


"""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"""







from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray


def      lowercase			(				A_  ,   A_  ,   A_ = False       )->      list[float]:
  '''simple docstring'''







  if radian_mode:
    return [magnitude * cos(A_       ), magnitude * sin(A_       )]
  return [magnitude * cos(radians(A_       )       ), magnitude * sin(radians(A_       )       )]


def      lowercase			(				A_  ,   A_  ,   A_ = 10**-1       )->      bool:
  '''simple docstring'''







  a						:		NDArray[floataa]      							=			cross(A_  ,   A_       )
  a						:		float      							=			sum(A_       )
  return abs(A_       ) < eps


if __name__ == "__main__":
 # Test to check if it works
 __lowercase    					=      array(
     [
         polar_force(7_18.4, 180 - 30),
         polar_force(8_79.54, 45),
         polar_force(100, -90),
     ]
 )

 __lowercase    					=      array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem 1 in image_data/2D_problems.jpg
 __lowercase    					=      array(
     [
         polar_force(30 * 9.81, 15),
         polar_force(215, 180 - 45),
         polar_force(264, 90 - 30),
     ]
 )

 __lowercase    					=      array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem in image_data/2D_problems_1.jpg
 __lowercase    					=      array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

 __lowercase    					=      array([[0, 0], [6, 0], [10, 0], [12, 0]])

 assert in_static_equilibrium(forces, location)

 import doctest

 doctest.testmod()






