





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_			(	)					->		Any:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =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()


__A     =    load("accuracy")





def 					lowerCAmelCase_			(	__a		)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =eval_pred
  lowerCamelCase__:				List[Any]        =np.argmax(__a     ,   axis=1		)
  return metric.compute(predictions=__a     ,   references=__a		)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def __init__(self    :       Optional[int]	,			UpperCAmelCase_    :       Optional[int])	->None:



   '''simple docstring'''



   super().__init__()
   lowerCamelCase__:				int        =trainer







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       Dict	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Optional[int]	,			**UpperCAmelCase_    :       Any)	->int:



   '''simple docstring'''



   if control.should_evaluate:
     lowerCamelCase__:				Tuple        =deepcopy(UpperCAmelCase_)
     self._trainer.evaluate(eval_dataset=self._trainer.train_dataset	,			metric_key_prefix="train")
     return control_copy





def 					lowerCAmelCase_			(	)					->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:				Dict        =get_args()
  set_seed(args.seed		)

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

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

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

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

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

  lowerCamelCase__:				Tuple        =train_test_validation.map(
      __a     ,   batched=__a     ,   remove_columns=train_test_validation["train"].column_names     ,   )
  lowerCamelCase__:				Optional[Any]        =DataCollatorWithPadding(tokenizer=__a		)

  lowerCamelCase__:				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=0.0_1     ,   metric_for_best_model="accuracy"     ,   run_name="complexity-java"     ,   report_to="wandb"     ,   )

  lowerCamelCase__:				Optional[Any]        =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()








import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      CTRLTokenizer
 lowercase_		=      False
 lowercase_		=      False

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



   '''simple docstring'''



   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   lowerCamelCase__:				Optional[Any]        =["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
   lowerCamelCase__:				Optional[Any]        =dict(zip(UpperCAmelCase_	,			range(len(UpperCAmelCase_))))
   lowerCamelCase__:				Union[str, Any]        =["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
   lowerCamelCase__:				Union[str, Any]        ={"unk_token": "<unk>"}

   lowerCamelCase__:				Union[str, Any]        =os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES["vocab_file"])
   lowerCamelCase__:				Optional[Any]        =os.path.join(self.tmpdirname	,			VOCAB_FILES_NAMES["merges_file"])
   with open(self.vocab_file	,			"w"	,			encoding="utf-8") as fp:
     fp.write(json.dumps(UpperCAmelCase_) + "\n")
   with open(self.merges_file	,			"w"	,			encoding="utf-8") as fp:
     fp.write("\n".join(UpperCAmelCase_))

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			**UpperCAmelCase_    :       str)	->List[Any]:



   '''simple docstring'''



   kwargs.update(self.special_tokens_map)
   return CTRLTokenizer.from_pretrained(self.tmpdirname	,			**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       Optional[int])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				int        ="adapt react readapt apt"
   lowerCamelCase__:				int        ="adapt react readapt apt"
   return input_text, output_text







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



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =CTRLTokenizer(self.vocab_file	,			self.merges_file	,			**self.special_tokens_map)
   lowerCamelCase__:				Union[str, Any]        ="adapt react readapt apt"
   lowerCamelCase__:				Tuple        ="adapt re@@ a@@ c@@ t re@@ adapt apt".split()
   lowerCamelCase__:				Dict        =tokenizer.tokenize(UpperCAmelCase_)
   self.assertListEqual(UpperCAmelCase_	,			UpperCAmelCase_)

   lowerCamelCase__:				Optional[int]        =tokens + [tokenizer.unk_token]

   lowerCamelCase__:				List[str]        =[0, 1, 2, 4, 5, 1, 0, 3, 6]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_)	,			UpperCAmelCase_)

