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 					_snake_case	(  ):
					_UpperCamelCase			     =     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()


_lowerCAmelCase       =		load("accuracy")
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase     ,    _UpperCamelCase			     =     eval_pred
					_UpperCamelCase			     =     np.argmax(__snake_case  ,     axis=1						)
					return metric.compute(predictions=__snake_case  ,     references=__snake_case						)
class     lowerCAmelCase_     (       __lowercase ):







							def __init__(   self      :     Optional[int] ,			_A      :     Any		):
												super().__init__()
												_UpperCamelCase			     =     trainer







							def  UpperCamelCase_						(   self      :     int ,			_A      :     List[str] ,			_A      :     List[str] ,			_A      :     Union[str, Any] ,			**_A      :     Tuple		):
												if control.should_evaluate:
																	_UpperCamelCase			     =     deepcopy(_A		)
																	self._trainer.evaluate(eval_dataset=self._trainer.train_dataset ,			metric_key_prefix='''train'''		)
																	return control_copy




def 					_snake_case	(  ):
					_UpperCamelCase			     =     get_args()
					set_seed(args.seed						)

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

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

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

					_UpperCamelCase			     =     ClassLabel(num_classes=7  ,     names=list(set(train_test_validation['''train''']['''complexity''']						)						)						)

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

					_UpperCamelCase			     =     train_test_validation.map(
					    __snake_case  ,     batched=__snake_case  ,     remove_columns=train_test_validation['''train'''].column_names  ,     )
					_UpperCamelCase			     =     DataCollatorWithPadding(tokenizer=__snake_case						)

					_UpperCamelCase			     =     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			     =     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()







"""simple docstring"""





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:Dict      ):
											__lowerCamelCase   :   Union[str, Any]       =inspect.getfile(accelerate.test_utils      )
											__lowerCamelCase   :   Union[str, Any]       =os.path.sep.join(
											    mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']      )

											from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

											__lowerCamelCase   :   List[str]       =test_metrics




				@require_cpu
				def        __lowercase	(  self	:Optional[Any]      ):
											debug_launcher(self.test_metrics.main    ,     num_processes=1      )




				@require_cpu
				def        __lowercase	(  self	:int      ):
											debug_launcher(self.test_metrics.main      )




				@require_single_gpu
				def        __lowercase	(  self	:int      ):
											self.test_metrics.main()




				@require_multi_gpu
				def        __lowercase	(  self	:Dict      ):
											print(f'Found {torch.cuda.device_count()} devices.'      )
											__lowerCamelCase   :   Union[str, Any]       =['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
											with patch_environment(omp_num_threads=1      ):
																		execute_subprocess_async(__lowercase    ,     env=os.environ.copy()      )




