



"""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    A_  (			):







						'''simple docstring'''


						snake_case_ :Optional[Any]						=			argparse.ArgumentParser()
						parser.add_argument("""--model_ckpt""",					type=_lowercase,					default="""microsoft/unixcoder-base-nine"""	)
						parser.add_argument("""--num_epochs""",					type=_lowercase,					default=5	)
						parser.add_argument("""--batch_size""",					type=_lowercase,					default=6	)
						parser.add_argument("""--gradient_accumulation_steps""",					type=_lowercase,					default=1	)
						parser.add_argument("""--freeze""",					type=_lowercase,					default=_lowercase	)
						parser.add_argument("""--learning_rate""",					type=_lowercase,					default=5e-4	)
						parser.add_argument("""--seed""",					type=_lowercase,					default=0	)
						parser.add_argument("""--lr_scheduler_type""",					type=_lowercase,					default="""cosine"""	)
						parser.add_argument("""--num_warmup_steps""",					type=_lowercase,					default=10	)
						parser.add_argument("""--weight_decay""",					type=_lowercase,					default=0.01	)
						parser.add_argument("""--output_dir""",					type=_lowercase,					default="""./results"""	)
						return parser.parse_args()


__a      =				load("accuracy")
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_,       snake_case_ :Dict						=			eval_pred
						snake_case_ :Optional[int]						=			np.argmax(_lowercase,					axis=1	)
						return metric.compute(predictions=_lowercase,					references=_lowercase	)
class 				lowerCamelCase  (			lowerCAmelCase_				):






					'''simple docstring'''





					def __init__(       self:							List[str]						,     snake_case:							Tuple						)   ->	Tuple:
											super().__init__()
											snake_case_ :int						=			trainer






					def    lowerCAmelCase_				(       self:							Union[str, Any]						,     snake_case:							Any						,     snake_case:							List[Any]						,     snake_case:							Tuple						,     **snake_case:							Dict						)   ->	Optional[Any]:
											if control.should_evaluate:
																	snake_case_ :Tuple						=			deepcopy(__lowerCAmelCase						)
																	self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,     metric_key_prefix="""train"""						)
																	return control_copy

def    A_  (			):







						'''simple docstring'''


						snake_case_ :Optional[Any]						=			get_args()
						set_seed(args.seed	)

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

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

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

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

						def tokenize(_lowercase	):
												snake_case_ :Dict						=			tokenizer(example["""src"""],					truncation=_lowercase,					max_length=1024	)
												snake_case_ :str						=			labels.straint(example["""complexity"""]	)
												return {
												    "input_ids": inputs["input_ids"],
												    "attention_mask": inputs["attention_mask"],
												    "label": label,
												}

						snake_case_ :Dict						=			train_test_validation.map(
						    _lowercase,					batched=_lowercase,					remove_columns=train_test_validation["""train"""].column_names,					)
						snake_case_ :Union[str, Any]						=			DataCollatorWithPadding(tokenizer=_lowercase	)

						snake_case_ :List[Any]						=			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""",					)

						snake_case_ :List[Any]						=			Trainer(
						    model=_lowercase,					args=_lowercase,					train_dataset=tokenized_datasets["""train"""],					eval_dataset=tokenized_datasets["""valid"""],					tokenizer=_lowercase,					data_collator=_lowercase,					compute_metrics=_lowercase,					)

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


if __name__ == "__main__":
						main()

"""simple docstring"""


import math


class 				a :


    def    lowerCAmelCase_     (    self		:     Tuple      , __lowerCAmelCase		:     list[list[float]]      , __lowerCAmelCase		:     list[int]    ):
      _UpperCAmelCase         =					0.0
      _UpperCAmelCase         =					0.0
      for i in range(len(__lowerCAmelCase    )    ):
        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    lowerCAmelCase_     (    self		:     Union[str, Any]      , __lowerCAmelCase		:     list[list[int | float]]      , __lowerCAmelCase		:     list[int]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     float    ):
      for i in range(len(__lowerCAmelCase    )    ):
        weights[j][i] += alpha * (sample[i] - weights[j][i])
      return weights






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  # Training Examples ( m, n )
  _UpperCAmelCase         =					[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

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

  # training
  _UpperCAmelCase         =					SelfOrganizingMap()
  _UpperCAmelCase         =					3
  _UpperCAmelCase         =					0.5

  for _ in range(lowercase						):
    for j in range(len(lowercase						)						):
      # training sample
      _UpperCAmelCase         =					training_samples[j]

      # Compute the winning vector
      _UpperCAmelCase         =					self_organizing_map.get_winner(lowercase      ,lowercase						)

      # Update the winning vector
      _UpperCAmelCase         =					self_organizing_map.update(lowercase      ,lowercase      ,lowercase      ,lowercase						)

    # classify test sample
  _UpperCAmelCase         =					[0, 0, 0, 1]
  _UpperCAmelCase         =					self_organizing_map.get_winner(lowercase      ,lowercase						)

  # 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()
