


'''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 			UpperCAmelCase_				(      ):
					lowercase_  							=			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=1_0  )
					parser.add_argument("""--weight_decay"""						,							type=UpperCAmelCase__						,							default=0.01  )
					parser.add_argument("""--output_dir"""						,							type=UpperCAmelCase__						,							default="""./results"""  )
					return parser.parse_args()


a					  =     load('accuracy')


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_							,					lowercase_  							=			eval_pred
					lowercase_  							=			np.argmax(UpperCAmelCase__						,							axis=1  )
					return metric.compute(predictions=UpperCAmelCase__						,							references=UpperCAmelCase__  )







class        UpperCamelCase__   (	__magic_name__   ):

					def __init__(     self       :   Tuple							,					UpperCamelCase__       :   int       ):






										'''simple docstring'''






										super().__init__()
										lowercase_  							=			trainer







					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   List[Any]							,					UpperCamelCase__       :   Optional[Any]							,					UpperCamelCase__       :   int							,					**UpperCamelCase__       :   str       ):






										'''simple docstring'''






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



def 			UpperCAmelCase_				(      ):
					lowercase_  							=			get_args()
					set_seed(args.seed  )

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

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

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

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

					def tokenize(UpperCAmelCase__  ):
										lowercase_  							=			tokenizer(example["""src"""]						,							truncation=UpperCAmelCase__						,							max_length=1_0_2_4  )
										lowercase_  							=			labels.straint(example["""complexity"""]  )
										return {
										    "input_ids": inputs["input_ids"],
										    "attention_mask": inputs["attention_mask"],
										    "label": label,
										}

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

					lowercase_  							=			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"""						,							)

					lowercase_  							=			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()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


a					  =     {
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a					  =     [
							    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TrOCRForCausalLM',
							    'TrOCRPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
							from .processing_trocr import TrOCRProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
							import sys

							a					  =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


