


from typing import TYPE_CHECKING

from ...utils import _LazyModule


SCREAMING_SNAKE_CASE							    =							{'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
  from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







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  a   ():
   __a					=  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=0.0_1   )
   parser.add_argument("""--output_dir"""		,				type=lowerCAmelCase__		,				default="""./results"""   )
   return parser.parse_args()


SCREAMING_SNAKE_CASE							    =							load('accuracy')




def  a   (lowerCAmelCase__   ):
   __a	,    __a					=  eval_pred
   __a					=  np.argmax(lowerCAmelCase__		,				axis=1   )
   return metric.compute(predictions=lowerCAmelCase__		,				references=lowerCAmelCase__   )


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        super().__init__()
        __a					=  trainer


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       **__A    ):
        if control.should_evaluate:
           __a					=  deepcopy(__A    )
           self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,       metric_key_prefix="""train"""    )
           return control_copy







def  a   ():
   __a					=  get_args()
   set_seed(args.seed   )

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

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

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

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

   def tokenize(lowerCAmelCase__   ):
      __a					=  tokenizer(example["""src"""]		,				truncation=lowerCAmelCase__		,				max_length=1_024   )
      __a					=  labels.straint(example["""complexity"""]   )
      return {
          "input_ids": inputs["input_ids"],
          "attention_mask": inputs["attention_mask"],
          "label": label,
      }

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

   __a					=  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"""		,				)

   __a					=  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()



