


"""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  __snake_case       (   )      ->		str:







		"""simple docstring"""





		A					       = argparse.ArgumentParser()
		parser.add_argument('--model_ckpt' ,			type=UpperCamelCase__ ,			default='microsoft/unixcoder-base-nine'	)
		parser.add_argument('--num_epochs' ,			type=UpperCamelCase__ ,			default=5	)
		parser.add_argument('--batch_size' ,			type=UpperCamelCase__ ,			default=6	)
		parser.add_argument('--gradient_accumulation_steps' ,			type=UpperCamelCase__ ,			default=1	)
		parser.add_argument('--freeze' ,			type=UpperCamelCase__ ,			default=UpperCamelCase__	)
		parser.add_argument('--learning_rate' ,			type=UpperCamelCase__ ,			default=5E-4	)
		parser.add_argument('--seed' ,			type=UpperCamelCase__ ,			default=0	)
		parser.add_argument('--lr_scheduler_type' ,			type=UpperCamelCase__ ,			default='cosine'	)
		parser.add_argument('--num_warmup_steps' ,			type=UpperCamelCase__ ,			default=10	)
		parser.add_argument('--weight_decay' ,			type=UpperCamelCase__ ,			default=0.0_1	)
		parser.add_argument('--output_dir' ,			type=UpperCamelCase__ ,			default='./results'	)
		return parser.parse_args()


UpperCamelCase						:				Dict             =    load("accuracy")







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		A       , A					       = eval_pred
		A					       = np.argmax(UpperCamelCase__ ,			axis=1	)
		return metric.compute(predictions=UpperCamelCase__ ,			references=UpperCamelCase__	)



class 			lowerCamelCase__						(   UpperCAmelCase_			):







						def __init__(							self   :    Tuple			,			_lowercase   :    Union[str, Any]		):
								super().__init__()
								A					       = trainer







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







def  __snake_case       (   )      ->		int:







		"""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(UpperCamelCase__	):
				A					       = tokenizer(example['src'] ,			truncation=UpperCamelCase__ ,			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(
		    UpperCamelCase__ ,			batched=UpperCamelCase__ ,			remove_columns=train_test_validation['train'].column_names ,			)
		A					       = DataCollatorWithPadding(tokenizer=UpperCamelCase__	)

		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=UpperCamelCase__ ,			args=UpperCamelCase__ ,			train_dataset=tokenized_datasets['train'] ,			eval_dataset=tokenized_datasets['valid'] ,			tokenizer=UpperCamelCase__ ,			data_collator=UpperCamelCase__ ,			compute_metrics=UpperCamelCase__ ,			)

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


if __name__ == "__main__":
	main()









"""simple docstring"""


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
	import tensorflow as tf


UpperCamelCase						:				Optional[Any]             =      logging.get_logger(__name__)



@dataclass
class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     [
						    """no_inference""",
						    """no_cuda""",
						    """no_tpu""",
						    """no_speed""",
						    """no_memory""",
						    """no_env_print""",
						    """no_multi_process""",
						]







						def __init__(							self   :    str			,			**_lowercase   :    Union[str, Any]		):
								for deprecated_arg in self.deprecated_args:
										if deprecated_arg in kwargs:
												A					       = deprecated_arg[3:]
												A					       = not kwargs.pop(_lowercase		)
												logger.warning(
												    f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
												    f' {positive_arg}={kwargs[positive_arg]}'		)
								A					       = kwargs.pop('tpu_name'			,			self.tpu_name		)
								A					       = kwargs.pop('device_idx'			,			self.device_idx		)
								A					       = kwargs.pop('eager_mode'			,			self.eager_mode		)
								A					       = kwargs.pop('use_xla'			,			self.use_xla		)
								super().__init__(**_lowercase		)

						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={"""help""": """Name of TPU"""}							,    )
						lowerCAmelCase       =     field(
						    default=0							,    metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}							,    )
						lowerCAmelCase       =     field(default=UpperCAmelCase_							,    metadata={"""help""": """Benchmark models in eager model."""}			)
						lowerCAmelCase       =     field(
						    default=UpperCAmelCase_							,    metadata={
						        """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
						    }							,    )







						@cached_property
						def __a						(							self   :    Optional[Any]		):
								requires_backends(self			,			['tf']		)
								A					       = None
								if self.tpu:
										try:
												if self.tpu_name:
														A					       = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name		)
												else:
														A					       = tf.distribute.cluster_resolver.TPUClusterResolver()
										except ValueError:
												A					       = None
								return tpu







						@cached_property
						def __a						(							self   :    Dict		):
								requires_backends(self			,			['tf']		)
								if self.is_tpu:
										tf.config.experimental_connect_to_cluster(self._setup_tpu		)
										tf.tpu.experimental.initialize_tpu_system(self._setup_tpu		)

										A					       = tf.distribute.TPUStrategy(self._setup_tpu		)
								else:
										# currently no multi gpu is allowed
										if self.is_gpu:
												# TODO: Currently only single GPU is supported
												tf.config.set_visible_devices(self.gpu_list[self.device_idx]			,			'GPU'		)
												A					       = tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}'		)
										else:
												tf.config.set_visible_devices([]			,			'GPU'		)  # disable GPU
												A					       = tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}'		)

								return strategy







						@property
						def __a						(							self   :    List[Any]		):
								requires_backends(self			,			['tf']		)
								return self._setup_tpu is not None







						@property
						def __a						(							self   :    Optional[Any]		):
								requires_backends(self			,			['tf']		)
								return self._setup_strategy







						@property
						def __a						(							self   :    str		):
								requires_backends(self			,			['tf']		)
								return tf.config.list_physical_devices('GPU'		)







						@property
						def __a						(							self   :    Any		):
								requires_backends(self			,			['tf']		)
								if self.cuda:
										return len(self.gpu_list		)
								return 0







						@property
						def __a						(							self   :    Dict		):
								return self.n_gpu > 0





