



import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
						UpperCamelCase__:     List[str]										=    "%20".join(argv[1:]) if len(argv) > 1 else quote(str(input("Search: ")))

						print("Googling.....")

						UpperCamelCase__:     str										=    F"https://www.google.com/search?q={query}&num=100"

						UpperCamelCase__:     Union[str, Any]										=    requests.get(
						    url,
						    headers={"User-Agent": str(UserAgent().random)},
						)

						try:
												UpperCamelCase__:     Dict										=    (
												    BeautifulSoup(res.text, "html.parser")
												    .find("div", attrs={"class": "yuRUbf"})
												    .find("a")
												    .get("href")
												)

						except AttributeError:
												UpperCamelCase__:     Tuple										=    parse_qs(
												    BeautifulSoup(res.text, "html.parser")
												    .find("div", attrs={"class": "kCrYT"})
												    .find("a")
												    .get("href")
												)["url"][0]

						webbrowser.open(link)










'''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_  (      )   ->     Optional[int]:
							UpperCAmelCase				: Any											=	argparse.ArgumentParser()
							parser.add_argument('''--model_ckpt'''     ,   type=_lowerCAmelCase     ,   default='''microsoft/unixcoder-base-nine'''						)
							parser.add_argument('''--num_epochs'''     ,   type=_lowerCAmelCase     ,   default=5						)
							parser.add_argument('''--batch_size'''     ,   type=_lowerCAmelCase     ,   default=6						)
							parser.add_argument('''--gradient_accumulation_steps'''     ,   type=_lowerCAmelCase     ,   default=1						)
							parser.add_argument('''--freeze'''     ,   type=_lowerCAmelCase     ,   default=_lowerCAmelCase						)
							parser.add_argument('''--learning_rate'''     ,   type=_lowerCAmelCase     ,   default=5e-4						)
							parser.add_argument('''--seed'''     ,   type=_lowerCAmelCase     ,   default=0						)
							parser.add_argument('''--lr_scheduler_type'''     ,   type=_lowerCAmelCase     ,   default='''cosine'''						)
							parser.add_argument('''--num_warmup_steps'''     ,   type=_lowerCAmelCase     ,   default=10						)
							parser.add_argument('''--weight_decay'''     ,   type=_lowerCAmelCase     ,   default=0.0_1						)
							parser.add_argument('''--output_dir'''     ,   type=_lowerCAmelCase     ,   default='''./results'''						)
							return parser.parse_args()


UpperCamelCase__:     Any										=    load("accuracy")


def 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     int:
							UpperCAmelCase							,	UpperCAmelCase				: Dict											=	eval_pred
							UpperCAmelCase				: Optional[Any]											=	np.argmax(_lowerCAmelCase     ,   axis=1						)
							return metric.compute(predictions=_lowerCAmelCase     ,   references=_lowerCAmelCase						)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  List[Any]	,    __snake_case    :  Optional[int]	)    ->	None:
								super().__init__()
								UpperCAmelCase				: Dict											=	trainer







	def   A			(		self    :  str	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  int	,    __snake_case    :  Any	,    **__snake_case    :  List[Any]	)    ->	Dict:
								if control.should_evaluate:
															UpperCAmelCase				: Optional[int]											=	deepcopy(__snake_case	)
															self._trainer.evaluate(eval_dataset=self._trainer.train_dataset	,    metric_key_prefix='''train'''	)
															return control_copy


def 				snake_case_  (      )   ->     List[Any]:
							UpperCAmelCase				: Tuple											=	get_args()
							set_seed(args.seed						)

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

							print('''Loading tokenizer and model'''						)
							UpperCAmelCase				: Tuple											=	AutoTokenizer.from_pretrained(args.model_ckpt						)
							UpperCAmelCase				: Optional[int]											=	tokenizer.eos_token
							UpperCAmelCase				: Optional[int]											=	AutoModelForSequenceClassification.from_pretrained(args.model_ckpt     ,   num_labels=7						)
							UpperCAmelCase				: List[Any]											=	model.config.eos_token_id

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

							UpperCAmelCase				: Optional[Any]											=	ClassLabel(num_classes=7     ,   names=list(set(train_test_validation['''train''']['''complexity''']						)						)						)

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

							UpperCAmelCase				: List[str]											=	train_test_validation.map(
							    _lowerCAmelCase     ,   batched=_lowerCAmelCase     ,   remove_columns=train_test_validation['''train'''].column_names     ,   )
							UpperCAmelCase				: int											=	DataCollatorWithPadding(tokenizer=_lowerCAmelCase						)

							UpperCAmelCase				: Tuple											=	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'''     ,   )

							UpperCAmelCase				: int											=	Trainer(
							    model=_lowerCAmelCase     ,   args=_lowerCAmelCase     ,   train_dataset=tokenized_datasets['''train''']     ,   eval_dataset=tokenized_datasets['''valid''']     ,   tokenizer=_lowerCAmelCase     ,   data_collator=_lowerCAmelCase     ,   compute_metrics=_lowerCAmelCase     ,   )

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


if __name__ == "__main__":
						main()





