					       = False

		A					       = ClassLabel(num_classes=7 ,			names=list(set(train_test_validation['train']['complexity']	)	)	)

		def tokenize(UpperCamelCase__	):
				A					       = tokenizer(example['src'] ,			truncation=UpperCamelCase__ ,			max_length=1024	)
				A					       = labels.straint(example['complexity']	)
				return {
				    "input_ids": inputs["input_ids"],
				    "attention_mask": inputs["attention_mask"],
				    "label": label,
				}

		A					       = train_test_validation.map(
		    UpperCamelCase__ ,			batched=UpperCamelCase__ ,			remove_columns=train_test_validation['train'].column_names ,			)
		A					       = DataCollatorWithPadding(tokenizer=UpperCamelCase__	)

		A					       = TrainingArguments(
		    output_dir=args.output_dir ,			learning_rate=args.learning_rate ,			lr_scheduler_type=args.lr_scheduler_type ,			evaluation_strategy='epoch' ,			save_strategy='epoch' ,			logging_strategy='epoch' ,			per_device_train_batch_size=args.batch_size ,			per_device_eval_batch_size=args.batch_size ,			num_train_epochs=args.num_epochs ,			gradient_accumulation_steps=args.gradient_accumulation_steps ,			weight_decay=0.0_1 ,			metric_for_best_model='accuracy' ,			run_name='complexity-java' ,			report_to='wandb' ,			)

		A					       = Trainer(
		    model=UpperCamelCase__ ,			args=UpperCamelCase__ ,			train_dataset=tokenized_datasets['train'] ,			eval_dataset=tokenized_datasets['valid'] ,			tokenizer=UpperCamelCase__ ,			data_collator=UpperCamelCase__ ,			compute_metrics=UpperCamelCase__ ,			)

		print('Training...'	)
		trainer.add_callback(CustomCallback(UpperCamelCase__	)	)
		trainer.train()


if __name__ == "__main__":
	main()









"""simple docstring"""


from math import factorial







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		float:







		"""simple docstring"""





		if successes > trials:
				raise ValueError('successes must be lower or equal to trials'	)
		if trials < 0 or successes < 0:
				raise ValueError('the function is defined for non-negative integers'	)
		if not isinstance(UpperCamelCase__ ,			UpperCamelCase__	) or not isinstance(UpperCamelCase__ ,			UpperCamelCase__	):
				raise ValueError('the function is defined for non-negative integers'	)
		if not 0 < prob < 1:
				raise ValueError('prob has to be in range of 1 - 0'	)
		A					       = (prob**successes) * ((1 - prob) ** (trials - successes))
		# Calculate the binomial coefficient: n! / k!(n-k)!
		A					       = float(factorial(UpperCamelCase__	)	)
		coefficient /= factorial(UpperCamelCase__	) * factorial(trials - successes	)
		return probability * coefficient


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	print("Probability of 2 successes out of 4 trails")
	print("with probability of 0.75 is:", end=" ")
	print(binomial_distribution(2, 4, 0.75))





