






from __future__ import annotations

from random import random
class        a       :



      """simple docstring"""


      def __init__( self   :     Optional[int]						, __lowercase   :     int | None = None   )				-> Any:
        __UpperCAmelCase				:      Optional[Any]	 =    value
        __UpperCAmelCase				:      Dict	 =    random()
        __UpperCAmelCase				:      Node | None	 =    None
        __UpperCAmelCase				:      Node | None	 =    None
      def __repr__( self   :     Optional[int]   )				-> str:
        from pprint import pformat

        if self.left is None and self.right is None:
          return f"""'{self.value}: {self.prior:.5}'"""
        else:
          return pformat(
              {f"""{self.value}: {self.prior:.5}""": (self.left, self.right)}						, indent=1   )




      def __str__( self   :     Tuple   )				-> str:
        __UpperCAmelCase				:      Dict	 =    str(self.value   ) + """ """
        __UpperCAmelCase				:      List[str]	 =    str(self.left or """"""   )
        __UpperCAmelCase				:      Optional[Any]	 =    str(self.right or """"""   )
        return value + left + right






def 							lowerCamelCase__ (       __lowerCamelCase     :	Node | None    ,						__lowerCamelCase     :	int						):
  if root is None:  # None tree is split into 2 Nones
    return None, None
  elif root.value is None:
    return None, None
  else:
    if value < root.value:
      __UpperCAmelCase ,					__UpperCAmelCase				:      List[str]	 =    split(root.left    ,						__lowerCamelCase						)
      return left, root
    else:
      __UpperCAmelCase ,					__UpperCAmelCase				:      Any	 =    split(root.right    ,						__lowerCamelCase						)
      return root, right






def 							lowerCamelCase__ (       __lowerCamelCase     :	Node | None    ,						__lowerCamelCase     :	Node | None						):
  if (not left) or (not right):  # If one node is None, return the other
    return left or right
  elif left.prior < right.prior:
    __UpperCAmelCase				:      Optional[Any]	 =    merge(left.right    ,						__lowerCamelCase						)
    return left
  else:
    __UpperCAmelCase				:      List[Any]	 =    merge(__lowerCamelCase    ,						right.left						)
    return right






def 							lowerCamelCase__ (       __lowerCamelCase     :	Node | None    ,						__lowerCamelCase     :	int						):
  __UpperCAmelCase				:      Tuple	 =    Node(__lowerCamelCase						)
  __UpperCAmelCase ,					__UpperCAmelCase				:      List[str]	 =    split(__lowerCamelCase    ,						__lowerCamelCase						)
  return merge(merge(__lowerCamelCase    ,						__lowerCamelCase						)    ,						__lowerCamelCase						)






def 							lowerCamelCase__ (       __lowerCamelCase     :	Node | None    ,						__lowerCamelCase     :	int						):
  __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[Any]	 =    split(__lowerCamelCase    ,						value - 1						)
  __UpperCAmelCase ,					__UpperCAmelCase				:      Any	 =    split(__lowerCamelCase    ,						__lowerCamelCase						)
  return merge(__lowerCamelCase    ,						__lowerCamelCase						)






def 							lowerCamelCase__ (       __lowerCamelCase     :	Node | None						):
  if not root:  # None
    return
  else:
    inorder(root.left						)
    print(root.value    ,						end=""","""						)
    inorder(root.right						)






def 							lowerCamelCase__ (       __lowerCamelCase     :	Node | None    ,						__lowerCamelCase     :	str						):
  for arg in args.split():
    if arg[0] == "+":
      __UpperCAmelCase				:      Tuple	 =    insert(__lowerCamelCase    ,						int(arg[1:]						)						)

    elif arg[0] == "-":
      __UpperCAmelCase				:      Union[str, Any]	 =    erase(__lowerCamelCase    ,						int(arg[1:]						)						)

    else:
      print("""Unknown command"""						)

  return root






def 							lowerCamelCase__ (       ):
  __UpperCAmelCase				:      str	 =    None
  print(
      """enter numbers to create a tree, + value to add value into treap, """
      """- value to erase all nodes with value. 'q' to quit. """						)

  __UpperCAmelCase				:      int	 =    input()
  while args != "q":
    __UpperCAmelCase				:      Union[str, Any]	 =    interact_treap(__lowerCamelCase    ,						__lowerCamelCase						)
    print(__lowerCamelCase						)
    __UpperCAmelCase				:      Dict	 =    input()

  print("""good by!"""						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()




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


UpperCamelCase__          =	load('accuracy')
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					eval_pred
    UpperCAmelCase__	:      Optional[int]     							=					np.argmax(_snake_case    ,axis=1 )
    return metric.compute(predictions=_snake_case    ,references=_snake_case )





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_      ):
          super().__init__()
          UpperCAmelCase__	:      List[str]     							=					trainer



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ):
          if control.should_evaluate:
              UpperCAmelCase__	:      int     							=					deepcopy(UpperCamelCase_      )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,	metric_key_prefix='train'      )
              return control_copy




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      int     							=					get_args()
    set_seed(args.seed )

    UpperCAmelCase__	:      Optional[int]     							=					load_dataset('codeparrot/codecomplex'    ,split='train' )
    UpperCAmelCase__	:      Tuple     							=					dataset.train_test_split(test_size=0.2 )
    UpperCAmelCase__	:      List[Any]     							=					train_test['test'].train_test_split(test_size=0.5 )
    UpperCAmelCase__	:      Tuple     							=					DatasetDict(
        {
            'train': train_test['train'],
            'test': test_validation['train'],
            'valid': test_validation['test'],
        } )

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

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

    UpperCAmelCase__	:      Optional[Any]     							=					ClassLabel(num_classes=7    ,names=list(set(train_test_validation['train']['complexity'] ) ) )

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

    UpperCAmelCase__	:      List[Any]     							=					train_test_validation.map(
        _snake_case    ,batched=_snake_case    ,remove_columns=train_test_validation['train'].column_names    ,)
    UpperCAmelCase__	:      Tuple     							=					DataCollatorWithPadding(tokenizer=_snake_case )

    UpperCAmelCase__	:      Union[str, 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'    ,)

    UpperCAmelCase__	:      Dict     							=					Trainer(
        model=_snake_case    ,args=_snake_case    ,train_dataset=tokenized_datasets['train']    ,eval_dataset=tokenized_datasets['valid']    ,tokenizer=_snake_case    ,data_collator=_snake_case    ,compute_metrics=_snake_case    ,)

    print('Training...' )
    trainer.add_callback(CustomCallback(_snake_case ) )
    trainer.train()


if __name__ == "__main__":
    main()

