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__       (						):
      SCREAMING_SNAKE_CASE_      =     argparse.ArgumentParser()
      parser.add_argument("--model_ckpt"   ,					type=__UpperCamelCase   ,					default="microsoft/unixcoder-base-nine" )
      parser.add_argument("--num_epochs"   ,					type=__UpperCamelCase   ,					default=5 )
      parser.add_argument("--batch_size"   ,					type=__UpperCamelCase   ,					default=6 )
      parser.add_argument("--gradient_accumulation_steps"   ,					type=__UpperCamelCase   ,					default=1 )
      parser.add_argument("--freeze"   ,					type=__UpperCamelCase   ,					default=__UpperCamelCase )
      parser.add_argument("--learning_rate"   ,					type=__UpperCamelCase   ,					default=5E-4 )
      parser.add_argument("--seed"   ,					type=__UpperCamelCase   ,					default=0 )
      parser.add_argument("--lr_scheduler_type"   ,					type=__UpperCamelCase   ,					default="cosine" )
      parser.add_argument("--num_warmup_steps"   ,					type=__UpperCamelCase   ,					default=1_0 )
      parser.add_argument("--weight_decay"   ,					type=__UpperCamelCase   ,					default=0.01 )
      parser.add_argument("--output_dir"   ,					type=__UpperCamelCase   ,					default="./results" )
      return parser.parse_args()


A   :     List[Any]       =				load("accuracy")


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     eval_pred
      SCREAMING_SNAKE_CASE_      =     np.argmax(__UpperCamelCase   ,					axis=1 )
      return metric.compute(predictions=__UpperCamelCase   ,					references=__UpperCamelCase )


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  def __init__(							self       :				int     ,   __magic_name__       :				Any      )     ->		None:
        super().__init__()
        SCREAMING_SNAKE_CASE_      =     trainer







  def       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				Union[str, Any]     ,   __magic_name__       :				Optional[int]     ,   __magic_name__       :				Optional[int]     ,   **__magic_name__       :				Optional[Any]      )     ->		str:
        if control.should_evaluate:
              SCREAMING_SNAKE_CASE_      =     deepcopy(__magic_name__      )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,   metric_key_prefix="train"      )
              return control_copy



def    a__       (						):
      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(__UpperCamelCase ):
            SCREAMING_SNAKE_CASE_      =     tokenizer(example["src"]   ,					truncation=__UpperCamelCase   ,					max_length=1_0_2_4 )
            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(
          __UpperCamelCase   ,					batched=__UpperCamelCase   ,					remove_columns=train_test_validation["train"].column_names   ,					)
      SCREAMING_SNAKE_CASE_      =     DataCollatorWithPadding(tokenizer=__UpperCamelCase )

      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=0.01   ,					metric_for_best_model="accuracy"   ,					run_name="complexity-java"   ,					report_to="wandb"   ,					)

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

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


if __name__ == "__main__":
      main()



from ....configuration_utils import PretrainedConfig
from ....utils import logging


A   :     str       =				logging.get_logger(__name__)

# TODO: upload to AWS
A   :     Dict       =				{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''retribert'''







  def __init__(							self       :				Optional[int]     ,   __magic_name__       :				Optional[Any]=30_522     ,   __magic_name__       :				int=768     ,   __magic_name__       :				Dict=8     ,   __magic_name__       :				List[Any]=12     ,   __magic_name__       :				Tuple=3_072     ,   __magic_name__       :				List[Any]="gelu"     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Tuple=512     ,   __magic_name__       :				Dict=2     ,   __magic_name__       :				int=0.02     ,   __magic_name__       :				List[Any]=1e-12     ,   __magic_name__       :				List[str]=True     ,   __magic_name__       :				Dict=128     ,   __magic_name__       :				Union[str, Any]=0     ,   **__magic_name__       :				List[Any]     ,   )     ->		Dict:
        super().__init__(pad_token_id=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     vocab_size
        SCREAMING_SNAKE_CASE_      =     hidden_size
        SCREAMING_SNAKE_CASE_      =     num_hidden_layers
        SCREAMING_SNAKE_CASE_      =     num_attention_heads
        SCREAMING_SNAKE_CASE_      =     hidden_act
        SCREAMING_SNAKE_CASE_      =     intermediate_size
        SCREAMING_SNAKE_CASE_      =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_      =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_      =     max_position_embeddings
        SCREAMING_SNAKE_CASE_      =     type_vocab_size
        SCREAMING_SNAKE_CASE_      =     initializer_range
        SCREAMING_SNAKE_CASE_      =     layer_norm_eps
        SCREAMING_SNAKE_CASE_      =     share_encoders
        SCREAMING_SNAKE_CASE_      =     projection_dim


