'''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_							(					)    ->    Optional[int]:


       """simple docstring"""







       _UpperCAmelCase     :  Any							      =    argparse.ArgumentParser()
       parser.add_argument("--model_ckpt"			,	type=_UpperCAmelCase			,	default="microsoft/unixcoder-base-nine"    )
       parser.add_argument("--num_epochs"			,	type=_UpperCAmelCase			,	default=5    )
       parser.add_argument("--batch_size"			,	type=_UpperCAmelCase			,	default=6    )
       parser.add_argument("--gradient_accumulation_steps"			,	type=_UpperCAmelCase			,	default=1    )
       parser.add_argument("--freeze"			,	type=_UpperCAmelCase			,	default=_UpperCAmelCase    )
       parser.add_argument("--learning_rate"			,	type=_UpperCAmelCase			,	default=5e-4    )
       parser.add_argument("--seed"			,	type=_UpperCAmelCase			,	default=0    )
       parser.add_argument("--lr_scheduler_type"			,	type=_UpperCAmelCase			,	default="cosine"    )
       parser.add_argument("--num_warmup_steps"			,	type=_UpperCAmelCase			,	default=10    )
       parser.add_argument("--weight_decay"			,	type=_UpperCAmelCase			,	default=0.0_1    )
       parser.add_argument("--output_dir"			,	type=_UpperCAmelCase			,	default="./results"    )
       return parser.parse_args()


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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Tuple    )    ->    List[Any]:


       """simple docstring"""







       _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[Any]							      =    eval_pred
       _UpperCAmelCase     :  Any							      =    np.argmax(_UpperCAmelCase			,	axis=1    )
       return metric.compute(predictions=_UpperCAmelCase			,	references=_UpperCAmelCase    )

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[int]						,					A						:				Optional[Any]       ):
             super().__init__()
             _UpperCAmelCase     :  int							      =    trainer





      def      _A	(       self						:				Optional[Any]						,					A						:				List[str]						,					A						:				Union[str, Any]						,					A						:				int						,					**A						:				int       ):
             if control.should_evaluate:
                    _UpperCAmelCase     :  Any							      =    deepcopy(A       )
                    self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,					metric_key_prefix="train"       )
                    return control_copy

def 	UpperCamelCase_							(					)    ->    Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    get_args()
       set_seed(args.seed    )

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

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

       if args.freeze:
              for param in model.roberta.parameters():
                     _UpperCAmelCase     :  Union[str, Any]							      =    False

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

       def tokenize(_UpperCAmelCase  :      Dict    ):
              _UpperCAmelCase     :  Dict							      =    tokenizer(example["src"]			,	truncation=_UpperCAmelCase			,	max_length=1_024    )
              _UpperCAmelCase     :  Tuple							      =    labels.straint(example["complexity"]    )
              return {
                  "input_ids": inputs["input_ids"],
                  "attention_mask": inputs["attention_mask"],
                  "label": label,
              }

       _UpperCAmelCase     :  List[Any]							      =    train_test_validation.map(
           _UpperCAmelCase			,	batched=_UpperCAmelCase			,	remove_columns=train_test_validation["train"].column_names			,	)
       _UpperCAmelCase     :  List[Any]							      =    DataCollatorWithPadding(tokenizer=_UpperCAmelCase    )

       _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=0.0_1			,	metric_for_best_model="accuracy"			,	run_name="complexity-java"			,	report_to="wandb"			,	)

       _UpperCAmelCase     :  Union[str, Any]							      =    Trainer(
           model=_UpperCAmelCase			,	args=_UpperCAmelCase			,	train_dataset=tokenized_datasets["train"]			,	eval_dataset=tokenized_datasets["valid"]			,	tokenizer=_UpperCAmelCase			,	data_collator=_UpperCAmelCase			,	compute_metrics=_UpperCAmelCase			,	)

       print("Training..."    )
       trainer.add_callback(CustomCallback(_UpperCAmelCase    )    )
       trainer.train()


if __name__ == "__main__":
  main()


