




"""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   _SCREAMING_SNAKE_CASE       (							)						->		List[Any]:
 A__	=    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=0.0_1						)
 parser.add_argument("--output_dir"      ,			type=lowercase_      ,			default="./results"						)
 return parser.parse_args()


SCREAMING_SNAKE_CASE    =	load("accuracy")







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		Any:
 A__,				A__	=    eval_pred
 A__	=    np.argmax(lowercase_      ,			axis=1						)
 return metric.compute(predictions=lowercase_      ,			references=lowercase_						)




class      UpperCAmelCase_  (						A_							):




    def __init__(   self						:	Any     ,			snake_case_						:	Union[str, Any]				)				->							None:




     '''simple docstring'''

     super().__init__()
     A__	=    trainer




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	int     ,			snake_case_						:	Optional[Any]     ,			**snake_case_						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

     if control.should_evaluate:
      A__	=    deepcopy(snake_case_				)
      self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,			metric_key_prefix="train"				)
      return control_copy







def   _SCREAMING_SNAKE_CASE       (							)						->		List[str]:
 A__	=    get_args()
 set_seed(args.seed						)

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

 print("Loading tokenizer and model"						)
 A__	=    AutoTokenizer.from_pretrained(args.model_ckpt						)
 A__	=    tokenizer.eos_token
 A__	=    AutoModelForSequenceClassification.from_pretrained(args.model_ckpt      ,			num_labels=7						)
 A__	=    model.config.eos_token_id

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

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

 def tokenize(lowercase_						):
  A__	=    tokenizer(example["src"]      ,			truncation=lowercase_      ,			max_length=10_24						)
  A__	=    labels.straint(example["complexity"]						)
  return {
      "input_ids": inputs["input_ids"],
      "attention_mask": inputs["attention_mask"],
      "label": label,
  }

 A__	=    train_test_validation.map(
     lowercase_      ,			batched=lowercase_      ,			remove_columns=train_test_validation["train"].column_names      ,			)
 A__	=    DataCollatorWithPadding(tokenizer=lowercase_						)

 A__	=    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__	=    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"""



from __future__ import annotations

SCREAMING_SNAKE_CASE    =	"#"




class      UpperCAmelCase_  :




    def __init__(   self						:	Dict				)				->							None:




     '''simple docstring'''

     A__	=    {}




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	str				)				->							None:




     '''simple docstring'''

     A__	=    self._trie
     for char in text:
      if char not in trie:
       A__	=    {}
      A__	=    trie[char]
     A__	=    True




    def 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	str				)				->							tuple | list:




     '''simple docstring'''

     A__	=    self._trie
     for char in prefix:
      if char in trie:
       A__	=    trie[char]
      else:
       return []
     return self._elements(snake_case_				)




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			snake_case_						:	dict				)				->							tuple:




     '''simple docstring'''

     A__	=    []
     for c, v in d.items():
      A__	=    [" "] if c == END else [(c + s) for s in self._elements(snake_case_				)]
      result.extend(snake_case_				)
     return tuple(snake_case_				)






SCREAMING_SNAKE_CASE    =	Trie()
SCREAMING_SNAKE_CASE    =	("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
      trie.insert_word(word)







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		tuple:
 A__	=    trie.find_word(lowercase_						)
 return tuple(string + word for word in suffixes						)







def   _SCREAMING_SNAKE_CASE       (							)						->		None:
 print(autocomplete_using_trie("de"						)						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()





