


'''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 					a__       (					)    ->	int:
    UpperCAmelCase__			:		Optional[int]													=				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=0.0_1						)
    parser.add_argument('''--output_dir'''  ,					type=lowerCAmelCase__  ,					default='''./results'''						)
    return parser.parse_args()


UpperCamelCase__    					=      load('''accuracy''')





def 					a__       (					lowerCAmelCase__						)    ->	Dict:
    UpperCAmelCase__						,						UpperCAmelCase__			:		Tuple													=				eval_pred
    UpperCAmelCase__			:		Any													=				np.argmax(lowerCAmelCase__  ,					axis=1						)
    return metric.compute(predictions=lowerCAmelCase__  ,					references=lowerCAmelCase__						)




class 			lowerCamelCase_   (	__a      ):






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


          '''simple docstring'''


          super().__init__()
          UpperCAmelCase__			:		List[Any]													=				trainer






      def lowercase_							(					self				:    List[str]						,						_A				:    Union[str, Any]						,						_A				:    Dict						,						_A				:    List[Any]						,						**_A				:    Dict ):


          '''simple docstring'''


          if control.should_evaluate:
              UpperCAmelCase__			:		Dict													=				deepcopy(_A )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,						metric_key_prefix='''train''' )
              return control_copy





def 					a__       (					)    ->	List[Any]:
    UpperCAmelCase__			:		Optional[Any]													=				get_args()
    set_seed(args.seed						)

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

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

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

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

    def tokenize(lowerCAmelCase__						):
        UpperCAmelCase__			:		Union[str, Any]													=				tokenizer(example['''src''']  ,					truncation=lowerCAmelCase__  ,					max_length=10_24						)
        UpperCAmelCase__			:		str													=				labels.straint(example['''complexity''']						)
        return {
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"],
            "label": label,
        }

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

    UpperCAmelCase__			:		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=0.0_1  ,					metric_for_best_model='''accuracy'''  ,					run_name='''complexity-java'''  ,					report_to='''wandb'''  ,					)

    UpperCAmelCase__			:		Dict													=				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()




'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    List[str]						,						_A				:    NestedDataStructureLike[PathLike]						,						_A				:    Optional[NamedSplit] = None						,						_A				:    Optional[Features] = None						,						_A				:    str = None						,						_A				:    bool = False						,						_A				:    bool = False						,						_A				:    Optional[str] = None						,						_A				:    Optional[int] = None						,						**_A				:    Union[str, Any]						,						):


          '''simple docstring'''


          super().__init__(
              _A						,						split=_A						,						features=_A						,						cache_dir=_A						,						keep_in_memory=_A						,						streaming=_A						,						num_proc=_A						,						**_A						,						)
          UpperCAmelCase__			:		List[str]													=				field
          UpperCAmelCase__			:		Optional[Any]													=				path_or_paths if isinstance(_A						,						_A ) else {self.split: path_or_paths}
          UpperCAmelCase__			:		Any													=				Json(
              cache_dir=_A						,						data_files=_A						,						features=_A						,						field=_A						,						**_A						,						)






      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          if self.streaming:
              UpperCAmelCase__			:		int													=				self.builder.as_streaming_dataset(split=self.split )
          # Build regular (map-style) dataset
          else:
              UpperCAmelCase__			:		List[str]													=				None
              UpperCAmelCase__			:		Dict													=				None
              UpperCAmelCase__			:		Optional[int]													=				None
              UpperCAmelCase__			:		List[Any]													=				None

              self.builder.download_and_prepare(
                  download_config=_A						,						download_mode=_A						,						verification_mode=_A						,						base_path=_A						,						num_proc=self.num_proc						,						)
              UpperCAmelCase__			:		str													=				self.builder.as_dataset(
                  split=self.split						,						verification_mode=_A						,						in_memory=self.keep_in_memory )
          return dataset




class 			lowerCamelCase_   :






      def __init__(					self				:    Optional[Any]						,						_A				:    Dataset						,						_A				:    Union[PathLike, BinaryIO]						,						_A				:    Optional[int] = None						,						_A				:    Optional[int] = None						,						**_A				:    List[str]						,						):


