

"""simple docstring"""


import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a       :Optional[int]     =			logging.get_logger(__name__)

a       :str     =			{"vocab_file": "spiece.model"}

a       :Dict     =			{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

a       :Any     =			{
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}

class   __a				(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE    :Dict							=			VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE    :int							=			PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE    :Optional[Any]							=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE    :List[str]							=			["""input_ids""", """attention_mask"""]
     _SCREAMING_SNAKE_CASE    :List[int]							=			[]

     def __init__(    self						,     _a						,     _a="<unk>"						,     _a="<s>"						,     _a="</s>"						,     _a="<pad>"						,     _a="[SEP]"						,     _a="[MASK]"						,     _a="[CLS]"						,     _a = None						,     **_a						,     )	->   Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   int   		=						AddedToken(UpperCamelCase_						,     lstrip=UpperCamelCase_						,     rstrip=UpperCamelCase_	) if isinstance(UpperCamelCase_						,     UpperCamelCase_	) else bos_token
           SCREAMING_SNAKE_CASE__ :   int   		=						AddedToken(UpperCamelCase_						,     lstrip=UpperCamelCase_						,     rstrip=UpperCamelCase_	) if isinstance(UpperCamelCase_						,     UpperCamelCase_	) else eos_token
           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						AddedToken(UpperCamelCase_						,     lstrip=UpperCamelCase_						,     rstrip=UpperCamelCase_	) if isinstance(UpperCamelCase_						,     UpperCamelCase_	) else unk_token
           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						AddedToken(UpperCamelCase_						,     lstrip=UpperCamelCase_						,     rstrip=UpperCamelCase_	) if isinstance(UpperCamelCase_						,     UpperCamelCase_	) else pad_token
           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						AddedToken(UpperCamelCase_						,     lstrip=UpperCamelCase_						,     rstrip=UpperCamelCase_	) if isinstance(UpperCamelCase_						,     UpperCamelCase_	) else cls_token
           SCREAMING_SNAKE_CASE__ :   str   		=						AddedToken(UpperCamelCase_						,     lstrip=UpperCamelCase_						,     rstrip=UpperCamelCase_	) if isinstance(UpperCamelCase_						,     UpperCamelCase_	) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						AddedToken(UpperCamelCase_						,     lstrip=UpperCamelCase_						,     rstrip=UpperCamelCase_	) if isinstance(UpperCamelCase_						,     UpperCamelCase_	) else mask_token

           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               bos_token=UpperCamelCase_						,     eos_token=UpperCamelCase_						,     unk_token=UpperCamelCase_						,     pad_token=UpperCamelCase_						,     sep_token=UpperCamelCase_						,     mask_token=UpperCamelCase_						,     cls_token=UpperCamelCase_						,     sp_model_kwargs=self.sp_model_kwargs						,     **UpperCamelCase_						,     )

           SCREAMING_SNAKE_CASE__ :   Any   		=						vocab_file

           SCREAMING_SNAKE_CASE__ :   str   		=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(UpperCamelCase_	)

     @property
     def        _a      (    self	)	->   Optional[Any]:


           """simple docstring"""


           return self.sp_model.get_piece_size()

     def        _a      (    self	)	->   Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						{self.convert_ids_to_tokens(UpperCamelCase_	): i for i in range(self.vocab_size	)}
           vocab.update(self.added_tokens_encoder	)
           return vocab

     def __getstate__(    self	)	->   List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   str   		=						self.__dict__.copy()
           SCREAMING_SNAKE_CASE__ :   Dict   		=						None
           return state

     def __setstate__(    self						,     _a	)	->   Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						d

           # for backward compatibility
           if not hasattr(self						,     """sp_model_kwargs"""	):
                 SCREAMING_SNAKE_CASE__ :   Any   		=						{}

           SCREAMING_SNAKE_CASE__ :   Any   		=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(self.vocab_file	)

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


           """simple docstring"""


           return self.sp_model.encode(UpperCamelCase_						,     out_type=UpperCamelCase_	)

     def        _a      (    self						,     _a	)	->   List[str]:


           """simple docstring"""


           return self.sp_model.piece_to_id(UpperCamelCase_	)

     def        _a      (    self						,     _a	)	->   Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   int   		=						self.sp_model.IdToPiece(UpperCamelCase_	)
           return token

     def        _a      (    self						,     _a	)	->   Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   List[str]   		=						[]
           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						''
           SCREAMING_SNAKE_CASE__ :   str   		=						False
           for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                       if not prev_is_special:
                             out_string += " "
                       out_string += self.sp_model.decode(UpperCamelCase_	) + token
                       SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						True
                       SCREAMING_SNAKE_CASE__ :   str   		=						[]
                 else:
                       current_sub_tokens.append(UpperCamelCase_	)
                       SCREAMING_SNAKE_CASE__ :   Tuple   		=						False
           out_string += self.sp_model.decode(UpperCamelCase_	)
           return out_string.strip()

     def        _a      (    self						,     _a						,     _a = False						,     _a = None						,     _a = True						,     **_a						,     )	->   Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Any   		=						kwargs.pop("""use_source_tokenizer"""						,     UpperCamelCase_	)

           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						self.convert_ids_to_tokens(UpperCamelCase_						,     skip_special_tokens=UpperCamelCase_	)

