"""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 					UpperCAmelCase    (		)				->      Optional[int]:
     snake_case_					       =					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()


__UpperCamelCase									=			load('''accuracy''')







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      List[Any]:
     snake_case_					       =					eval_pred
     snake_case_					       =					np.argmax(_UpperCAmelCase						,					axis=1					)
     return metric.compute(predictions=_UpperCAmelCase						,					references=_UpperCAmelCase					)




class  UpperCamelCase					(				snake_case__   ):







       def __init__( self,							lowerCAmelCase__)      ->      Union[str, Any]:
            super().__init__()
            snake_case_					       =					trainer







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							**lowerCAmelCase__)      ->      str:
            if control.should_evaluate:
                 snake_case_					       =					deepcopy(lowerCAmelCase__)
                 self._trainer.evaluate(eval_dataset=self._trainer.train_dataset,							metric_key_prefix='train')
                 return control_copy







def 					UpperCAmelCase    (		)				->      Optional[Any]:
     snake_case_					       =					get_args()
     set_seed(args.seed					)

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

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

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

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

     def tokenize(UpperCAmelCase					):
          snake_case_					       =					tokenizer(example['src']						,					truncation=_UpperCAmelCase						,					max_length=1024					)
          snake_case_					       =					labels.straint(example['complexity']					)
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

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

     snake_case_					       =					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'						,					)

     snake_case_					       =					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 shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
  from transformers.trainer import DEFAULT_CALLBACKS

  from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    []




      def      _A	(       self						:				Any						,					A						:				Union[str, Any]						,					A						:				Optional[int]						,					A						:				List[str]						,					**A						:				Tuple       ):
             self.events.append("on_init_end"       )




      def      _A	(       self						:				Any						,					A						:				str						,					A						:				List[Any]						,					A						:				List[Any]						,					**A						:				Tuple       ):
             self.events.append("on_train_begin"       )




      def      _A	(       self						:				Tuple						,					A						:				List[str]						,					A						:				Tuple						,					A						:				int						,					**A						:				List[str]       ):
             self.events.append("on_train_end"       )




      def      _A	(       self						:				Optional[Any]						,					A						:				Dict						,					A						:				Any						,					A						:				Optional[Any]						,					**A						:				List[Any]       ):
             self.events.append("on_epoch_begin"       )




      def      _A	(       self						:				Optional[Any]						,					A						:				List[Any]						,					A						:				List[str]						,					A						:				Optional[int]						,					**A						:				Optional[int]       ):
             self.events.append("on_epoch_end"       )




      def      _A	(       self						:				List[str]						,					A						:				Optional[int]						,					A						:				List[Any]						,					A						:				Union[str, Any]						,					**A						:				Any       ):
             self.events.append("on_step_begin"       )




      def      _A	(       self						:				Tuple						,					A						:				Union[str, Any]						,					A						:				int						,					A						:				Optional[int]						,					**A						:				int       ):
             self.events.append("on_step_end"       )




      def      _A	(       self						:				Optional[int]						,					A						:				Optional[Any]						,					A						:				Union[str, Any]						,					A						:				str						,					**A						:				Union[str, Any]       ):
             self.events.append("on_evaluate"       )




      def      _A	(       self						:				Optional[Any]						,					A						:				Optional[int]						,					A						:				Dict						,					A						:				List[Any]						,					**A						:				Dict       ):
             self.events.append("on_predict"       )




      def      _A	(       self						:				Dict						,					A						:				Dict						,					A						:				List[Any]						,					A						:				Dict						,					**A						:				str       ):
             self.events.append("on_save"       )




      def      _A	(       self						:				Tuple						,					A						:				Optional[Any]						,					A						:				Union[str, Any]						,					A						:				Optional[int]						,					**A						:				Dict       ):
             self.events.append("on_log"       )





      def      _A	(       self						:				Optional[int]						,					A						:				Optional[Any]						,					A						:				Tuple						,					A						:				Tuple						,					**A						:				List[str]       ):
             self.events.append("on_prediction_step"       )

