

'''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  __UpperCamelCase							(		):






     '''simple docstring'''





     __lowercase							=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=0.01       )
     parser.add_argument('--output_dir',    type=lowerCAmelCase__,    default='./results'       )
     return parser.parse_args()


UpperCAmelCase       =     load('''accuracy''')

def  __UpperCamelCase							(		lowercase__  :     Optional[Any]       ):






     '''simple docstring'''





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


class      lowerCAmelCase   (					__a      ):







       def __init__(							self	:       str  ,					__lowercase	:       Union[str, Any]					):







            """simple docstring"""







            super().__init__()
            __lowercase							=trainer







       def 	snake_case      (							self	:       List[str]  ,					__lowercase	:       Union[str, Any]  ,					__lowercase	:       Dict  ,					__lowercase	:       List[Any]  ,					**__lowercase	:       Dict					):







            """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  __UpperCamelCase							(		):






     '''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(lowercase__  :     List[Any]       ):
          __lowercase							=tokenizer(example['src'],    truncation=lowerCAmelCase__,    max_length=10_24       )
          __lowercase							=labels.straint(example['complexity']       )
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

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

     __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=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()






'''simple docstring'''
import math




class 			lowerCamelCase_   :






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    list[list[float]]						,						_A				:    list[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				0.0
          UpperCAmelCase__			:		Union[str, Any]													=				0.0
          for i in range(len(_A ) ):
              da += math.pow((sample[i] - weights[0][i])						,						2 )
              da += math.pow((sample[i] - weights[1][i])						,						2 )
              return 0 if da > da else 1
          return 0






      def lowercase_							(					self				:    Dict						,						_A				:    list[list[int | float]]						,						_A				:    list[int]						,						_A				:    int						,						_A				:    float ):


          '''simple docstring'''


          for i in range(len(_A ) ):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
          return weights





def 					a__       (					)    ->	None:
    # Training Examples ( m, n )
    UpperCAmelCase__			:		Optional[Any]													=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    UpperCAmelCase__			:		str													=				[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    UpperCAmelCase__			:		Optional[Any]													=				SelfOrganizingMap()
    UpperCAmelCase__			:		Tuple													=				3
    UpperCAmelCase__			:		Dict													=				0.5

    for _ in range(lowerCAmelCase__						):
        for j in range(len(lowerCAmelCase__						)						):
            # training sample
            UpperCAmelCase__			:		Optional[int]													=				training_samples[j]

            # Compute the winning vector
            UpperCAmelCase__			:		str													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

            # Update the winning vector
            UpperCAmelCase__			:		int													=				self_organizing_map.update(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

    # classify test sample
    UpperCAmelCase__			:		str													=				[0, 0, 0, 1]
    UpperCAmelCase__			:		int													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

    # results
    print(F"""Clusters that the test sample belongs to : {winner}"""						)
    print(F"""Weights that have been trained : {weights}"""						)


# running the main() function
if __name__ == "__main__":
  main()
