


"""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      lowercase			(				)->      List[Any]:
  '''simple docstring'''







  a						:		Any      							=			argparse.ArgumentParser()
  parser.add_argument("--model_ckpt"  ,   type=A_  ,   default="microsoft/unixcoder-base-nine"       )
  parser.add_argument("--num_epochs"  ,   type=A_  ,   default=5       )
  parser.add_argument("--batch_size"  ,   type=A_  ,   default=6       )
  parser.add_argument("--gradient_accumulation_steps"  ,   type=A_  ,   default=1       )
  parser.add_argument("--freeze"  ,   type=A_  ,   default=A_       )
  parser.add_argument("--learning_rate"  ,   type=A_  ,   default=5e-4       )
  parser.add_argument("--seed"  ,   type=A_  ,   default=0       )
  parser.add_argument("--lr_scheduler_type"  ,   type=A_  ,   default="cosine"       )
  parser.add_argument("--num_warmup_steps"  ,   type=A_  ,   default=10       )
  parser.add_argument("--weight_decay"  ,   type=A_  ,   default=0.0_1       )
  parser.add_argument("--output_dir"  ,   type=A_  ,   default="./results"       )
  return parser.parse_args()


__lowercase    					=      load("""accuracy""")


def      lowercase			(				A_       )->      Tuple:
  '''simple docstring'''







  a							,		a						:		List[str]      							=			eval_pred
  a						:		str      							=			np.argmax(A_  ,   axis=1       )
  return metric.compute(predictions=A_  ,   references=A_       )






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__(					self				:    Optional[Any]				, __UpperCAmelCase				:    Union[str, Any]):
      super().__init__()
      a						:		int      							=			trainer






    def 	__snake_case							(					self				:    Optional[Any]				, __UpperCAmelCase				:    Dict				, __UpperCAmelCase				:    Tuple				, __UpperCAmelCase				:    Optional[int]				, **__UpperCAmelCase				:    List[Any]):
      if control.should_evaluate:
        a						:		Union[str, Any]      							=			deepcopy(__UpperCAmelCase)
        self._trainer.evaluate(eval_dataset=self._trainer.train_dataset				, metric_key_prefix="train")
        return control_copy






def      lowercase			(				)->      Union[str, Any]:
  '''simple docstring'''







  a						:		List[str]      							=			get_args()
  set_seed(args.seed       )

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

  print("Loading tokenizer and model"       )
  a						:		Dict      							=			AutoTokenizer.from_pretrained(args.model_ckpt       )
  a						:		Optional[int]      							=			tokenizer.eos_token
  a						:		Any      							=			AutoModelForSequenceClassification.from_pretrained(args.model_ckpt  ,   num_labels=7       )
  a						:		Any      							=			model.config.eos_token_id

  if args.freeze:
    for param in model.roberta.parameters():
      a						:		Optional[Any]      							=			False

  a						:		Union[str, Any]      							=			ClassLabel(num_classes=7  ,   names=list(set(train_test_validation["train"]["complexity"]       )       )       )

  def tokenize(A_       ):
    a						:		List[str]      							=			tokenizer(example["src"]  ,   truncation=A_  ,   max_length=1_024       )
    a						:		Optional[int]      							=			labels.straint(example["complexity"]       )
    return {
        "input_ids": inputs["input_ids"],
        "attention_mask": inputs["attention_mask"],
        "label": label,
    }

  a						:		Optional[Any]      							=			train_test_validation.map(
      A_  ,   batched=A_  ,   remove_columns=train_test_validation["train"].column_names  ,   )
  a						:		Dict      							=			DataCollatorWithPadding(tokenizer=A_       )

  a						:		Optional[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=0.0_1  ,   metric_for_best_model="accuracy"  ,   run_name="complexity-java"  ,   report_to="wandb"  ,   )

  a						:		Union[str, Any]      							=			Trainer(
      model=A_  ,   args=A_  ,   train_dataset=tokenized_datasets["train"]  ,   eval_dataset=tokenized_datasets["valid"]  ,   tokenizer=A_  ,   data_collator=A_  ,   compute_metrics=A_  ,   )

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


if __name__ == "__main__":
 main()










"""simple docstring"""




__UpperCamelCase						=      0  # The first color of the flag.
__UpperCamelCase						=      1  # The second color of the flag.
__UpperCamelCase						=      2  # The third color of the flag.
__UpperCamelCase						=      (red, white, blue)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list  )   ->						list:
						if not sequence:
												return []
						if len(SCREAMING_SNAKE_CASE_  ) == 1:
												return list(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					0
						SCREAMING_SNAKE_CASE													=					len(SCREAMING_SNAKE_CASE_  ) - 1
						SCREAMING_SNAKE_CASE													=					0
						while mid <= high:
												if sequence[mid] == colors[0]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[mid], sequence[low]
																		low += 1
																		mid += 1
												elif sequence[mid] == colors[1]:
																		mid += 1
												elif sequence[mid] == colors[2]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[high], sequence[mid]
																		high -= 1
												else:
																		SCREAMING_SNAKE_CASE													=					F'The elements inside the sequence must contains only {colors} values'
																		raise ValueError(SCREAMING_SNAKE_CASE_  )
						return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__UpperCamelCase						=      input('''Enter numbers separated by commas:\n''').strip()
	__UpperCamelCase						=      [int(item.strip()) for item in user_input.split(''',''')]
	print(f'''{dutch_national_flag_sort(unsorted)}''')


