

"""simple docstring"""



# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


SCREAMING_SNAKE_CASE_					:	Any							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	Tuple							  =						{}
SCREAMING_SNAKE_CASE_					:	List[Any]							  =						{}
SCREAMING_SNAKE_CASE_					:	Dict							  =						{}





def  _snake_case     (    UpperCAmelCase_  :					List[Any]					,   UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					str = None					,   ):
     A__										=  aliases if aliases is not None else []
     if format_type in _FORMAT_TYPES:
          logger.warning(
              F"""Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""			)
     A__										=  formatter_cls
     for alias in set(aliases + [format_type]			):
          if alias in _FORMAT_TYPES_ALIASES:
               logger.warning(
                   F"""Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""			)
          A__										=  format_type





def  _snake_case     (    UpperCAmelCase_  :					List[Any]					,   UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					str = None			):
     A__										=  aliases if aliases is not None else []
     for alias in set(aliases + [format_type]			):
          A__										=  unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
 from .torch_formatter import TorchFormatter

 _register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
 SCREAMING_SNAKE_CASE_					:	Any							  =						ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
 _register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
 from .tf_formatter import TFFormatter

 _register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
 SCREAMING_SNAKE_CASE_					:	Tuple							  =						ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
 _register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
 from .jax_formatter import JaxFormatter

 _register_formatter(JaxFormatter, 'jax', aliases=[])
else:
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						ValueError('JAX needs to be installed to be able to return JAX arrays.')
 _register_unavailable_formatter(_jax_error, 'jax', aliases=[])





def  _snake_case     (    UpperCAmelCase_  :					Union[str, Any]			):







     if format_type in _FORMAT_TYPES_ALIASES:
          return _FORMAT_TYPES_ALIASES[format_type]
     else:
          return format_type





def  _snake_case     (    UpperCAmelCase_  :					List[Any]					,   **UpperCAmelCase_  :					Union[str, Any]			):
     A__										=  get_format_type_from_alias(__snake_case			)
     if format_type in _FORMAT_TYPES:
          return _FORMAT_TYPES[format_type](**__snake_case			)
     if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
          raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
     else:
          raise ValueError(
              F"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None			)}, but got \'{format_type}\'"""			)







"""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  _snake_case     (    ):
     A__										=  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()


SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						load('accuracy')





def  _snake_case     (    UpperCAmelCase_  :					Tuple			):
     A__			,				A__										=  eval_pred
     A__										=  np.argmax(UpperCAmelCase_					,   axis=1			)
     return metric.compute(predictions=UpperCAmelCase_					,   references=UpperCAmelCase_			)



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    def __init__(     self: Optional[int]    ,						UpperCamelCase: Optional[Any]       ):






         """simple docstring"""


         super().__init__()
         A__										=  trainer





    def 				UpperCamelCase	(     self: int    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: Dict    ,						**UpperCamelCase: int       ):






         """simple docstring"""


         if control.should_evaluate:
              A__										=  deepcopy(UpperCamelCase       )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset    ,						metric_key_prefix="""train"""       )
              return control_copy





def  _snake_case     (    ):
     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(UpperCAmelCase_  :					int			):
          A__										=  tokenizer(example["""src"""]					,   truncation=UpperCAmelCase_					,   max_length=1024			)
          A__										=  labels.straint(example["""complexity"""]			)
          return {
              "input_ids": inputs["input_ids"],
              "attention_mask": inputs["attention_mask"],
              "label": label,
          }

     A__										=  train_test_validation.map(
         UpperCAmelCase_					,   batched=UpperCAmelCase_					,   remove_columns=train_test_validation["""train"""].column_names					,   )
     A__										=  DataCollatorWithPadding(tokenizer=UpperCAmelCase_			)

     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=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()






