
"""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     :						int						=   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()


_a      :			Any					=							load("""accuracy""")
def 				a__   (   a   :				Optional[Any]		):

  """simple docstring"""







  _snake_case     :						str						=   eval_pred
  _snake_case     :						Union[str, Any]						=   np.argmax(UpperCAmelCase__       ,     axis=1		)
  return metric.compute(predictions=UpperCAmelCase__       ,     references=UpperCAmelCase__		)






class      _UpperCAmelCase				(					_UpperCAmelCase):
    def __init__(    self						,    snake_case_     ):
      super().__init__()
      _snake_case     :						List[Any]						=   trainer


    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    **snake_case_     ):
      if control.should_evaluate:
        _snake_case     :						Union[str, Any]						=   deepcopy(lowercase_     )
        self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,    metric_key_prefix="train"     )
        return control_copy


def 				a__   (   ):

  """simple docstring"""







  _snake_case     :						Union[str, 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     :						Any						=   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     :						int						=   AutoTokenizer.from_pretrained(args.model_ckpt		)
  _snake_case     :						Optional[int]						=   tokenizer.eos_token
  _snake_case     :						Any						=   AutoModelForSequenceClassification.from_pretrained(args.model_ckpt       ,     num_labels=7		)
  _snake_case     :						Optional[Any]						=   model.config.eos_token_id

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

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

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

  _snake_case     :						List[str]						=   train_test_validation.map(
      UpperCAmelCase__       ,     batched=UpperCAmelCase__       ,     remove_columns=train_test_validation["train"].column_names       ,     )
  _snake_case     :						int						=   DataCollatorWithPadding(tokenizer=UpperCAmelCase__		)

  _snake_case     :						Dict						=   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=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()

"""simple docstring"""
from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def 				a__   (   a   :				float       ,     a   :				float       ,     a   :				bool = False		):

  """simple docstring"""







  if radian_mode:
    return [magnitude * cos(a		), magnitude * sin(a		)]
  return [magnitude * cos(radians(a		)		), magnitude * sin(radians(a		)		)]
def 				a__   (   a   :				NDArray[floataa]       ,     a   :				NDArray[floataa]       ,     a   :				float = 10**-1		):

  """simple docstring"""







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


if __name__ == "__main__":
    # Test to check if it works
    _a      :			Tuple					=							array(
        [
            polar_force(718.4, 180 - 30),
            polar_force(879.54, 45),
            polar_force(100, -90),
        ]
    )

    _a      :			NDArray[floataa]					=							array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

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

    _a      :			List[Any]					=							array([[0, 0], [0, 0], [0, 0]])

    assert in_static_equilibrium(forces, location)

    # Problem in image_data/2D_problems_1.jpg
    _a      :			List[str]					=							array([[0, -2_000], [0, -1_200], [0, 15_600], [0, -12_400]])

    _a      :			Optional[int]					=							array([[0, 0], [6, 0], [10, 0], [12, 0]])

    assert in_static_equilibrium(forces, location)

    import doctest

    doctest.testmod()

