




"""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     a__ (				)						->					Any:
			lowerCamelCase			      =					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							:			List[Any]     							=							load("""accuracy""")



def     a__ (				snake_case__				)						->					str:
			lowerCamelCase      ,   lowerCamelCase			      =					eval_pred
			lowerCamelCase			      =					np.argmax(snake_case__       , axis=1				)
			return metric.compute(predictions=snake_case__       , references=snake_case__				)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''





				def __init__(       self		, _a ):





							"""simple docstring"""
							super().__init__()
							lowerCamelCase			      =					trainer





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a		, **_a ):





							"""simple docstring"""
							if control.should_evaluate:
										lowerCamelCase			      =					deepcopy(_a )
										self._trainer.evaluate(eval_dataset=self._trainer.train_dataset		, metric_key_prefix="""train""" )
										return control_copy




def     a__ (				)						->					Optional[Any]:
			lowerCamelCase			      =					get_args()
			set_seed(args.seed				)

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

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

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

			lowerCamelCase			      =					ClassLabel(num_classes=7       , names=list(set(train_test_validation["""train"""]["""complexity"""]				)				)				)

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

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

			lowerCamelCase			      =					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"""       , )

			lowerCamelCase			      =					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 operator as op


lowerCAmelCase							:			Dict     							=							"""scaler.pt"""
lowerCAmelCase							:			Tuple     							=							"""pytorch_model"""
lowerCAmelCase							:			Union[str, Any]     							=							"""random_states"""
lowerCAmelCase							:			Union[str, Any]     							=							"""optimizer"""
lowerCAmelCase							:			Dict     							=							"""scheduler"""
lowerCAmelCase							:			int     							=							"""pytorch_model.bin"""
lowerCAmelCase							:			str     							=							"""pytorch_model.bin.index.json"""
lowerCAmelCase							:			Union[str, Any]     							=							"""model.safetensors"""
lowerCAmelCase							:			List[Any]     							=							"""model.safetensors.index.json"""
lowerCAmelCase							:			List[Any]     							=							"""1.10.2"""
lowerCAmelCase							:			Any     							=							"""py38"""
lowerCAmelCase							:			Optional[int]     							=							"""4.17.0"""
lowerCAmelCase							:			str     							=							["""ml.p3.16xlarge""", """ml.p3dn.24xlarge""", """ml.p4dn.24xlarge"""]
lowerCAmelCase							:			Tuple     							=							["""FULL_SHARD""", """SHARD_GRAD_OP""", """NO_SHARD""", """HYBRID_SHARD""", """HYBRID_SHARD_ZERO2"""]
lowerCAmelCase							:			List[Any]     							=							["""TRANSFORMER_BASED_WRAP""", """SIZE_BASED_WRAP""", """NO_WRAP"""]
lowerCAmelCase							:			List[str]     							=							["""BACKWARD_PRE""", """BACKWARD_POST""", """NO_PREFETCH"""]
lowerCAmelCase							:			List[str]     							=							["""FULL_STATE_DICT""", """LOCAL_STATE_DICT""", """SHARDED_STATE_DICT"""]
lowerCAmelCase							:			Any     							=							"""2.0.1"""
lowerCAmelCase							:			List[Any]     							=							["""pdsh""", """standard""", """openmpi""", """mvapich"""]
lowerCAmelCase							:			Union[str, Any]     							=							["""default""", """reduce-overhead""", """max-autotune"""]

lowerCAmelCase							:			Optional[int]     							=							{""">""": op.gt, """>=""": op.ge, """==""": op.eq, """!=""": op.ne, """<=""": op.le, """<""": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
lowerCAmelCase							:			Union[str, Any]     							=							[
    """nnodes""",
    """nproc_per_node""",
    """rdzv_backend""",
    """rdzv_endpoint""",
    """rdzv_id""",
    """rdzv_conf""",
    """standalone""",
    """max_restarts""",
    """monitor_interval""",
    """start_method""",
    """role""",
    """module""",
    """m""",
    """no_python""",
    """run_path""",
    """log_dir""",
    """r""",
    """redirects""",
    """t""",
    """tee""",
    """node_rank""",
    """master_addr""",
    """master_port""",
]

lowerCAmelCase							:			List[str]     							=							["""DEEPSPEED""", """MULTI_GPU""", """FSDP""", """MEGATRON_LM"""]
lowerCAmelCase							:			Optional[Any]     							=							["""DEEPSPEED""", """MULTI_XPU""", """FSDP"""]



