"""simple docstring"""







from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching



def     lowercase__( ):
     import os as original_os
     from os import path as original_path
     from os import rename as original_rename
     from os.path import dirname as original_dirname
     from os.path import join as original_join

     assert _test_patching.os is original_os
     assert _test_patching.path is original_path
     assert _test_patching.join is original_join

     assert _test_patching.renamed_os is original_os
     assert _test_patching.renamed_path is original_path
     assert _test_patching.renamed_join is original_join

     lowercase_			:       int									=			'__test_patch_submodule_mock__'
     with patch_submodule(_test_patching  ,    'os.path.join'  ,    __SCREAMING_SNAKE_CASE   ):
          # Every way to access os.path.join must be patched, and the rest must stay untouched

          # check os.path.join
          assert isinstance(_test_patching.os  ,    _PatchedModuleObj   )
          assert isinstance(_test_patching.os.path  ,    _PatchedModuleObj   )
          assert _test_patching.os.path.join is mock

          # check path.join
          assert isinstance(_test_patching.path  ,    _PatchedModuleObj   )
          assert _test_patching.path.join is mock

          # check join
          assert _test_patching.join is mock

          # check that the other attributes are untouched
          assert _test_patching.os.rename is original_rename
          assert _test_patching.path.dirname is original_dirname
          assert _test_patching.os.path.dirname is original_dirname

          # Even renamed modules or objects must be patched

          # check renamed_os.path.join
          assert isinstance(_test_patching.renamed_os  ,    _PatchedModuleObj   )
          assert isinstance(_test_patching.renamed_os.path  ,    _PatchedModuleObj   )
          assert _test_patching.renamed_os.path.join is mock

          # check renamed_path.join
          assert isinstance(_test_patching.renamed_path  ,    _PatchedModuleObj   )
          assert _test_patching.renamed_path.join is mock

          # check renamed_join
          assert _test_patching.renamed_join is mock

          # check that the other attributes are untouched
          assert _test_patching.renamed_os.rename is original_rename
          assert _test_patching.renamed_path.dirname is original_dirname
          assert _test_patching.renamed_os.path.dirname is original_dirname

     # check that everthing is back to normal when the patch is over

     assert _test_patching.os is original_os
     assert _test_patching.path is original_path
     assert _test_patching.join is original_join

     assert _test_patching.renamed_os is original_os
     assert _test_patching.renamed_path is original_path
     assert _test_patching.renamed_join is original_join



def     lowercase__( ):
     assert _test_patching.open is open

     lowercase_			:       Dict									=			'__test_patch_submodule_builtin_mock__'
     # _test_patching has "open" in its globals
     assert _test_patching.open is open
     with patch_submodule(_test_patching  ,    'open'  ,    __SCREAMING_SNAKE_CASE   ):
          assert _test_patching.open is mock

     # check that everthing is back to normal when the patch is over

     assert _test_patching.open is open



def     lowercase__( ):
     # pandas.read_csv is not present in _test_patching
     lowercase_			:       Any									=			'__test_patch_submodule_missing_mock__'
     with patch_submodule(_test_patching  ,    'pandas.read_csv'  ,    __SCREAMING_SNAKE_CASE   ):
          pass



def     lowercase__( ):
     # builtin should always be mocked even if they're not in the globals
     # in case they're loaded at one point
     lowercase_			:       int									=			'__test_patch_submodule_missing_builtin_mock__'
     # _test_patching doesn't have "len" in its globals
     assert getattr(_test_patching  ,    'len'  ,    __SCREAMING_SNAKE_CASE   ) is None
     with patch_submodule(_test_patching  ,    'len'  ,    __SCREAMING_SNAKE_CASE   ):
          assert _test_patching.len is mock
     assert _test_patching.len is len



def     lowercase__( ):
     lowercase_			:       Dict									=			'__test_patch_submodule_start_and_stop_mock__'
     lowercase_			:       Optional[int]									=			patch_submodule(_test_patching  ,    'open'  ,    __SCREAMING_SNAKE_CASE   )
     assert _test_patching.open is open
     patch.start()
     assert _test_patching.open is mock
     patch.stop()
     assert _test_patching.open is open



def     lowercase__( ):
     from os import rename as original_rename
     from os.path import dirname as original_dirname
     from os.path import join as original_join

     lowercase_			:       Optional[int]									=			'__test_patch_submodule_successive_join__'
     lowercase_			:       Dict									=			'__test_patch_submodule_successive_dirname__'
     lowercase_			:       List[Any]									=			'__test_patch_submodule_successive_rename__'
     assert _test_patching.os.path.join is original_join
     assert _test_patching.os.path.dirname is original_dirname
     assert _test_patching.os.rename is original_rename

     with patch_submodule(_test_patching  ,    'os.path.join'  ,    __SCREAMING_SNAKE_CASE   ):
          with patch_submodule(_test_patching  ,    'os.rename'  ,    __SCREAMING_SNAKE_CASE   ):
               with patch_submodule(_test_patching  ,    'os.path.dirname'  ,    __SCREAMING_SNAKE_CASE   ):
                    assert _test_patching.os.path.join is mock_join
                    assert _test_patching.os.path.dirname is mock_dirname
                    assert _test_patching.os.rename is mock_rename

    # try another order
     with patch_submodule(_test_patching  ,    'os.rename'  ,    __SCREAMING_SNAKE_CASE   ):
          with patch_submodule(_test_patching  ,    'os.path.join'  ,    __SCREAMING_SNAKE_CASE   ):
               with patch_submodule(_test_patching  ,    'os.path.dirname'  ,    __SCREAMING_SNAKE_CASE   ):
                    assert _test_patching.os.path.join is mock_join
                    assert _test_patching.os.path.dirname is mock_dirname
                    assert _test_patching.os.rename is mock_rename

     assert _test_patching.os.path.join is original_join
     assert _test_patching.os.path.dirname is original_dirname
     assert _test_patching.os.rename is original_rename







def     lowercase__( ):
     lowercase_			:       Optional[Any]									=			'__test_patch_submodule_doesnt_exist_mock__'
     with patch_submodule(_test_patching  ,    '__module_that_doesn_exist__.__attribute_that_doesn_exist__'  ,    __SCREAMING_SNAKE_CASE   ):
          pass
     with patch_submodule(_test_patching  ,    'os.__attribute_that_doesn_exist__'  ,    __SCREAMING_SNAKE_CASE   ):
          pass

"""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()

