"""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     UpperCAmelCase__ (       )				->					str:
      '''simple docstring'''


      lowercase														=			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=1_0	)
      parser.add_argument("""--weight_decay"""      ,	type=lowerCAmelCase__      ,	default=0.01	)
      parser.add_argument("""--output_dir"""      ,	type=lowerCAmelCase__      ,	default="""./results"""	)
      return parser.parse_args()


__lowerCAmelCase					: int        =load("""accuracy""")


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any	)				->					Tuple:
      '''simple docstring'''


      lowercase							,							lowercase														=			eval_pred
      lowercase														=			np.argmax(lowerCAmelCase__      ,	axis=1	)
      return metric.compute(predictions=lowerCAmelCase__      ,	references=lowerCAmelCase__	)





class 				_A		( lowerCAmelCase			):

      def __init__(		self	, __lowerCAmelCase		):







            """simple docstring"""



            super().__init__()
            lowercase														=			trainer







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



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






def     UpperCAmelCase__ (       )				->					Optional[Any]:
      '''simple docstring'''


      lowercase														=			get_args()
      set_seed(args.seed	)

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

      print("""Loading tokenizer and model"""	)
      lowercase														=			AutoTokenizer.from_pretrained(args.model_ckpt	)
      lowercase														=			tokenizer.eos_token
      lowercase														=			AutoModelForSequenceClassification.from_pretrained(args.model_ckpt      ,	num_labels=7	)
      lowercase														=			model.config.eos_token_id

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

      lowercase														=			ClassLabel(num_classes=7      ,	names=list(set(train_test_validation["""train"""]["""complexity"""]	)	)	)

      def tokenize(lowerCAmelCase__  :str	):
            lowercase														=			tokenizer(example["""src"""]      ,	truncation=lowerCAmelCase__      ,	max_length=1_0_2_4	)
            lowercase														=			labels.straint(example["""complexity"""]	)
            return {
                "input_ids": inputs["input_ids"],
                "attention_mask": inputs["attention_mask"],
                "label": label,
            }

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

      lowercase														=			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"""      ,	)

      lowercase														=			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 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					: Tuple        =logging.get_logger(__name__)


__lowerCAmelCase					: 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					: Optional[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_0_2_4,
    """facebook/bart-large""": 1_0_2_4,
    """facebook/bart-large-mnli""": 1_0_2_4,
    """facebook/bart-large-cnn""": 1_0_2_4,
    """facebook/bart-large-xsum""": 1_0_2_4,
    """yjernite/bart_eli5""": 1_0_2_4,
}





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Optional[int]													=			VOCAB_FILES_NAMES
      snake_case__    :     Tuple													=			PRETRAINED_VOCAB_FILES_MAP
      snake_case__    :     Tuple													=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__    :     Optional[Any]													=			['input_ids', 'attention_mask']
      snake_case__    :     Dict													=			BartTokenizer

      def __init__(		self	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase="replace"	, __lowerCAmelCase="<s>"	, __lowerCAmelCase="</s>"	, __lowerCAmelCase="</s>"	, __lowerCAmelCase="<s>"	, __lowerCAmelCase="<unk>"	, __lowerCAmelCase="<pad>"	, __lowerCAmelCase="<mask>"	, __lowerCAmelCase=False	, __lowerCAmelCase=True	, **__lowerCAmelCase	, ):







            """simple docstring"""



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

            lowercase														=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
            if pre_tok_state.get("""add_prefix_space"""	, __lowerCAmelCase		) != add_prefix_space:
                  lowercase														=			getattr(__lowerCAmelCase	, pre_tok_state.pop("""type"""		)		)
                  lowercase														=			add_prefix_space
                  lowercase														=			pre_tok_class(**__lowerCAmelCase		)

            lowercase														=			add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            lowercase														=			"""post_processor"""
            lowercase														=			getattr(self.backend_tokenizer	, __lowerCAmelCase	, __lowerCAmelCase		)
            if tokenizer_component_instance:
                  lowercase														=			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:
                        lowercase														=			tuple(state["""sep"""]		)
                  if "cls" in state:
                        lowercase														=			tuple(state["""cls"""]		)

                  lowercase														=			False

                  if state.get("""add_prefix_space"""	, __lowerCAmelCase		) != add_prefix_space:
                        lowercase														=			add_prefix_space
                        lowercase														=			True

                  if state.get("""trim_offsets"""	, __lowerCAmelCase		) != trim_offsets:
                        lowercase														=			trim_offsets
                        lowercase														=			True

                  if changes_to_apply:
                        lowercase														=			getattr(__lowerCAmelCase	, state.pop("""type"""		)		)
                        lowercase														=			component_class(**__lowerCAmelCase		)
                        setattr(self.backend_tokenizer	, __lowerCAmelCase	, __lowerCAmelCase		)

      @property
      def        A__ (		self		):







            """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        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			AddedToken(__lowerCAmelCase	, lstrip=__lowerCAmelCase	, rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase	, __lowerCAmelCase		) else value
            lowercase														=			value

      def        A__ (		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			kwargs.get("""is_split_into_words"""	, __lowerCAmelCase		)

            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(*__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			kwargs.get("""is_split_into_words"""	, __lowerCAmelCase		)

            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(*__lowerCAmelCase	, **__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



            lowercase														=			self._tokenizer.model.save(__lowerCAmelCase	, name=__lowerCAmelCase		)
            return tuple(__lowerCAmelCase		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase=None		):







            """simple docstring"""



            lowercase														=			[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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



            lowercase														=			[self.sep_token_id]
            lowercase														=			[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]



