




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


lowerCamelCase_			=						load('accuracy')
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]  )			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				eval_pred
   _SCREAMING_SNAKE_CASE	=				np.argmax(__A	,					axis=1  )
   return metric.compute(predictions=__A	,					references=__A  )







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Optional[int]  ,				__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      super().__init__()
      _SCREAMING_SNAKE_CASE	=				trainer



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     Any  ,				**__lowerCamelCase :     Any   ):

      """simple docstring"""
      if control.should_evaluate:
         _SCREAMING_SNAKE_CASE	=				deepcopy(__lowerCamelCase   )
         self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,				metric_key_prefix="train"   )
         return control_copy


def 			SCREAMING_SNAKE_CASE_			(							)			->			Dict:
   _SCREAMING_SNAKE_CASE	=				get_args()
   set_seed(args.seed  )

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

   print("Loading tokenizer and model"  )
   _SCREAMING_SNAKE_CASE	=				AutoTokenizer.from_pretrained(args.model_ckpt  )
   _SCREAMING_SNAKE_CASE	=				tokenizer.eos_token
   _SCREAMING_SNAKE_CASE	=				AutoModelForSequenceClassification.from_pretrained(args.model_ckpt	,					num_labels=7  )
   _SCREAMING_SNAKE_CASE	=				model.config.eos_token_id

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

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

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

   _SCREAMING_SNAKE_CASE	=				train_test_validation.map(
       __A	,					batched=__A	,					remove_columns=train_test_validation["train"].column_names	,					)
   _SCREAMING_SNAKE_CASE	=				DataCollatorWithPadding(tokenizer=__A  )

   _SCREAMING_SNAKE_CASE	=				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"	,					)

   _SCREAMING_SNAKE_CASE	=				Trainer(
       model=__A	,					args=__A	,					train_dataset=tokenized_datasets["train"]	,					eval_dataset=tokenized_datasets["valid"]	,					tokenizer=__A	,					data_collator=__A	,					compute_metrics=__A	,					)

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


if __name__ == "__main__":
 main()







'''simple docstring'''



# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase_			=						abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict  )			->			Dict:
   from diffusers.utils.testing_utils import pytest_addoption_shared

   pytest_addoption_shared(__A  )






def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]  )			->			str:
   from diffusers.utils.testing_utils import pytest_terminal_summary_main

   _SCREAMING_SNAKE_CASE	=				terminalreporter.config.getoption("--make-reports"  )
   if make_reports:
      pytest_terminal_summary_main(__A	,					id=__A  )

