





'''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        _lowerCAmelCase			(  )     ->     Optional[Any]:
     __A						: Dict      =       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()


lowercase__    :      Optional[Any]						=    load('''accuracy''')






def        _lowerCAmelCase			(  __snake_case  : Optional[Any]			)     ->     Dict:
     __A ,__A						: Tuple      =       eval_pred
     __A						: List[Any]      =       np.argmax(__snake_case ,							axis=1			)
     return metric.compute(predictions=__snake_case ,							references=__snake_case			)


class  SCREAMING_SNAKE_CASE    (a__							):


     def __init__(						self						,	_UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          __A						: Dict      =       trainer


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          if control.should_evaluate:
               __A						: Dict      =       deepcopy(_UpperCAmelCase)
               self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,	metric_key_prefix='train')
               return control_copy






def        _lowerCAmelCase			(  )     ->     Optional[int]:
     __A						: str      =       get_args()
     set_seed(args.seed			)

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

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

     if args.freeze:
          for param in model.roberta.parameters():
               __A						: List[str]      =       False

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

     def tokenize(__snake_case  : Union[str, Any]			):
          __A						: Optional[Any]      =       tokenizer(example['src'] ,							truncation=__snake_case ,							max_length=10_24			)
          __A						: Optional[int]      =       labels.straint(example['complexity']			)
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     __A						: Any      =       train_test_validation.map(
         __snake_case ,							batched=__snake_case ,							remove_columns=train_test_validation['train'].column_names ,							)
     __A						: Any      =       DataCollatorWithPadding(tokenizer=__snake_case			)

     __A						: str      =       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' ,							)

     __A						: 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'''




import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
  lowercase__    :      List[Any]						=    pd.read_csv('''sample_data.csv''', header=None)
  lowercase__    :      Union[str, Any]						=    df.shape[:1][0]
  # If you're using some other dataset input the target column
  lowercase__    :      Any						=    df.iloc[:, 1:2]
  lowercase__    :      int						=    actual_data.values.reshape(len_data, 1)
  lowercase__    :      int						=    MinMaxScaler().fit_transform(actual_data)
  lowercase__    :      Dict						=    10
  lowercase__    :      List[str]						=    5
  lowercase__    :      Dict						=    20
  lowercase__    :      Dict						=    len_data - periods * look_back
  lowercase__    :      Any						=    actual_data[:division]
  lowercase__    :      Optional[int]						=    actual_data[division - look_back :]
  lowercase__	,							lowercase__    :      Optional[Any]						=    [], []
  lowercase__	,							lowercase__    :      Tuple						=    [], []

  for i in range(0, len(train_data) - forward_days - look_back + 1):
    train_x.append(train_data[i : i + look_back])
    train_y.append(train_data[i + look_back : i + look_back + forward_days])
  for i in range(0, len(test_data) - forward_days - look_back + 1):
    test_x.append(test_data[i : i + look_back])
    test_y.append(test_data[i + look_back : i + look_back + forward_days])
  lowercase__    :      List[Any]						=    np.array(train_x)
  lowercase__    :      List[Any]						=    np.array(test_x)
  lowercase__    :      str						=    np.array([list(i.ravel()) for i in train_y])
  lowercase__    :      Any						=    np.array([list(i.ravel()) for i in test_y])

  lowercase__    :      Optional[Any]						=    Sequential()
  model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
  model.add(LSTM(64, input_shape=(1_28, 1)))
  model.add(Dense(forward_days))
  model.compile(loss='''mean_squared_error''', optimizer='''adam''')
  lowercase__    :      Tuple						=    model.fit(
      x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
  )
  lowercase__    :      int						=    model.predict(x_test)