





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    (       )			->    Dict:
   A_       :					List[str]        =      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()


_lowerCAmelCase				:   Any						       =						load('''accuracy''')

def   __snake_case    (       _lowerCAmelCase			:			List[str]						)			->    Optional[int]:
   A_   ,    A_       :					Any        =      eval_pred
   A_       :					Tuple        =      np.argmax(_lowerCAmelCase					,					axis=1						)
   return metric.compute(predictions=_lowerCAmelCase					,					references=_lowerCAmelCase						)


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     def __init__(				self							:Union[str, Any]      ,   snake_case							:int	):





        '''simple docstring'''




        super().__init__()
        A_       :					List[str]        =      trainer





     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:Optional[int]      ,   snake_case							:List[Any]      ,   snake_case							:Dict      ,   **snake_case							:Dict	):





        '''simple docstring'''




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




def   __snake_case    (       )			->    List[Any]:
   A_       :					Optional[int]        =      get_args()
   set_seed(args.seed						)

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

   print("Loading tokenizer and model"						)
   A_       :					Any        =      AutoTokenizer.from_pretrained(args.model_ckpt						)
   A_       :					List[str]        =      tokenizer.eos_token
   A_       :					Dict        =      AutoModelForSequenceClassification.from_pretrained(args.model_ckpt					,					num_labels=7						)
   A_       :					Dict        =      model.config.eos_token_id

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

   A_       :					List[Any]        =      ClassLabel(num_classes=7					,					names=list(set(train_test_validation["train"]["complexity"]						)						)						)

   def tokenize(_lowerCAmelCase			:			Dict						):
      A_       :					Tuple        =      tokenizer(example["src"]					,					truncation=_lowerCAmelCase					,					max_length=1024						)
      A_       :					Optional[Any]        =      labels.straint(example["complexity"]						)
      return {
          "input_ids": inputs["input_ids"],
          "attention_mask": inputs["attention_mask"],
          "label": label,
      }

   A_       :					List[str]        =      train_test_validation.map(
       _lowerCAmelCase					,					batched=_lowerCAmelCase					,					remove_columns=train_test_validation["train"].column_names					,					)
   A_       :					str        =      DataCollatorWithPadding(tokenizer=_lowerCAmelCase						)

   A_       :					str        =      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_       :					str        =      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()







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_lowerCAmelCase				:   Optional[Any]						       =						logging.get_logger(__name__)


_lowerCAmelCase				:   List[str]						       =						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
_lowerCAmelCase				:   int						       =						{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

_lowerCAmelCase				:   Optional[int]						       =						{
    '''facebook/bart-base''': 1_024,
    '''facebook/bart-large''': 1_024,
    '''facebook/bart-large-mnli''': 1_024,
    '''facebook/bart-large-cnn''': 1_024,
    '''facebook/bart-large-xsum''': 1_024,
    '''yjernite/bart_eli5''': 1_024,
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  VOCAB_FILES_NAMES
     __UpperCamelCase            =  PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase            =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase            =  ['''input_ids''', '''attention_mask''']
     __UpperCamelCase            =  BartTokenizer



     def __init__(				self							:List[str]      ,   snake_case							:Tuple=None      ,   snake_case							:Union[str, Any]=None      ,   snake_case							:Union[str, Any]=None      ,   snake_case							:List[Any]="replace"      ,   snake_case							:List[str]="<s>"      ,   snake_case							:Optional[int]="</s>"      ,   snake_case							:Union[str, Any]="</s>"      ,   snake_case							:Optional[Any]="<s>"      ,   snake_case							:List[Any]="<unk>"      ,   snake_case							:Optional[Any]="<pad>"      ,   snake_case							:Dict="<mask>"      ,   snake_case							:int=False      ,   snake_case							:List[Any]=True      ,   **snake_case							:Union[str, Any]      ,   ):





        '''simple docstring'''




        super().__init__(
            snake_case      ,   snake_case      ,   tokenizer_file=snake_case      ,   errors=snake_case      ,   bos_token=snake_case      ,   eos_token=snake_case      ,   sep_token=snake_case      ,   cls_token=snake_case      ,   unk_token=snake_case      ,   pad_token=snake_case      ,   mask_token=snake_case      ,   add_prefix_space=snake_case      ,   trim_offsets=snake_case      ,   **snake_case      ,   )

        A_       :					Optional[Any]        =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
        if pre_tok_state.get("add_prefix_space"      ,   snake_case	) != add_prefix_space:
           A_       :					Dict        =      getattr(snake_case      ,   pre_tok_state.pop("type"	)	)
           A_       :					List[str]        =      add_prefix_space
           A_       :					int        =      pre_tok_class(**snake_case	)

        A_       :					Any        =      add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        A_       :					Tuple        =      "post_processor"
        A_       :					Union[str, Any]        =      getattr(self.backend_tokenizer      ,   snake_case      ,   snake_case	)
        if tokenizer_component_instance:
           A_       :					Any        =      json.loads(tokenizer_component_instance.__getstate__()	)

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
              A_       :					List[Any]        =      tuple(state["sep"]	)
           if "cls" in state:
              A_       :					str        =      tuple(state["cls"]	)

           A_       :					int        =      False

           if state.get("add_prefix_space"      ,   snake_case	) != add_prefix_space:
              A_       :					List[Any]        =      add_prefix_space
              A_       :					Union[str, Any]        =      True

           if state.get("trim_offsets"      ,   snake_case	) != trim_offsets:
              A_       :					int        =      trim_offsets
              A_       :					str        =      True

           if changes_to_apply:
              A_       :					Tuple        =      getattr(snake_case      ,   state.pop("type"	)	)
              A_       :					Union[str, Any]        =      component_class(**snake_case	)
              setattr(self.backend_tokenizer      ,   snake_case      ,   snake_case	)



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        if self._mask_token is None:
           if self.verbose:
              logger.error("Using mask_token, but it is not set yet."	)
           return None
        return str(self._mask_token	)



     @mask_token.setter
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:Dict	):





        '''simple docstring'''




        A_       :					Optional[int]        =      AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case	) if isinstance(snake_case      ,   snake_case	) else value
        A_       :					List[Any]        =      value



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   *snake_case							:str      ,   **snake_case							:str	):





        '''simple docstring'''




        A_       :					Tuple        =      kwargs.get("is_split_into_words"      ,   snake_case	)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."	)

        return super()._batch_encode_plus(*snake_case      ,   **snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   *snake_case							:Any      ,   **snake_case							:Optional[Any]	):





        '''simple docstring'''




        A_       :					List[Any]        =      kwargs.get("is_split_into_words"      ,   snake_case	)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."	)

        return super()._encode_plus(*snake_case      ,   **snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:str      ,   snake_case							:Optional[str] = None	):





        '''simple docstring'''




        A_       :					Any        =      self._tokenizer.model.save(snake_case      ,   name=snake_case	)
        return tuple(snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   snake_case							:List[str]      ,   snake_case							:Optional[int]=None	):





        '''simple docstring'''




        A_       :					str        =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None	):





        '''simple docstring'''




        A_       :					Dict        =      [self.sep_token_id]
        A_       :					List[str]        =      [self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep	) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]