          '''simple docstring'''


          if num_proc is not None and num_proc <= 0:
              raise ValueError(f"""num_proc {num_proc} must be an integer > 0.""" )

          UpperCAmelCase__			:		Dict													=				dataset
          UpperCAmelCase__			:		Any													=				path_or_buf
          UpperCAmelCase__			:		List[str]													=				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          UpperCAmelCase__			:		Tuple													=				num_proc
          UpperCAmelCase__			:		Any													=				'''utf-8'''
          UpperCAmelCase__			:		Optional[int]													=				to_json_kwargs






      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				self.to_json_kwargs.pop('''path_or_buf'''						,						_A )
          UpperCAmelCase__			:		Optional[int]													=				self.to_json_kwargs.pop('''orient'''						,						'''records''' )
          UpperCAmelCase__			:		Tuple													=				self.to_json_kwargs.pop('''lines'''						,						True if orient == '''records''' else False )
          UpperCAmelCase__			:		str													=				self.to_json_kwargs.pop('''index'''						,						False if orient in ['''split''', '''table'''] else True )
          UpperCAmelCase__			:		Optional[Any]													=				self.to_json_kwargs.pop('''compression'''						,						_A )

          if compression not in [None, "infer", "gzip", "bz2", "xz"]:
              raise NotImplementedError(f"""`datasets` currently does not support {compression} compression""" )

          if isinstance(self.path_or_buf						,						(str, bytes, os.PathLike) ):
              with fsspec.open(self.path_or_buf						,						'''wb'''						,						compression=_A ) as buffer:
                  UpperCAmelCase__			:		Union[str, Any]													=				self._write(file_obj=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          else:
              if compression:
                  raise NotImplementedError(
                      f"""The compression parameter is not supported when writing to a buffer, but compression={compression}"""
                      ''' was passed. Please provide a local path instead.''' )
              UpperCAmelCase__			:		Union[str, Any]													=				self._write(
                  file_obj=self.path_or_buf						,						orient=_A						,						lines=_A						,						index=_A						,						**self.to_json_kwargs )
          return written






      def lowercase_							(					self				:    Optional[int]						,						_A				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				args

          UpperCAmelCase__			:		Dict													=				query_table(
              table=self.dataset.data						,						key=slice(_A						,						offset + self.batch_size )						,						indices=self.dataset._indices						,						)
          UpperCAmelCase__			:		str													=				batch.to_pandas().to_json(
              path_or_buf=_A						,						orient=_A						,						lines=_A						,						index=_A						,						**_A )
          if not json_str.endswith('''\n''' ):
              json_str += "\n"
          return json_str.encode(self.encoding )






      def lowercase_							(					self				:    Union[str, Any]						,						_A				:    BinaryIO						,						_A				:    Optional[int]						,						_A				:    int						,						_A				:    Any						,						**_A				:    Any						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				0

          if self.num_proc is None or self.num_proc == 1:
              for offset in logging.tqdm(
                  range(0						,						len(self.dataset )						,						self.batch_size )						,						unit='''ba'''						,						disable=not logging.is_progress_bar_enabled()						,						desc='''Creating json from Arrow format'''						,						):
                  UpperCAmelCase__			:		Optional[int]													=				self._batch_json((offset, orient, lines, index, to_json_kwargs) )
                  written += file_obj.write(_A )
          else:
              UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				len(self.dataset ), self.batch_size
              with multiprocessing.Pool(self.num_proc ) as pool:
                  for json_str in logging.tqdm(
                      pool.imap(
                          self._batch_json						,						[(offset, orient, lines, index, to_json_kwargs) for offset in range(0						,						_A						,						_A )]						,						)						,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,						unit='''ba'''						,						disable=not logging.is_progress_bar_enabled()						,						desc='''Creating json from Arrow format'''						,						):
                      written += file_obj.write(_A )

          return written