           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						[]
           SCREAMING_SNAKE_CASE__ :   Any   		=						[]
           for token in filtered_tokens:
                 if skip_special_tokens and token in self.all_special_ids:
                       continue
                 if token in self.added_tokens_encoder:
                       if current_sub_text:
                             sub_texts.append(self.convert_tokens_to_string(UpperCamelCase_	)	)
                             SCREAMING_SNAKE_CASE__ :   Dict   		=						[]
                       sub_texts.append(UpperCamelCase_	)
                 else:
                       current_sub_text.append(UpperCamelCase_	)
           if current_sub_text:
                 sub_texts.append(self.convert_tokens_to_string(UpperCamelCase_	)	)

           # Mimic the behavior of the Rust tokenizer:
           # No space before [MASK] and [SEP]
           if spaces_between_special_tokens:
                 SCREAMING_SNAKE_CASE__ :   List[str]   		=						re.sub(r""" (\[(MASK|SEP)\])"""						,     r"""\1"""						,     """ """.join(UpperCamelCase_	)	)
           else:
                 SCREAMING_SNAKE_CASE__ :   str   		=						''.join(UpperCamelCase_	)

           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						(
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
                 SCREAMING_SNAKE_CASE__ :   str   		=						self.clean_up_tokenization(UpperCamelCase_	)
                 return clean_text
           else:
                 return text

     def        _a      (    self						,     _a						,     _a = None	)	->   List[Any]:


           """simple docstring"""


           if not os.path.isdir(UpperCamelCase_	):
                 logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
                 return
           SCREAMING_SNAKE_CASE__ :   Tuple   		=						os.path.join(
               UpperCamelCase_						,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

           if os.path.abspath(self.vocab_file	) != os.path.abspath(UpperCamelCase_	) and os.path.isfile(self.vocab_file	):
                 copyfile(self.vocab_file						,     UpperCamelCase_	)
           elif not os.path.isfile(self.vocab_file	):
                 with open(UpperCamelCase_						,     """wb"""	) as fi:
                       SCREAMING_SNAKE_CASE__ :   str   		=						self.sp_model.serialized_model_proto()
                       fi.write(UpperCamelCase_	)

           return (out_vocab_file,)

     def        _a      (    self						,     _a						,     _a = None	)	->   List[str]:


           """simple docstring"""


           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           SCREAMING_SNAKE_CASE__ :   int   		=						[self.cls_token_id]
           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						[self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep

     def        _a      (    self						,     _a						,     _a = None						,     _a = False	)	->   int:


           """simple docstring"""


           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=UpperCamelCase_						,     token_ids_a=UpperCamelCase_						,     already_has_special_tokens=UpperCamelCase_	)

           if token_ids_a is None:
                 return [1] + ([0] * len(UpperCamelCase_	)) + [1]
           return [1] + ([0] * len(UpperCamelCase_	)) + [1] + ([0] * len(UpperCamelCase_	)) + [1]





     def        _a      (    self						,     _a						,     _a = None	)	->   List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   str   		=						[self.sep_token_id]
           SCREAMING_SNAKE_CASE__ :   int   		=						[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	) * [0] + len(token_ids_a + sep	) * [1]






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


UpperCamelCase__          =	load('accuracy')
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					eval_pred
    UpperCAmelCase__	:      Optional[int]     							=					np.argmax(_snake_case    ,axis=1 )
    return metric.compute(predictions=_snake_case    ,references=_snake_case )





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_      ):
          super().__init__()
          UpperCAmelCase__	:      List[str]     							=					trainer



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ):
          if control.should_evaluate:
              UpperCAmelCase__	:      int     							=					deepcopy(UpperCamelCase_      )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,	metric_key_prefix='train'      )
              return control_copy




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      int     							=					get_args()
    set_seed(args.seed )

    UpperCAmelCase__	:      Optional[int]     							=					load_dataset('codeparrot/codecomplex'    ,split='train' )
    UpperCAmelCase__	:      Tuple     							=					dataset.train_test_split(test_size=0.2 )
    UpperCAmelCase__	:      List[Any]     							=					train_test['test'].train_test_split(test_size=0.5 )
    UpperCAmelCase__	:      Tuple     							=					DatasetDict(
        {
            'train': train_test['train'],
            'test': test_validation['train'],
            'valid': test_validation['test'],
        } )

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

    if args.freeze:
        for param in model.roberta.parameters():
            UpperCAmelCase__	:      Optional[int]     							=					False

    UpperCAmelCase__	:      Optional[Any]     							=					ClassLabel(num_classes=7    ,names=list(set(train_test_validation['train']['complexity'] ) ) )

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

    UpperCAmelCase__	:      List[Any]     							=					train_test_validation.map(
        _snake_case    ,batched=_snake_case    ,remove_columns=train_test_validation['train'].column_names    ,)
    UpperCAmelCase__	:      Tuple     							=					DataCollatorWithPadding(tokenizer=_snake_case )

    UpperCAmelCase__	:      Union[str, Any]     							=					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'    ,)

    UpperCAmelCase__	:      Dict     							=					Trainer(
        model=_snake_case    ,args=_snake_case    ,train_dataset=tokenized_datasets['train']    ,eval_dataset=tokenized_datasets['valid']    ,tokenizer=_snake_case    ,data_collator=_snake_case    ,compute_metrics=_snake_case    ,)

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


if __name__ == "__main__":
    main()