@require_torch
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    tempfile.mkdtemp()




      def      _A	(       self						:				List[Any]       ):
             shutil.rmtree(self.output_dir       )




      def      _A	(       self						:				Union[str, Any]						,					A						:				Optional[int]=0						,					A						:				Optional[Any]=0						,					A						:				Optional[Any]=64						,					A						:				Dict=64						,					A						:				Any=None						,					A						:				Tuple=False						,					**A						:				Optional[int]       ):
             # disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
             # its set to False since the tests later on depend on its value.
             _UpperCAmelCase     :  str							      =    RegressionDataset(length=A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    RegressionDataset(length=A       )
             _UpperCAmelCase     :  Any							      =    RegressionModelConfig(a=A						,					b=A       )
             _UpperCAmelCase     :  List[Any]							      =    RegressionPreTrainedModel(A       )

             _UpperCAmelCase     :  Dict							      =    TrainingArguments(self.output_dir						,					disable_tqdm=A						,					report_to=[]						,					**A       )
             return Trainer(
                 A						,					A						,					train_dataset=A						,					eval_dataset=A						,					callbacks=A						,					)




      def      _A	(       self						:				str						,					A						:				List[str]						,					A						:				List[str]       ):
             self.assertEqual(len(A       )						,					len(A       )       )

             # Order doesn't matter
             _UpperCAmelCase     :  Tuple							      =    sorted(A						,					key=lambda A       : cb.__name__ if isinstance(A						,					A       ) else cb.__class__.__name__       )
             _UpperCAmelCase     :  Any							      =    sorted(A						,					key=lambda A       : cb.__name__ if isinstance(A						,					A       ) else cb.__class__.__name__       )

             for cba, cba in zip(A						,					A       ):
                    if isinstance(A						,					A       ) and isinstance(A						,					A       ):
                           self.assertEqual(A						,					A       )
                    elif isinstance(A						,					A       ) and not isinstance(A						,					A       ):
                           self.assertEqual(A						,					cba.__class__       )
                    elif not isinstance(A						,					A       ) and isinstance(A						,					A       ):
                           self.assertEqual(cba.__class__						,					A       )
                    else:
                           self.assertEqual(A						,					A       )




      def      _A	(       self						:				int						,					A						:				List[str]       ):
             _UpperCAmelCase     :  List[str]							      =    ["on_init_end", "on_train_begin"]
             _UpperCAmelCase     :  str							      =    0
             _UpperCAmelCase     :  Optional[Any]							      =    len(trainer.get_eval_dataloader()       )
             _UpperCAmelCase     :  Optional[int]							      =    ["on_prediction_step"] * len(trainer.get_eval_dataloader()       ) + ["on_log", "on_evaluate"]
             for _ in range(trainer.state.num_train_epochs       ):
                    expected_events.append("on_epoch_begin"       )
                    for _ in range(A       ):
                           step += 1
                           expected_events += ["on_step_begin", "on_step_end"]
                           if step % trainer.args.logging_steps == 0:
                                  expected_events.append("on_log"       )
                           if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
                                  expected_events += evaluation_events.copy()
                           if step % trainer.args.save_steps == 0:
                                  expected_events.append("on_save"       )
                    expected_events.append("on_epoch_end"       )
                    if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
                           expected_events += evaluation_events.copy()
             expected_events += ["on_log", "on_train_end"]
             return expected_events




      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  Any							      =    self.get_trainer()
             _UpperCAmelCase     :  int							      =    DEFAULT_CALLBACKS.copy() + [ProgressCallback]
             self.check_callbacks_equality(trainer.callback_handler.callbacks						,					A       )

             # Callbacks passed at init are added to the default callbacks
             _UpperCAmelCase     :  Optional[int]							      =    self.get_trainer(callbacks=[MyTestTrainerCallback]       )
             expected_callbacks.append(A       )
             self.check_callbacks_equality(trainer.callback_handler.callbacks						,					A       )

             # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
             _UpperCAmelCase     :  List[Any]							      =    self.get_trainer(disable_tqdm=A       )
             _UpperCAmelCase     :  Tuple							      =    DEFAULT_CALLBACKS.copy() + [PrinterCallback]
             self.check_callbacks_equality(trainer.callback_handler.callbacks						,					A       )




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Dict							      =    DEFAULT_CALLBACKS.copy() + [ProgressCallback]
             _UpperCAmelCase     :  Dict							      =    self.get_trainer()

