'''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			(   )    ->   Any:
							lowercase_      :    int       		= 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=10				)
							parser.add_argument("""--weight_decay"""   ,   type=UpperCAmelCase__   ,   default=0.01				)
							parser.add_argument("""--output_dir"""   ,   type=UpperCAmelCase__   ,   default="""./results"""				)
							return parser.parse_args()


_lowercase		:				Any	       =		load("accuracy")




def 							lowerCamelCase			(   UpperCAmelCase__ :      Optional[Any]				)    ->   Tuple:
							lowercase_		,		lowercase_      :    str       		= eval_pred
							lowercase_      :    Union[str, Any]       		= np.argmax(UpperCAmelCase__   ,   axis=1				)
							return metric.compute(predictions=UpperCAmelCase__   ,   references=UpperCAmelCase__				)





class 					__magic_name__		(					_UpperCAmelCase):







			def __init__(			self       :						Optional[int]       ,    lowercase_       :						Tuple					):
										super().__init__()
										lowercase_      :    List[Any]       		= trainer







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						Optional[Any]       ,    lowercase_       :						Union[str, Any]       ,    lowercase_       :						Tuple       ,    **lowercase_       :						Dict					):
										if control.should_evaluate:
																	lowercase_      :    Union[str, Any]       		= deepcopy(lowercase_					)
																	self._trainer.evaluate(eval_dataset=self._trainer.train_dataset       ,    metric_key_prefix="""train"""					)
																	return control_copy




def 							lowerCamelCase			(   )    ->   List[str]:
							lowercase_      :    Union[str, Any]       		= get_args()
							set_seed(args.seed				)

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

							print("""Loading tokenizer and model"""				)
							lowercase_      :    int       		= AutoTokenizer.from_pretrained(args.model_ckpt				)
							lowercase_      :    Optional[int]       		= tokenizer.eos_token
							lowercase_      :    Any       		= AutoModelForSequenceClassification.from_pretrained(args.model_ckpt   ,   num_labels=7				)
							lowercase_      :    Optional[Any]       		= model.config.eos_token_id

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

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

							def tokenize(UpperCAmelCase__ :      Optional[Any]				):
														lowercase_      :    Any       		= tokenizer(example["""src"""]   ,   truncation=UpperCAmelCase__   ,   max_length=1024				)
														lowercase_      :    int       		= labels.straint(example["""complexity"""]				)
														return {
														    "input_ids": inputs["input_ids"],
														    "attention_mask": inputs["attention_mask"],
														    "label": label,
														}

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

							lowercase_      :    Dict       		= 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_      :    List[Any]       		= 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()






'''simple docstring'''




import argparse


_lowercase		:				Optional[int]	       =		"docs/source/_static/js/custom.js"




def 							lowerCamelCase			(   UpperCAmelCase__ :      Tuple				)    ->   Dict:
							with open(UpperCAmelCase__   ,   encoding="""utf-8"""   ,   newline="""\n"""				) as f:
														lowercase_      :    Optional[int]       		= f.readlines()
							lowercase_      :    Tuple       		= 0

							# First let's put the right version
							while not lines[index].startswith("""const stableVersion ="""				):
														index += 1
							lowercase_      :    Optional[Any]       		= F'''const stableVersion = "v{version}"\n'''

							# Then update the dictionary
							while not lines[index].startswith("""const versionMapping = {"""				):
														index += 1

							# We go until the end
							while not lines[index].startswith("""}"""				):
														index += 1
							# We add the new version at the end
							lines[index - 1] += F'''    "v{version}": "v{version}",\n'''

							with open(UpperCAmelCase__   ,   """w"""   ,   encoding="""utf-8"""   ,   newline="""\n"""				) as f:
														f.writelines(UpperCAmelCase__				)


if __name__ == "__main__":
						_lowercase		:				Union[str, Any]	       =		argparse.ArgumentParser()
						parser.add_argument("--version", help="Release version.")
						_lowercase		:				Dict	       =		parser.parse_args()
						update_custom_js(args.version)





