






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 						__UpperCAmelCase			(     ):
							snake_case_				=							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()


lowercase  							=					load("accuracy")





def 						__UpperCAmelCase			(     a_):
							snake_case_			,		snake_case_				=							eval_pred
							snake_case_				=							np.argmax(a_							,     axis=1)
							return metric.compute(predictions=a_							,     references=a_)




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			a					)       ->    None:
								super().__init__()
								snake_case_				=							trainer






	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			**a					)       ->    List[Any]:
								if control.should_evaluate:
															snake_case_				=							deepcopy(a					)
															self._trainer.evaluate(eval_dataset=self._trainer.train_dataset       ,			metric_key_prefix='train'					)
															return control_copy






def 						__UpperCAmelCase			(     ):
							snake_case_				=							get_args()
							set_seed(args.seed)

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

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

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

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

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

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

							snake_case_				=							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_				=							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()













from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowercase  							=					{"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					["GPTNeoXTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							=					[
	    "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTNeoXForCausalLM",
	    "GPTNeoXForQuestionAnswering",
	    "GPTNeoXForSequenceClassification",
	    "GPTNeoXForTokenClassification",
	    "GPTNeoXLayer",
	    "GPTNeoXModel",
	    "GPTNeoXPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowercase  							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





