'''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 _lowercase	(      )					->       Optional[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				int       				=		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=0.01						)
       parser.add_argument("--output_dir"							,    type=lowerCamelCase__							,    default="./results"						)
       return parser.parse_args()


_a   :	List[str] 							=   load("accuracy")

def _lowercase	(      lowerCamelCase__						)					->       Optional[Any]:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				int       				=		eval_pred
       __UpperCAmelCase  :				Optional[int]       				=		np.argmax(lowerCamelCase__							,    axis=1						)
       return metric.compute(predictions=lowerCamelCase__							,    references=lowerCamelCase__						)






class 		__A       (__magic_name__   ):






       def __init__(      self				,     UpperCamelCase_      ):
              super().__init__()
              __UpperCAmelCase  :				int       				=		trainer







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ):
              if control.should_evaluate:
                     __UpperCAmelCase  :				List[Any]       				=		deepcopy(UpperCamelCase_      )
                     self._trainer.evaluate(eval_dataset=self._trainer.train_dataset				,     metric_key_prefix="train"      )
                     return control_copy





def _lowercase	(      )					->       Union[str, Any]:





       """simple docstring"""


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

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

       print("Loading tokenizer and model"						)
       __UpperCAmelCase  :				Any       				=		AutoTokenizer.from_pretrained(args.model_ckpt						)
       __UpperCAmelCase  :				Optional[Any]       				=		tokenizer.eos_token
       __UpperCAmelCase  :				Dict       				=		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  :				Optional[Any]       				=		False

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

       def tokenize(lowerCamelCase__						):
              __UpperCAmelCase  :				Optional[int]       				=		tokenizer(example["src"]							,    truncation=lowerCamelCase__							,    max_length=1024						)
              __UpperCAmelCase  :				str       				=		labels.straint(example["complexity"]						)
              return {
                  "input_ids": inputs["input_ids"],
                  "attention_mask": inputs["attention_mask"],
                  "label": label,
              }

       __UpperCAmelCase  :				int       				=		train_test_validation.map(
           lowerCamelCase__							,    batched=lowerCamelCase__							,    remove_columns=train_test_validation["train"].column_names							,    )
       __UpperCAmelCase  :				str       				=		DataCollatorWithPadding(tokenizer=lowerCamelCase__						)

       __UpperCAmelCase  :				List[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"							,    )

       __UpperCAmelCase  :				Union[str, Any]       				=		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()







'''simple docstring'''




def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       float:





       """simple docstring"""


       if discount_rate < 0:
              raise ValueError("Discount rate cannot be negative"						)
       if not cash_flows:
              raise ValueError("Cash flows list cannot be empty"						)
       __UpperCAmelCase  :				Tuple       				=		sum(
           cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(lowerCamelCase__						)						)
       return round(lowerCamelCase__							,    ndigits=2						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






