
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   lowerCAmelCase_						(					)				->		str:







	'''simple docstring'''


	__magic_name__		:		int	   =     argparse.ArgumentParser()
	parser.add_argument("--model_ckpt"      ,   type=_snake_case      ,   default="microsoft/unixcoder-base-nine"   )
	parser.add_argument("--num_epochs"      ,   type=_snake_case      ,   default=5   )
	parser.add_argument("--batch_size"      ,   type=_snake_case      ,   default=6   )
	parser.add_argument("--gradient_accumulation_steps"      ,   type=_snake_case      ,   default=1   )
	parser.add_argument("--freeze"      ,   type=_snake_case      ,   default=_snake_case   )
	parser.add_argument("--learning_rate"      ,   type=_snake_case      ,   default=5E-4   )
	parser.add_argument("--seed"      ,   type=_snake_case      ,   default=0   )
	parser.add_argument("--lr_scheduler_type"      ,   type=_snake_case      ,   default="cosine"   )
	parser.add_argument("--num_warmup_steps"      ,   type=_snake_case      ,   default=10   )
	parser.add_argument("--weight_decay"      ,   type=_snake_case      ,   default=0.01   )
	parser.add_argument("--output_dir"      ,   type=_snake_case      ,   default="./results"   )
	return parser.parse_args()


snake_case :		Optional[int]							      =  load("accuracy")






def   lowerCAmelCase_						(					_snake_case      :  Tuple   )				->		int:







	'''simple docstring'''


	__magic_name__   ,    __magic_name__		:		Any	   =     eval_pred
	__magic_name__		:		List[str]	   =     np.argmax(_snake_case      ,   axis=1   )
	return metric.compute(predictions=_snake_case      ,   references=_snake_case   )





class       _snake_case					(     snake_case				):







			def __init__(				self						,   _a		):
				super().__init__()
				__magic_name__		:		Tuple	   =     trainer







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   **_a		):
				if control.should_evaluate:
					__magic_name__		:		Any	   =     deepcopy(_a		)
					self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,   metric_key_prefix="train"		)
					return control_copy







def   lowerCAmelCase_						(					)				->		Any:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     get_args()
	set_seed(args.seed   )

	__magic_name__		:		Union[str, Any]	   =     load_dataset("codeparrot/codecomplex"      ,   split="train"   )
	__magic_name__		:		Union[str, Any]	   =     dataset.train_test_split(test_size=0.2   )
	__magic_name__		:		Tuple	   =     train_test["test"].train_test_split(test_size=0.5   )
	__magic_name__		:		Optional[Any]	   =     DatasetDict(
	    {
	        "train": train_test["train"],
	        "test": test_validation["train"],
	        "valid": test_validation["test"],
	    }   )

	print("Loading tokenizer and model"   )
	__magic_name__		:		Dict	   =     AutoTokenizer.from_pretrained(args.model_ckpt   )
	__magic_name__		:		Union[str, Any]	   =     tokenizer.eos_token
	__magic_name__		:		str	   =     AutoModelForSequenceClassification.from_pretrained(args.model_ckpt      ,   num_labels=7   )
	__magic_name__		:		Tuple	   =     model.config.eos_token_id

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

	__magic_name__		:		str	   =     ClassLabel(num_classes=7      ,   names=list(set(train_test_validation["train"]["complexity"]   )   )   )

	def tokenize(_snake_case      :  List[Any]   ):
		__magic_name__		:		Dict	   =     tokenizer(example["src"]      ,   truncation=_snake_case      ,   max_length=1024   )
		__magic_name__		:		Union[str, Any]	   =     labels.straint(example["complexity"]   )
		return {
		    "input_ids": inputs["input_ids"],
		    "attention_mask": inputs["attention_mask"],
		    "label": label,
		}

	__magic_name__		:		Optional[int]	   =     train_test_validation.map(
	    _snake_case      ,   batched=_snake_case      ,   remove_columns=train_test_validation["train"].column_names      ,   )
	__magic_name__		:		str	   =     DataCollatorWithPadding(tokenizer=_snake_case   )

	__magic_name__		:		Any	   =     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=0.01      ,   metric_for_best_model="accuracy"      ,   run_name="complexity-java"      ,   report_to="wandb"      ,   )

	__magic_name__		:		Dict	   =     Trainer(
	    model=_snake_case      ,   args=_snake_case      ,   train_dataset=tokenized_datasets["train"]      ,   eval_dataset=tokenized_datasets["valid"]      ,   tokenizer=_snake_case      ,   data_collator=_snake_case      ,   compute_metrics=_snake_case      ,   )

	print("Training..."   )
	trainer.add_callback(CustomCallback(_snake_case   )   )
	trainer.train()


if __name__ == "__main__":
				main()


import math






def   lowerCAmelCase_						(					_snake_case      :  float      ,   _snake_case      :  float   )				->		float:







	'''simple docstring'''


	return math.pow(_snake_case      ,   2   ) - a






def   lowerCAmelCase_						(					_snake_case      :  float   )				->		float:







	'''simple docstring'''


	return 2 * x






def   lowerCAmelCase_						(					_snake_case      :  float   )				->		float:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     2.0

	while start <= a:
		__magic_name__		:		str	   =     math.pow(_snake_case      ,   2   )

	return start






def   lowerCAmelCase_						(					_snake_case      :  float      ,   _snake_case      :  int = 9999      ,   _snake_case      :  float = 0.00_000_000_000_001   )				->		float:







	'''simple docstring'''


	if a < 0:
		raise ValueError("math domain error"   )

	__magic_name__		:		Optional[int]	   =     get_initial_point(_snake_case   )

	for _ in range(_snake_case   ):
		__magic_name__		:		int	   =     value
		__magic_name__		:		str	   =     value - fx(_snake_case      ,   _snake_case   ) / fx_derivative(_snake_case   )
		if abs(prev_value - value   ) < tolerance:
			return value

	return value


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

				testmod()
