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				(    )		->						Union[str, Any]:




       '''simple docstring'''






       _A=	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=10   )
       parser.add_argument('--weight_decay'       ,					type=lowerCAmelCase_       ,					default=0.01   )
       parser.add_argument('--output_dir'       ,					type=lowerCAmelCase_       ,					default='./results'   )
       return parser.parse_args()


UpperCAmelCase_								=       load('''accuracy''')

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						List[str]:




       '''simple docstring'''






       _A,			_A=	eval_pred
       _A=	np.argmax(lowerCAmelCase_       ,					axis=1   )
       return metric.compute(predictions=lowerCAmelCase_       ,					references=lowerCAmelCase_   )





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__       ):
            super().__init__()
            _A=	trainer





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      **lowerCAmelCase__       ):
            if control.should_evaluate:
                   _A=	deepcopy(lowerCAmelCase__       )
                   self._trainer.evaluate(eval_dataset=self._trainer.train_dataset				,      metric_key_prefix='train'       )
                   return control_copy


def 							UpperCamelCase				(    )		->						List[Any]:




       '''simple docstring'''






       _A=	get_args()
       set_seed(args.seed   )

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

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

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

       _A=	ClassLabel(num_classes=7       ,					names=list(set(train_test_validation['train']['complexity']   )   )   )

       def tokenize(lowerCAmelCase_   ):
              _A=	tokenizer(example['src']       ,					truncation=lowerCAmelCase_       ,					max_length=10_24   )
              _A=	labels.straint(example['complexity']   )
              return {
                  "input_ids": inputs["input_ids"],
                  "attention_mask": inputs["attention_mask"],
                  "label": label,
              }

       _A=	train_test_validation.map(
           lowerCAmelCase_       ,					batched=lowerCAmelCase_       ,					remove_columns=train_test_validation['train'].column_names       ,					)
       _A=	DataCollatorWithPadding(tokenizer=lowerCAmelCase_   )

       _A=	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=	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()
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]		="""convbert"""



     def __init__(					self				,      lowerCAmelCase__=30522				,      lowerCAmelCase__=768				,      lowerCAmelCase__=12				,      lowerCAmelCase__=12				,      lowerCAmelCase__=3072				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=512				,      lowerCAmelCase__=2				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=1E-12				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__=768				,      lowerCAmelCase__=2				,      lowerCAmelCase__=9				,      lowerCAmelCase__=1				,      lowerCAmelCase__=None				,      **lowerCAmelCase__				,      ):
            super().__init__(
                pad_token_id=lowerCAmelCase__				,      bos_token_id=lowerCAmelCase__				,      eos_token_id=lowerCAmelCase__				,      **lowerCAmelCase__				,      )

            _A=	vocab_size
            _A=	hidden_size
            _A=	num_hidden_layers
            _A=	num_attention_heads
            _A=	intermediate_size
            _A=	hidden_act
            _A=	hidden_dropout_prob
            _A=	attention_probs_dropout_prob
            _A=	max_position_embeddings
            _A=	type_vocab_size
            _A=	initializer_range
            _A=	layer_norm_eps
            _A=	embedding_size
            _A=	head_ratio
            _A=	conv_kernel_size
            _A=	num_groups
            _A=	classifier_dropout





class      lowerCAmelCase						(					_a			):



     @property
     def 			a__  (					self       ):
            if self.task == "multiple-choice":
                   _A=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   _A=	{0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('input_ids', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                    ('token_type_ids', dynamic_axis),
                ]       )