

"""simple docstring"""


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


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

@dataclass
class   __a				(datasets.BuilderConfig):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE    :int							=			1_00_00
     _SCREAMING_SNAKE_CASE    :Optional[List[str]]							=			None
     _SCREAMING_SNAKE_CASE    :Optional[datasets.Features]							=			None





class   __a				(datasets.ArrowBasedBuilder):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE    :Tuple							=			ParquetConfig

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


           """simple docstring"""


           return datasets.DatasetInfo(features=self.config.features	)

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


           """simple docstring"""


           if not self.config.data_files:
                 raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''	)
           SCREAMING_SNAKE_CASE__ :   Any   		=						dl_manager.download_and_extract(self.config.data_files	)
           if isinstance(UpperCamelCase_						,     (str, list, tuple)	):
                 SCREAMING_SNAKE_CASE__ :   Any   		=						data_files
                 if isinstance(UpperCamelCase_						,     UpperCamelCase_	):
                       SCREAMING_SNAKE_CASE__ :   List[str]   		=						[files]
                 # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                 SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						[dl_manager.iter_files(UpperCamelCase_	) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,     gen_kwargs={"""files""": files}	)]
           SCREAMING_SNAKE_CASE__ :   Tuple   		=						[]
           for split_name, files in data_files.items():
                 if isinstance(UpperCamelCase_						,     UpperCamelCase_	):
                       SCREAMING_SNAKE_CASE__ :   Tuple   		=						[files]
                 # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                 SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						[dl_manager.iter_files(UpperCamelCase_	) for file in files]
                 # Infer features is they are stoed in the arrow schema
                 if self.info.features is None:
                       for file in itertools.chain.from_iterable(UpperCamelCase_	):
                             with open(UpperCamelCase_						,     """rb"""	) as f:
                                   SCREAMING_SNAKE_CASE__ :   List[str]   		=						datasets.Features.from_arrow_schema(pq.read_schema(UpperCamelCase_	)	)
                             break
                 splits.append(datasets.SplitGenerator(name=UpperCamelCase_						,     gen_kwargs={"""files""": files}	)	)
           return splits

     def        _a      (    self						,     _a	)	->   Any:


           """simple docstring"""


           if self.info.features is not None:
                 # more expensive cast to support nested features with keys in a different order
                 # allows str <-> int/float or str to Audio for example
                 SCREAMING_SNAKE_CASE__ :   List[Any]   		=						table_cast(UpperCamelCase_						,     self.info.features.arrow_schema	)
           return pa_table





     def        _a      (    self						,     _a	)	->   str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						self.info.features.arrow_schema if self.info.features is not None else None
           if self.info.features is not None and self.config.columns is not None:
                 if sorted(field.name for field in schema	) != sorted(self.config.columns	):
                       raise ValueError(
                           f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''	)
           for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_	)	):
                 with open(UpperCamelCase_						,     """rb"""	) as f:
                       SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						pq.ParquetFile(UpperCamelCase_	)
                       try:
                             for batch_idx, record_batch in enumerate(
                                 parquet_file.iter_batches(batch_size=self.config.batch_size						,     columns=self.config.columns	)	):
                                   SCREAMING_SNAKE_CASE__ :   Any   		=						pa.Table.from_batches([record_batch]	)
                                   # Uncomment for debugging (will print the Arrow table size and elements)
                                   # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                   # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                   yield f'''{file_idx}_{batch_idx}''', self._cast_table(UpperCamelCase_	)
                       except ValueError as e:
                             logger.error(f'''Failed to read file \'{file}\' with error {type(UpperCamelCase_	)}: {e}'''	)
                             raise






"""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()

