



def        __lowerCAmelCase		(	__lowerCamelCase						:     Dict					, __lowerCamelCase						:     List[str]						)	->    Optional[Any]:
					__lowerCAmelCase						 =word.split()

					def justify(__lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Tuple						) -> str:
										__lowerCAmelCase						 =max_width - width
										__lowerCAmelCase						 =len(_A						)
										if len(_A						) == 1:
															# if there is only word in line
															# just insert overall_spaces_count for the remainder of line
															return line[0] + " " * overall_spaces_count
										else:
															__lowerCAmelCase						 =words_count - 1
															# num_spaces_between_words_list[i] : tells you to insert
															# num_spaces_between_words_list[i] spaces
															# after word on line[i]
															__lowerCAmelCase						 =spaces_to_insert_between_words * [
															    overall_spaces_count // spaces_to_insert_between_words
															]
															__lowerCAmelCase						 =(
															    overall_spaces_count % spaces_to_insert_between_words
															)
															# distribute spaces via round robin to the left words
															for i in range(_A						):
																				num_spaces_between_words_list[i] += 1
															__lowerCAmelCase						 =[]
															for i in range(_A						):
																				# add the word
																				aligned_words_list.append(line[i]						)
																				# add the spaces to insert
																				aligned_words_list.append(num_spaces_between_words_list[i] * """ """						)
															# just add the last word to the sentence
															aligned_words_list.append(line[-1]						)
															# join the aligned words list to form a justified line
															return "".join(_A						)

					__lowerCAmelCase						 =[]
					__lowerCAmelCase						 =[]
					__lowerCAmelCase						 =0
					for word in words:
										if width + len(_A						) + len(_A						) <= max_width:
															# keep adding words until we can fill out max_width
															# width = sum of length of all words (without overall_spaces_count)
															# len(word) = length of current word
															# len(line) = number of overall_spaces_count to insert between words
															line.append(_A						)
															width += len(_A						)
										else:
															# justify the line and add it to result
															answer.append(justify(_A					, _A					, _A						)						)
															# reset new line and new width
															__lowerCAmelCase ,						__lowerCAmelCase						 =[word], len(_A						)
					__lowerCAmelCase						 =max_width - width - len(_A						)
					answer.append(""" """.join(_A						) + (remaining_spaces + 1) * """ """						)
					return answer


if __name__ == "__main__":
				from doctest import testmod

				testmod()





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"""


 __a							=	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=0.01       )
 parser.add_argument("--output_dir"				,					type=_A				,					default="./results"       )
 return parser.parse_args()


SCREAMING_SNAKE_CASE			:						Tuple				  =		load("""accuracy""")



def 	__A		(      _A       ):



 """simple docstring"""


 __a							,  __a							=	eval_pred
 __a							=	np.argmax(_A				,					axis=1       )
 return metric.compute(predictions=_A				,					references=_A       )
class  A_			(							a_			):

  def __init__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   int							):
   super().__init__()
   __a							=	trainer

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				**__SCREAMING_SNAKE_CASE			:   int							):
   if control.should_evaluate:
    __a							=	deepcopy(__SCREAMING_SNAKE_CASE							)
    self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,				metric_key_prefix="train"							)
    return control_copy



def 	__A		(      ):



 """simple docstring"""


 __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(_A       ):
  __a							=	tokenizer(example["src"]				,					truncation=_A				,					max_length=1024       )
  __a							=	labels.straint(example["complexity"]       )
  return {
      "input_ids": inputs["input_ids"],
      "attention_mask": inputs["attention_mask"],
      "label": label,
  }

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

 __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=0.01				,					metric_for_best_model="accuracy"				,					run_name="complexity-java"				,					report_to="wandb"				,					)

 __a							=	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()


