


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_(				)->		Tuple:
			UpperCamelCase__					=       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=0.0_1  )
			parser.add_argument("--output_dir"				,     type=_A				,     default="./results"  )
			return parser.parse_args()


__UpperCamelCase    		=       load('accuracy')
def        UpperCamelCase_(				_A   :int  )->		List[str]:
			UpperCamelCase__,      UpperCamelCase__					=       eval_pred
			UpperCamelCase__					=       np.argmax(_A				,     axis=1  )
			return metric.compute(predictions=_A				,     references=_A  )







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case     ):

						'''simple docstring'''


						super().__init__()
						UpperCamelCase__					=       trainer







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						**snake_case     ):

						'''simple docstring'''


						if control.should_evaluate:
									UpperCamelCase__					=       deepcopy(snake_case     )
									self._trainer.evaluate(eval_dataset=self._trainer.train_dataset      ,						metric_key_prefix="train"     )
									return control_copy



def        UpperCamelCase_(				)->		List[str]:
			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(_A   :str  ):
						UpperCamelCase__					=       tokenizer(example["src"]				,     truncation=_A				,     max_length=10_24  )
						UpperCamelCase__					=       labels.straint(example["complexity"]  )
						return {
						    "input_ids": inputs["input_ids"],
						    "attention_mask": inputs["attention_mask"],
						    "label": label,
						}

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

			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=0.0_1				,     metric_for_best_model="accuracy"				,     run_name="complexity-java"				,     report_to="wandb"				,     )

			UpperCamelCase__					=       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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       {
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					int    =		'data2vec-text'




			def __init__(							self      ,						snake_case=30522      ,						snake_case=768      ,						snake_case=12      ,						snake_case=12      ,						snake_case=3072      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=512      ,						snake_case=2      ,						snake_case=0.02      ,						snake_case=1E-1_2      ,						snake_case=1      ,						snake_case=0      ,						snake_case=2      ,						snake_case="absolute"      ,						snake_case=True      ,						snake_case=None      ,						**snake_case      ,						):

						'''simple docstring'''


						super().__init__(pad_token_id=snake_case      ,						bos_token_id=snake_case      ,						eos_token_id=snake_case      ,						**snake_case     )

						UpperCamelCase__					=       vocab_size
						UpperCamelCase__					=       hidden_size
						UpperCamelCase__					=       num_hidden_layers
						UpperCamelCase__					=       num_attention_heads
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       intermediate_size
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       max_position_embeddings
						UpperCamelCase__					=       type_vocab_size
						UpperCamelCase__					=       initializer_range
						UpperCamelCase__					=       layer_norm_eps
						UpperCamelCase__					=       position_embedding_type
						UpperCamelCase__					=       use_cache
						UpperCamelCase__					=       classifier_dropout







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						if self.task == "multiple-choice":
									UpperCamelCase__					=       {0: "batch", 1: "choice", 2: "sequence"}
						else:
									UpperCamelCase__					=       {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]     )


