


"""simple docstring"""

from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class    lowerCamelCase__						(		_a    ):
      a :     Union[str, Any]											=		None
      a :     List[str]											=		None
      a :     str											=		None
      a :     Optional[int]											=		None






class    lowerCamelCase__						(		_a    ):
      def __init__(		self	:	Any      ,       A_	:	int=1      ,       A_	:	Any=0      ,       A_	:	Dict=2      ,       A_	:	Dict=5_1_2      ,       A_	:	Union[str, Any]="cls"      ,       A_	:	str=False      ,       A_	:	int=True      ,       **A_	:	List[str]      ,       ):






       '''simple docstring'''


       super().__init__(pad_token_id=__A      ,       bos_token_id=__A      ,       eos_token_id=__A      ,       **__A							)
       __lowercase									=	project_dim
       __lowercase									=	pooler_fn
       __lowercase									=	learn_encoder
       __lowercase									=	use_attention_mask






class    lowerCamelCase__						(		_a    ):
      a :     List[str]											=		[r"""pooler""", r"""logit_scale"""]
      a :     Union[str, Any]											=		[r"""position_ids""", r"""predictions.decoder.bias"""]
      a :     int											=		"""roberta"""
      a :     str											=		RobertaSeriesConfig
      def __init__(		self	:	Dict      ,       A_	:	Optional[Any]							):






       '''simple docstring'''


       super().__init__(__A							)
       __lowercase									=	XLMRobertaModel(__A							)
       __lowercase									=	nn.Linear(config.hidden_size      ,       config.project_dim							)
       __lowercase									=	getattr(__A      ,       """has_pre_transformation"""      ,       __A							)
       if self.has_pre_transformation:
        __lowercase									=	nn.Linear(config.hidden_size      ,       config.project_dim							)
        __lowercase									=	nn.LayerNorm(config.hidden_size      ,       eps=config.layer_norm_eps							)
       self.post_init()






      def SCREAMING_SNAKE_CASE_	(		self	:	Any      ,       A_	:	Optional[torch.Tensor] = None      ,       A_	:	Optional[torch.Tensor] = None      ,       A_	:	Optional[torch.Tensor] = None      ,       A_	:	Optional[torch.Tensor] = None      ,       A_	:	Optional[torch.Tensor] = None      ,       A_	:	Optional[torch.Tensor] = None      ,       A_	:	Optional[torch.Tensor] = None      ,       A_	:	Optional[torch.Tensor] = None      ,       A_	:	Optional[bool] = None      ,       A_	:	Optional[bool] = None      ,       A_	:	Optional[bool] = None      ,       ):






       '''simple docstring'''


       __lowercase									=	return_dict if return_dict is not None else self.config.use_return_dict

       __lowercase									=	self.base_model(
           input_ids=__A      ,       attention_mask=__A      ,       token_type_ids=__A      ,       position_ids=__A      ,       head_mask=__A      ,       inputs_embeds=__A      ,       encoder_hidden_states=__A      ,       encoder_attention_mask=__A      ,       output_attentions=__A      ,       output_hidden_states=True if self.has_pre_transformation else output_hidden_states      ,       return_dict=__A      ,       )

       if self.has_pre_transformation:
        __lowercase									=	outputs["""hidden_states"""][-2]
        __lowercase									=	self.pre_LN(__A							)
        __lowercase									=	self.transformation_pre(__A							)

        return TransformationModelOutput(
            projection_state=__A      ,       last_hidden_state=outputs.last_hidden_state      ,       hidden_states=outputs.hidden_states      ,       attentions=outputs.attentions      ,       )
       else:
        __lowercase									=	self.transformation(outputs.last_hidden_state							)
        return TransformationModelOutput(
            projection_state=__A      ,       last_hidden_state=outputs.last_hidden_state      ,       hidden_states=outputs.hidden_states      ,       attentions=outputs.attentions      ,       )




"""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 lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	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=10 )
 parser.add_argument("""--weight_decay"""				,   type=UpperCamelCase__				,   default=0.01 )
 parser.add_argument("""--output_dir"""				,   type=UpperCamelCase__				,   default="""./results""" )
 return parser.parse_args()


UpperCAmelCase__  							=load("accuracy")


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase							,		__lowercase									=	eval_pred
 __lowercase									=	np.argmax(UpperCamelCase__				,   axis=1 )
 return metric.compute(predictions=UpperCamelCase__				,   references=UpperCamelCase__ )





class    lowerCamelCase__						(		_a    ):
      def __init__(		self	:	Any      ,       A_	:	int							):






       '''simple docstring'''


       super().__init__()
       __lowercase									=	trainer






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	int      ,       A_	:	str      ,       A_	:	Any      ,       **A_	:	Optional[int]							):






       '''simple docstring'''


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






def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	get_args()
 set_seed(args.seed )

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

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

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

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

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

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

 __lowercase									=	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"""				,   )

 __lowercase									=	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()