             # We can add, pop, or remove by class name
             trainer.remove_callback(A       )
             expected_callbacks.remove(A       )
             self.check_callbacks_equality(trainer.callback_handler.callbacks						,					A       )

             _UpperCAmelCase     :  Optional[Any]							      =    self.get_trainer()
             _UpperCAmelCase     :  Any							      =    trainer.pop_callback(A       )
             self.assertEqual(cb.__class__						,					A       )
             self.check_callbacks_equality(trainer.callback_handler.callbacks						,					A       )

             trainer.add_callback(A       )
             expected_callbacks.insert(0						,					A       )
             self.check_callbacks_equality(trainer.callback_handler.callbacks						,					A       )

             # We can also add, pop, or remove by instance
             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_trainer()
             _UpperCAmelCase     :  List[Any]							      =    trainer.callback_handler.callbacks[0]
             trainer.remove_callback(A       )
             expected_callbacks.remove(A       )
             self.check_callbacks_equality(trainer.callback_handler.callbacks						,					A       )

             _UpperCAmelCase     :  List[Any]							      =    self.get_trainer()
             _UpperCAmelCase     :  List[Any]							      =    trainer.callback_handler.callbacks[0]
             _UpperCAmelCase     :  Union[str, Any]							      =    trainer.pop_callback(A       )
             self.assertEqual(A						,					A       )
             self.check_callbacks_equality(trainer.callback_handler.callbacks						,					A       )

             trainer.add_callback(A       )
             expected_callbacks.insert(0						,					A       )
             self.check_callbacks_equality(trainer.callback_handler.callbacks						,					A       )





      def      _A	(       self						:				Optional[Any]       ):
             import warnings

             # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
             warnings.simplefilter(action="ignore"						,					category=A       )

             _UpperCAmelCase     :  Optional[int]							      =    self.get_trainer(callbacks=[MyTestTrainerCallback]       )
             trainer.train()
             _UpperCAmelCase     :  Union[str, Any]							      =    trainer.callback_handler.callbacks[-2].events
             self.assertEqual(A						,					self.get_expected_events(A       )       )

             # Independent log/save/eval
             _UpperCAmelCase     :  Tuple							      =    self.get_trainer(callbacks=[MyTestTrainerCallback]						,					logging_steps=5       )
             trainer.train()
             _UpperCAmelCase     :  int							      =    trainer.callback_handler.callbacks[-2].events
             self.assertEqual(A						,					self.get_expected_events(A       )       )

             _UpperCAmelCase     :  List[str]							      =    self.get_trainer(callbacks=[MyTestTrainerCallback]						,					save_steps=5       )
             trainer.train()
             _UpperCAmelCase     :  Tuple							      =    trainer.callback_handler.callbacks[-2].events
             self.assertEqual(A						,					self.get_expected_events(A       )       )

             _UpperCAmelCase     :  int							      =    self.get_trainer(callbacks=[MyTestTrainerCallback]						,					eval_steps=5						,					evaluation_strategy="steps"       )
             trainer.train()
             _UpperCAmelCase     :  Optional[int]							      =    trainer.callback_handler.callbacks[-2].events
             self.assertEqual(A						,					self.get_expected_events(A       )       )

             _UpperCAmelCase     :  Optional[int]							      =    self.get_trainer(callbacks=[MyTestTrainerCallback]						,					evaluation_strategy="epoch"       )
             trainer.train()
             _UpperCAmelCase     :  int							      =    trainer.callback_handler.callbacks[-2].events
             self.assertEqual(A						,					self.get_expected_events(A       )       )

             # A bit of everything
             _UpperCAmelCase     :  int							      =    self.get_trainer(
                 callbacks=[MyTestTrainerCallback]						,					logging_steps=3						,					save_steps=10						,					eval_steps=5						,					evaluation_strategy="steps"						,					)
             trainer.train()
             _UpperCAmelCase     :  Optional[int]							      =    trainer.callback_handler.callbacks[-2].events
             self.assertEqual(A						,					self.get_expected_events(A       )       )

             # warning should be emitted for duplicated callbacks
             with patch("transformers.trainer_callback.logger.warning"       ) as warn_mock:
                    _UpperCAmelCase     :  Optional[Any]							      =    self.get_trainer(
                        callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]						,					)
                    assert str(A       ) in warn_mock.call_args[0][0]