'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     str     =  {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE       :     Dict     =  {
    """vocab_file""": {
        """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt""",
        """distilbert-base-uncased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"""
        ),
        """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt""",
        """distilbert-base-cased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"""
        ),
        """distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt""",
        """distilbert-base-multilingual-cased""": (
            """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json""",
        """distilbert-base-uncased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"""
        ),
        """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json""",
        """distilbert-base-cased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"""
        ),
        """distilbert-base-german-cased""": (
            """https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"""
        ),
        """distilbert-base-multilingual-cased""": (
            """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"""
        ),
    },
}

__SCREAMING_SNAKE_CASE       :     Optional[Any]     =  {
    """distilbert-base-uncased""": 512,
    """distilbert-base-uncased-distilled-squad""": 512,
    """distilbert-base-cased""": 512,
    """distilbert-base-cased-distilled-squad""": 512,
    """distilbert-base-german-cased""": 512,
    """distilbert-base-multilingual-cased""": 512,
}


__SCREAMING_SNAKE_CASE       :     List[Any]     =  {
    """distilbert-base-uncased""": {"""do_lower_case""": True},
    """distilbert-base-uncased-distilled-squad""": {"""do_lower_case""": True},
    """distilbert-base-cased""": {"""do_lower_case""": False},
    """distilbert-base-cased-distilled-squad""": {"""do_lower_case""": False},
    """distilbert-base-german-cased""": {"""do_lower_case""": False},
    """distilbert-base-multilingual-cased""": {"""do_lower_case""": False},
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]						=						VOCAB_FILES_NAMES
      __UpperCamelCase:  str						=						PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase:  Optional[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase:  Any						=						PRETRAINED_INIT_CONFIGURATION
      __UpperCamelCase:  str						=						["input_ids", "attention_mask"]
      __UpperCamelCase:  List[str]						=						DistilBertTokenizer




      def __init__(       self						:				str						,					A						:				int=None						,					A						:				Tuple=None						,					A						:				Tuple=True						,					A						:				Dict="[UNK]"						,					A						:				List[Any]="[SEP]"						,					A						:				Optional[Any]="[PAD]"						,					A						:				Dict="[CLS]"						,					A						:				Tuple="[MASK]"						,					A						:				str=True						,					A						:				Dict=None						,					**A						:				List[Any]						,					):
             super().__init__(
                 A						,					tokenizer_file=A						,					do_lower_case=A						,					unk_token=A						,					sep_token=A						,					pad_token=A						,					cls_token=A						,					mask_token=A						,					tokenize_chinese_chars=A						,					strip_accents=A						,					**A						,					)

             _UpperCAmelCase     :  str							      =    json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
             if (
                 normalizer_state.get("lowercase"						,					A       ) != do_lower_case
                 or normalizer_state.get("strip_accents"						,					A       ) != strip_accents
                 or normalizer_state.get("handle_chinese_chars"						,					A       ) != tokenize_chinese_chars
             ):
                    _UpperCAmelCase     :  Dict							      =    getattr(A						,					normalizer_state.pop("type"       )       )
                    _UpperCAmelCase     :  int							      =    do_lower_case
                    _UpperCAmelCase     :  Optional[int]							      =    strip_accents
                    _UpperCAmelCase     :  str							      =    tokenize_chinese_chars
                    _UpperCAmelCase     :  List[Any]							      =    normalizer_class(**A       )

             _UpperCAmelCase     :  Dict							      =    do_lower_case




      def      _A	(       self						:				List[Any]						,					A						:				Tuple						,					A						:				Any=None       ):
             _UpperCAmelCase     :  Optional[int]							      =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

             if token_ids_a:
                    output += token_ids_a + [self.sep_token_id]

             return output




      def      _A	(       self						:				int						,					A						:				List[int]						,					A						:				Optional[List[int]] = None       ):
             _UpperCAmelCase     :  Any							      =    [self.sep_token_id]
             _UpperCAmelCase     :  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       ) * [0] + len(token_ids_a + sep       ) * [1]





      def      _A	(       self						:				Dict						,					A						:				str						,					A						:				Optional[str] = None       ):
             _UpperCAmelCase     :  Any							      =    self._tokenizer.model.save(A						,					name=A       )
             return tuple(A       )

