



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       (					)   ->					int:


				"""simple docstring"""

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


A__								=						load('''accuracy''')



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Dict:


				"""simple docstring"""

				snake_case__		, snake_case__	:      Optional[int]     		=	eval_pred
				snake_case__	:      Union[str, Any]     		=	np.argmax(__lowerCAmelCase  ,  axis=1      )
				return metric.compute(predictions=__lowerCAmelCase  ,  references=__lowerCAmelCase      )


class       a							(			__lowerCamelCase				):



		def __init__(     self     :Optional[Any]   ,__lowercase     :Optional[int]  ):
						super().__init__()
						snake_case__	:      List[str]     		=	trainer






		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :Dict   ,__lowercase     :List[Any]   ,__lowercase     :Any   ,**__lowercase     :Optional[Any]  ):
						if control.should_evaluate:
										snake_case__	:      Any     		=	deepcopy(__lowercase  )
										self._trainer.evaluate(eval_dataset=self._trainer.train_dataset   ,metric_key_prefix='''train'''  )
										return control_copy



def 			_lowerCAmelCase       (					)   ->					List[Any]:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	get_args()
				set_seed(args.seed      )

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

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

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

				snake_case__	:      List[str]     		=	ClassLabel(num_classes=7  ,  names=list(set(train_test_validation['''train''']['''complexity''']      )      )      )

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

				snake_case__	:      str     		=	train_test_validation.map(
				    __lowerCAmelCase  ,  batched=__lowerCAmelCase  ,  remove_columns=train_test_validation['''train'''].column_names  ,  )
				snake_case__	:      List[Any]     		=	DataCollatorWithPadding(tokenizer=__lowerCAmelCase      )

				snake_case__	:      int     		=	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=__lowerCAmelCase  ,  args=__lowerCAmelCase  ,  train_dataset=tokenized_datasets['''train''']  ,  eval_dataset=tokenized_datasets['''valid''']  ,  tokenizer=__lowerCAmelCase  ,  data_collator=__lowerCAmelCase  ,  compute_metrics=__lowerCAmelCase  ,  )

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


if __name__ == "__main__":
				main()






import math
import sys



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Optional[Any]     		=	''''''
				try:
								with open(__lowerCAmelCase  ,  '''rb'''      ) as binary_file:
												snake_case__	:      int     		=	binary_file.read()
								for dat in data:
												snake_case__	:      Any     		=	f"""{dat:08b}"""
												result += curr_byte
								return result
				except OSError:
								print('''File not accessible'''      )
								sys.exit()



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	{'''0''': '''0''', '''1''': '''1'''}
				snake_case__		, snake_case__	:      List[Any]     		=	'''''', ''''''
				snake_case__	:      Tuple     		=	len(__lowerCAmelCase      )

				for i in range(len(__lowerCAmelCase      )      ):
								curr_string += data_bits[i]
								if curr_string not in lexicon:
												continue

								snake_case__	:      Tuple     		=	lexicon[curr_string]
								result += last_match_id
								snake_case__	:      Any     		=	last_match_id + '''0'''

								if math.loga(__lowerCAmelCase      ).is_integer():
												snake_case__	:      Tuple     		=	{}
												for curr_key in list(__lowerCAmelCase      ):
																snake_case__	:      Union[str, Any]     		=	lexicon.pop(__lowerCAmelCase      )
												snake_case__	:      Optional[Any]     		=	new_lex

								snake_case__	:      Tuple     		=	last_match_id + '''1'''
								index += 1
								snake_case__	:      Dict     		=	''''''
				return result



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					None:


				"""simple docstring"""

				snake_case__	:      Dict     		=	8
				try:
								with open(__lowerCAmelCase  ,  '''wb'''      ) as opened_file:
												snake_case__	:      Union[str, Any]     		=	[
												    to_write[i : i + byte_length]
												    for i in range(0  ,  len(__lowerCAmelCase      )  ,  __lowerCAmelCase      )
												]

												if len(result_byte_array[-1]      ) % byte_length == 0:
																result_byte_array.append('''10000000'''      )
												else:
																result_byte_array[-1] += "1" + "0" * (
																    byte_length - len(result_byte_array[-1]      ) - 1
																)

												for elem in result_byte_array[:-1]:
																opened_file.write(int(__lowerCAmelCase  ,  2      ).to_bytes(1  ,  byteorder='''big'''      )      )
				except OSError:
								print('''File not accessible'''      )
								sys.exit()



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Any     		=	0
				for letter in data_bits:
								if letter == "1":
												break
								counter += 1

				snake_case__	:      Optional[int]     		=	data_bits[counter:]
				snake_case__	:      int     		=	data_bits[counter + 1 :]
				return data_bits



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					None:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	read_file_binary(__lowerCAmelCase      )
				snake_case__	:      List[str]     		=	remove_prefix(__lowerCAmelCase      )
				snake_case__	:      Any     		=	decompress_data(__lowerCAmelCase      )
				write_file_binary(__lowerCAmelCase  ,  __lowerCAmelCase      )


if __name__ == "__main__":
				compress(sys.argv[1], sys.argv[2])

