"""simple docstring"""







import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
       import jax







@require_flax
class  UpperCamelCase	(    lowercase_		,   unittest.TestCase						):
   lowercase							=						FlaxAutoencoderKL
   @property
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			4
        lowercase_			:       List[Any]									=			3
        lowercase_			:       Tuple									=			(32, 32)

        lowercase_			:       Dict									=			jax.random.PRNGKey(0    )
        lowercase_			:       Union[str, Any]									=			jax.random.uniform(__UpperCamelCase  ,((batch_size, num_channels) + sizes)    )

        return {"sample": image, "prng_key": prng_key}







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


        '''simple docstring'''


        lowercase_			:       List[str]									=			{
            'block_out_channels': [32, 64],
            'in_channels': 3,
            'out_channels': 3,
            'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
            'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
            'latent_channels': 4,
        }
        lowercase_			:       Union[str, Any]									=			self.dummy_input
        return init_dict, inputs_dict


"""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     lowercase__( ):
     lowercase_			:       List[Any]									=			argparse.ArgumentParser()
     parser.add_argument('--model_ckpt'  ,    type=__SCREAMING_SNAKE_CASE  ,    default='microsoft/unixcoder-base-nine'   )
     parser.add_argument('--num_epochs'  ,    type=__SCREAMING_SNAKE_CASE  ,    default=5   )
     parser.add_argument('--batch_size'  ,    type=__SCREAMING_SNAKE_CASE  ,    default=6   )
     parser.add_argument('--gradient_accumulation_steps'  ,    type=__SCREAMING_SNAKE_CASE  ,    default=1   )
     parser.add_argument('--freeze'  ,    type=__SCREAMING_SNAKE_CASE  ,    default=__SCREAMING_SNAKE_CASE   )
     parser.add_argument('--learning_rate'  ,    type=__SCREAMING_SNAKE_CASE  ,    default=5E-4   )
     parser.add_argument('--seed'  ,    type=__SCREAMING_SNAKE_CASE  ,    default=0   )
     parser.add_argument('--lr_scheduler_type'  ,    type=__SCREAMING_SNAKE_CASE  ,    default='cosine'   )
     parser.add_argument('--num_warmup_steps'  ,    type=__SCREAMING_SNAKE_CASE  ,    default=10   )
     parser.add_argument('--weight_decay'  ,    type=__SCREAMING_SNAKE_CASE  ,    default=0.01   )
     parser.add_argument('--output_dir'  ,    type=__SCREAMING_SNAKE_CASE  ,    default='./results'   )
     return parser.parse_args()


__SCREAMING_SNAKE_CASE								=load("accuracy")



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]   ):
     lowercase_							,						lowercase_			:       Any									=			eval_pred
     lowercase_			:       Optional[Any]									=			np.argmax(__SCREAMING_SNAKE_CASE  ,    axis=1   )
     return metric.compute(predictions=__SCREAMING_SNAKE_CASE  ,    references=__SCREAMING_SNAKE_CASE   )







class  UpperCamelCase	(    lowercase_						):
   def __init__(    self  ,__UpperCamelCase    )			->   None:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       str									=			trainer







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        if control.should_evaluate:
             lowercase_			:       List[str]									=			deepcopy(__UpperCamelCase    )
             self._trainer.evaluate(eval_dataset=self._trainer.train_dataset  ,metric_key_prefix='train'    )
             return control_copy






def     lowercase__( ):
     lowercase_			:       Dict									=			get_args()
     set_seed(args.seed   )

     lowercase_			:       str									=			load_dataset('codeparrot/codecomplex'  ,    split='train'   )
     lowercase_			:       Optional[int]									=			dataset.train_test_split(test_size=0.2   )
     lowercase_			:       str									=			train_test['test'].train_test_split(test_size=0.5   )
     lowercase_			:       Any									=			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_			:       int									=			AutoModelForSequenceClassification.from_pretrained(args.model_ckpt  ,    num_labels=7   )
     lowercase_			:       Union[str, Any]									=			model.config.eos_token_id

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

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

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

     lowercase_			:       Optional[int]									=			train_test_validation.map(
         __SCREAMING_SNAKE_CASE  ,    batched=__SCREAMING_SNAKE_CASE  ,    remove_columns=train_test_validation['train'].column_names  ,    )
     lowercase_			:       Any									=			DataCollatorWithPadding(tokenizer=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       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'  ,    )

     lowercase_			:       Optional[int]									=			Trainer(
         model=__SCREAMING_SNAKE_CASE  ,    args=__SCREAMING_SNAKE_CASE  ,    train_dataset=tokenized_datasets['train']  ,    eval_dataset=tokenized_datasets['valid']  ,    tokenizer=__SCREAMING_SNAKE_CASE  ,    data_collator=__SCREAMING_SNAKE_CASE  ,    compute_metrics=__SCREAMING_SNAKE_CASE  ,    )

     print('Training...'   )
     trainer.add_callback(CustomCallback(__SCREAMING_SNAKE_CASE   )   )
     trainer.train()


if __name__ == "__main__":
       main()

