




"""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    UpperCAmelCase		(		):
					_lowerCAmelCase:Optional[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()


UpperCamelCase__						=			load('''accuracy''')





def    UpperCAmelCase		(		snake_case			:					Tuple    ):
					_lowerCAmelCase:Tuple									=  eval_pred
					_lowerCAmelCase:Optional[int]									=  np.argmax(snake_case      ,       axis=1    )
					return metric.compute(predictions=snake_case      ,       references=snake_case    )




class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	str     ,a__      :	Optional[Any])						->       None:




											"""simple docstring"""







											super().__init__()
											_lowerCAmelCase:Optional[int]									=  trainer







						def       __UpperCamelCase     (							self      :	int     ,a__      :	Optional[int]     ,a__      :	Dict     ,a__      :	Any     ,**a__      :	Union[str, Any])						->       Union[str, Any]:




											"""simple docstring"""







											if control.should_evaluate:
																_lowerCAmelCase:str									=  deepcopy(a__)
																self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,metric_key_prefix='''train''')
																return control_copy





def    UpperCAmelCase		(		):
					_lowerCAmelCase:Dict									=  get_args()
					set_seed(args.seed    )

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

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

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

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

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

					_lowerCAmelCase:Tuple									=  train_test_validation.map(
					    snake_case      ,       batched=snake_case      ,       remove_columns=train_test_validation['''train'''].column_names      ,       )
					_lowerCAmelCase:List[Any]									=  DataCollatorWithPadding(tokenizer=snake_case    )

					_lowerCAmelCase:Dict									=  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'''      ,       )

					_lowerCAmelCase:Optional[Any]									=  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()












"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Union[str, Any]     ,a__      :	List[str]=2000     ,a__      :	Tuple=0.1     ,a__      :	Union[str, Any]=20     ,a__      :	int=1E-3)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  None
											_lowerCAmelCase:List[Any]									=  None
											_lowerCAmelCase:Dict									=  None





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Any     ,a__      :	Union[str, torch.device] = None)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:str									=  torch.linspace(1     ,self.config.sampling_eps     ,a__     ,device=a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	str     ,a__      :	List[Any]     ,a__      :	Union[str, Any]     ,a__      :	Any=None)						->       Tuple:




											"""simple docstring"""







											if self.timesteps is None:
																raise ValueError(
																    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''')

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											_lowerCAmelCase:Union[str, Any]									=  (
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											_lowerCAmelCase:Dict									=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
											_lowerCAmelCase:Optional[Any]									=  std.flatten()
											while len(std.shape) < len(score.shape):
																_lowerCAmelCase:str									=  std.unsqueeze(-1)
											_lowerCAmelCase:Optional[int]									=  -score / std

											# compute
											_lowerCAmelCase:Optional[Any]									=  -1.0 / len(self.timesteps)

											_lowerCAmelCase:Union[str, Any]									=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											_lowerCAmelCase:Any									=  beta_t.flatten()
											while len(beta_t.shape) < len(x.shape):
																_lowerCAmelCase:List[str]									=  beta_t.unsqueeze(-1)
											_lowerCAmelCase:Tuple									=  -0.5 * beta_t * x

											_lowerCAmelCase:str									=  torch.sqrt(a__)
											_lowerCAmelCase:Union[str, Any]									=  drift - diffusion**2 * score
											_lowerCAmelCase:List[str]									=  x + drift * dt

											# add noise
											_lowerCAmelCase:Any									=  randn_tensor(x.shape     ,layout=x.layout     ,generator=a__     ,device=x.device     ,dtype=x.dtype)
											_lowerCAmelCase:Union[str, Any]									=  x_mean + diffusion * math.sqrt(-dt) * noise

											return x, x_mean







						def __len__(							self      :	int)						->       Tuple:




											"""simple docstring"""







											return self.config.num_train_timesteps






