
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    SCREAMING_SNAKE_CASE__		( )    ->   List[Any]:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	:							List[Any]											=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=0.01							)
							parser.add_argument('--output_dir'       ,type=_A       ,default='./results'							)
							return parser.parse_args()


__SCREAMING_SNAKE_CASE						       =						load('accuracy')
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			Tuple							)    ->   Dict:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_	:							Optional[Any]											=eval_pred
							SCREAMING_SNAKE_CASE_	:							Any											=np.argmax(_A       ,axis=1							)
							return metric.compute(predictions=_A       ,references=_A							)





class 							lowerCAmelCase_						(    a_							):




			'''simple docstring'''


			def __init__(	self    ,							__UpperCAmelCase  ):
										super().__init__()
										SCREAMING_SNAKE_CASE_	:							Dict											=trainer







			def      __lowerCamelCase    (	self    ,							__UpperCAmelCase    ,							__UpperCAmelCase    ,							__UpperCAmelCase    ,							**__UpperCAmelCase  ):
										if control.should_evaluate:
																	SCREAMING_SNAKE_CASE_	:							Dict											=deepcopy(__SCREAMING_SNAKE_CASE  )
																	self._trainer.evaluate(eval_dataset=self._trainer.train_dataset    ,							metric_key_prefix='train'  )
																	return control_copy




def    SCREAMING_SNAKE_CASE__		( )    ->   Union[str, Any]:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	:							List[Any]											=get_args()
							set_seed(args.seed							)

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

							print('Loading tokenizer and model'							)
							SCREAMING_SNAKE_CASE_	:							str											=AutoTokenizer.from_pretrained(args.model_ckpt							)
							SCREAMING_SNAKE_CASE_	:							Optional[int]											=tokenizer.eos_token
							SCREAMING_SNAKE_CASE_	:							List[Any]											=AutoModelForSequenceClassification.from_pretrained(args.model_ckpt       ,num_labels=7							)
							SCREAMING_SNAKE_CASE_	:							Dict											=model.config.eos_token_id

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

							SCREAMING_SNAKE_CASE_	:							Optional[int]											=ClassLabel(num_classes=7       ,names=list(set(train_test_validation['train']['complexity']							)							)							)

							def tokenize(lowerCAmelCase_				:			str							):
														SCREAMING_SNAKE_CASE_	:							List[Any]											=tokenizer(example['src']       ,truncation=_A       ,max_length=1024							)
														SCREAMING_SNAKE_CASE_	:							str											=labels.straint(example['complexity']							)
														return {
														    "input_ids": inputs["input_ids"],
														    "attention_mask": inputs["attention_mask"],
														    "label": label,
														}

							SCREAMING_SNAKE_CASE_	:							str											=train_test_validation.map(
							    _A       ,batched=_A       ,remove_columns=train_test_validation['train'].column_names       ,)
							SCREAMING_SNAKE_CASE_	:							Any											=DataCollatorWithPadding(tokenizer=_A							)

							SCREAMING_SNAKE_CASE_	:							Optional[Any]											=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'       ,)

							SCREAMING_SNAKE_CASE_	:							int											=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()






import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	torch.load(_A				,					map_location="cpu"       )

 __a							=	chkpt["model"]

 # We have the base model one level deeper than the original XLM repository
 __a							=	{}
 for k, v in state_dict.items():
  if "pred_layer" in k:
   __a							=	v
  else:
   __a							=	v

 __a							=	chkpt["params"]
 __a							=	{n: v for n, v in config.items() if not isinstance(_A				,					(torch.FloatTensor, numpy.ndarray)       )}

 __a							=	chkpt["dico_word2id"]
 __a							=	{s + "</w>" if s.find("@@"       ) == -1 and i > 13 else s.replace("@@"				,					""       ): i for s, i in vocab.items()}

 # Save pytorch-model
 __a							=	pytorch_dump_folder_path + "/" + WEIGHTS_NAME
 __a							=	pytorch_dump_folder_path + "/" + CONFIG_NAME
 __a							=	pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

 print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""       )
 torch.save(_A				,					_A       )

 print(f"""Save configuration file to {pytorch_config_dump_path}"""       )
 with open(_A				,					"w"				,					encoding="utf-8"       ) as f:
  f.write(json.dumps(_A				,					indent=2       ) + "\n"       )

 print(f"""Save vocab file to {pytorch_config_dump_path}"""       )
 with open(_A				,					"w"				,					encoding="utf-8"       ) as f:
  f.write(json.dumps(_A				,					indent=2       ) + "\n"       )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)


