

'''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 						__lowerCamelCase	(       )	->							str:






						"""simple docstring"""





						A__					:       List[str]     =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()


__snake_case			:     Optional[Any]										=      load('accuracy')
def 						__lowerCamelCase	(       __snake_case					:		Tuple	)	->							Dict:






						"""simple docstring"""





						A__					:       int     =eval_pred
						A__					:       int     =np.argmax(__snake_case,		axis=1	)
						return metric.compute(predictions=__snake_case,		references=__snake_case	)



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			def __init__(				self	:						Optional[Any]		,      lowerCAmelCase_	:						Optional[Any]			)      ->		None:





									'''simple docstring'''
									super().__init__()
									A__					:       int     =trainer



			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						List[Any]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						str		,      **lowerCAmelCase_	:						Union[str, Any]			)      ->		Optional[Any]:





									'''simple docstring'''
									if control.should_evaluate:
															A__					:       Optional[int]     =deepcopy(lowerCAmelCase_			)
															self._trainer.evaluate(eval_dataset=self._trainer.train_dataset		,      metric_key_prefix="""train"""			)
															return control_copy


def 						__lowerCamelCase	(       )	->							Optional[int]:






						"""simple docstring"""





						A__					:       Dict     =get_args()
						set_seed(args.seed	)

						A__					:       Tuple     =load_dataset("""codeparrot/codecomplex""",		split="""train"""	)
						A__					:       Dict     =dataset.train_test_split(test_size=0.2	)
						A__					:       Union[str, Any]     =train_test["""test"""].train_test_split(test_size=0.5	)
						A__					:       List[Any]     =DatasetDict(
						    {
						        """train""": train_test["""train"""],
						        """test""": test_validation["""train"""],
						        """valid""": test_validation["""test"""],
						    }	)

						print("""Loading tokenizer and model"""	)
						A__					:       List[Any]     =AutoTokenizer.from_pretrained(args.model_ckpt	)
						A__					:       Any     =tokenizer.eos_token
						A__					:       Dict     =AutoModelForSequenceClassification.from_pretrained(args.model_ckpt,		num_labels=7	)
						A__					:       Tuple     =model.config.eos_token_id

						if args.freeze:
												for param in model.roberta.parameters():
																		A__					:       List[Any]     =False

						A__					:       Optional[Any]     =ClassLabel(num_classes=7,		names=list(set(train_test_validation["""train"""]["""complexity"""]	)	)	)

						def tokenize(__snake_case					:		List[str]	):
												A__					:       Optional[int]     =tokenizer(example["""src"""],		truncation=__snake_case,		max_length=1_024	)
												A__					:       Any     =labels.straint(example["""complexity"""]	)
												return {
												    "input_ids": inputs["input_ids"],
												    "attention_mask": inputs["attention_mask"],
												    "label": label,
												}

						A__					:       List[Any]     =train_test_validation.map(
						    __snake_case,		batched=__snake_case,		remove_columns=train_test_validation["""train"""].column_names,		)
						A__					:       Optional[int]     =DataCollatorWithPadding(tokenizer=__snake_case	)

						A__					:       int     =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""",		)

						A__					:       List[Any]     =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 warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	['image_processor', 'tokenizer']
			__snake_case        =	'FlavaImageProcessor'
			__snake_case        =	('BertTokenizer', 'BertTokenizerFast')


			def __init__(				self	:						Optional[int]		,      lowerCAmelCase_	:						List[str]=None		,      lowerCAmelCase_	:						Optional[Any]=None		,      **lowerCAmelCase_	:						Optional[Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Any     =None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""		,      lowerCAmelCase_		,      )
															A__					:       Optional[Any]     =kwargs.pop("""feature_extractor"""			)

									A__					:       List[Any]     =image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""			)
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""			)

									super().__init__(lowerCAmelCase_		,      lowerCAmelCase_			)
									A__					:       int     =self.image_processor


			def __call__(				self	:						Union[str, Any]		,      lowerCAmelCase_	:						Optional[ImageInput] = None		,      lowerCAmelCase_	:						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Union[bool, str, PaddingStrategy] = False		,      lowerCAmelCase_	:						Union[bool, str, TruncationStrategy] = False		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						int = 0		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						Optional[bool] = None		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = False		,      lowerCAmelCase_	:						bool = True		,      lowerCAmelCase_	:						Optional[Union[str, TensorType]] = None		,      **lowerCAmelCase_	:						Optional[int]		,      )      ->		str:





									'''simple docstring'''

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""			)

									if text is not None:
															A__					:       int     =self.tokenizer(
															    text=lowerCAmelCase_		,      add_special_tokens=lowerCAmelCase_		,      padding=lowerCAmelCase_		,      truncation=lowerCAmelCase_		,      max_length=lowerCAmelCase_		,      stride=lowerCAmelCase_		,      pad_to_multiple_of=lowerCAmelCase_		,      return_token_type_ids=lowerCAmelCase_		,      return_attention_mask=lowerCAmelCase_		,      return_overflowing_tokens=lowerCAmelCase_		,      return_special_tokens_mask=lowerCAmelCase_		,      return_offsets_mapping=lowerCAmelCase_		,      return_length=lowerCAmelCase_		,      verbose=lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_		,      )
									if images is not None:
															A__					:       List[str]     =self.image_processor(
															    lowerCAmelCase_		,      return_image_mask=lowerCAmelCase_		,      return_codebook_pixels=lowerCAmelCase_		,      return_tensors=lowerCAmelCase_		,      **lowerCAmelCase_		,      )

									if text is not None and images is not None:
															encoding.update(lowerCAmelCase_			)
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**lowerCAmelCase_			)		,      tensor_type=lowerCAmelCase_			)


			def lowercase__						(				self	:						Any		,      *lowerCAmelCase_	:						str		,      **lowerCAmelCase_	:						Optional[int]			)      ->		str:





									'''simple docstring'''
									return self.tokenizer.batch_decode(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]		,      *lowerCAmelCase_	:						List[Any]		,      **lowerCAmelCase_	:						Any			)      ->		Tuple:





									'''simple docstring'''
									return self.tokenizer.decode(*lowerCAmelCase_		,      **lowerCAmelCase_			)


			@property
			def lowercase__						(				self	:						Tuple			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Any     =self.tokenizer.model_input_names
									A__					:       Optional[Any]     =self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)


			@property
			def lowercase__						(				self	:						Tuple			)      ->		int:





									'''simple docstring'''
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,      lowerCAmelCase_		,      )
									return self.image_processor_class



			@property
			def lowercase__						(				self	:						Tuple			)      ->		Dict:





									'''simple docstring'''
									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,      lowerCAmelCase_		,      )
									return self.image_processor

