
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_ (				):
				lowerCamelCase_						=			argparse.ArgumentParser()
				parser.add_argument("--model_ckpt"	, type=lowerCamelCase__	, default="microsoft/unixcoder-base-nine"    )
				parser.add_argument("--num_epochs"	, type=lowerCamelCase__	, default=5    )
				parser.add_argument("--batch_size"	, type=lowerCamelCase__	, default=6    )
				parser.add_argument("--gradient_accumulation_steps"	, type=lowerCamelCase__	, default=1    )
				parser.add_argument("--freeze"	, type=lowerCamelCase__	, default=lowerCamelCase__    )
				parser.add_argument("--learning_rate"	, type=lowerCamelCase__	, default=5e-4    )
				parser.add_argument("--seed"	, type=lowerCamelCase__	, default=0    )
				parser.add_argument("--lr_scheduler_type"	, type=lowerCamelCase__	, default="cosine"    )
				parser.add_argument("--num_warmup_steps"	, type=lowerCamelCase__	, default=1_0    )
				parser.add_argument("--weight_decay"	, type=lowerCamelCase__	, default=0.01    )
				parser.add_argument("--output_dir"	, type=lowerCamelCase__	, default="./results"    )
				return parser.parse_args()


__A				=load('''accuracy''')





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_	,    lowerCamelCase_						=			eval_pred
				lowerCamelCase_						=			np.argmax(lowerCamelCase__	, axis=1    )
				return metric.compute(predictions=lowerCamelCase__	, references=lowerCamelCase__    )




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase )				->       None:
					super().__init__()
					lowerCamelCase_						=			trainer



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  **lowercase )				->       Dict:
					if control.should_evaluate:
									lowerCamelCase_						=			deepcopy(lowercase )
									self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,  metric_key_prefix="train" )
									return control_copy





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

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

				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=lowerCamelCase__	, args=lowerCamelCase__	, train_dataset=tokenized_datasets["train"]	, eval_dataset=tokenized_datasets["valid"]	, tokenizer=lowerCamelCase__	, data_collator=lowerCamelCase__	, compute_metrics=lowerCamelCase__	, )

				print("Training..."    )
				trainer.add_callback(CustomCallback(lowerCamelCase__    )    )
				trainer.train()


if __name__ == "__main__":
			main()





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Dict	    =    "ChineseCLIPImageProcessor"
							UpperCAmelCase__       :						List[str]	    =    ("BertTokenizer", "BertTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    Optional[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.'''       ,							_a       ,							)
																	_a	:    Tuple         =		kwargs.pop('''feature_extractor'''       )

												_a	:    Tuple         =		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__(_a       ,							_a       )
												_a	:    List[str]         =		self.image_processor


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    int:

												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	:    List[str]         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    Union[str, Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Union[str, Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Any:
												return self.tokenizer.decode(*_a       ,							**_a       )


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Any         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )




							@property
							def 				__lowercase				(			self       )    ->    Dict:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,							_a       ,							)
												return self.image_processor_class



