

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


A_			:					Dict    			=     logging.get_logger(__name__)

A_			:					Dict    			=     {'vocab_file': 'vocab.txt'}

A_			:					str    			=     {
    'vocab_file': {
        'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
        'YituTech/conv-bert-medium-small': (
            'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
        ),
        'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
    }
}

A_			:					Optional[int]    			=     {
    'YituTech/conv-bert-base': 512,
    'YituTech/conv-bert-medium-small': 512,
    'YituTech/conv-bert-small': 512,
}


A_			:					Tuple    			=     {
    'YituTech/conv-bert-base': {'do_lower_case': True},
    'YituTech/conv-bert-medium-small': {'do_lower_case': True},
    'YituTech/conv-bert-small': {'do_lower_case': True},
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  str    				=			VOCAB_FILES_NAMES
					UpperCAmelCase__:  Optional[Any]    				=			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:  str    				=			PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:  Optional[Any]    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:  List[Any]    				=			ConvBertTokenizer






					def __init__(					self       ,			A__=None       ,			A__=None       ,			A__=True       ,			A__="[UNK]"       ,			A__="[SEP]"       ,			A__="[PAD]"       ,			A__="[CLS]"       ,			A__="[MASK]"       ,			A__=True       ,			A__=None       ,			**A__       ,			):
						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__       ,			)

						A__				:       Tuple	  =						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
						):
							A__				:       Optional[Any]	  =						getattr(A__       ,			normalizer_state.pop("""type"""     )     )
							A__				:       Optional[int]	  =						do_lower_case
							A__				:       List[str]	  =						strip_accents
							A__				:       Optional[Any]	  =						tokenize_chinese_chars
							A__				:       int	  =						normalizer_class(**A__     )

						A__				:       str	  =						do_lower_case






					def     __A     (					self       ,			A__       ,			A__=None     ):
						A__				:       Tuple	  =						[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       ,			A__       ,			A__ = None     ):
						A__				:       int	  =						[self.sep_token_id]
						A__				:       List[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       ,			A__       ,			A__ = None     ):
						A__				:       Optional[Any]	  =						self._tokenizer.model.save(A__       ,			name=A__     )
						return tuple(A__     )






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   ()	-> Union[str, Any]:
	A__				:       int	  =						argparse.ArgumentParser()
	parser.add_argument("""--model_ckpt"""       , type=lowercase_       , default="""microsoft/unixcoder-base-nine"""   )
	parser.add_argument("""--num_epochs"""       , type=lowercase_       , default=5   )
	parser.add_argument("""--batch_size"""       , type=lowercase_       , default=6   )
	parser.add_argument("""--gradient_accumulation_steps"""       , type=lowercase_       , default=1   )
	parser.add_argument("""--freeze"""       , type=lowercase_       , default=lowercase_   )
	parser.add_argument("""--learning_rate"""       , type=lowercase_       , default=5E-4   )
	parser.add_argument("""--seed"""       , type=lowercase_       , default=0   )
	parser.add_argument("""--lr_scheduler_type"""       , type=lowercase_       , default="""cosine"""   )
	parser.add_argument("""--num_warmup_steps"""       , type=lowercase_       , default=10   )
	parser.add_argument("""--weight_decay"""       , type=lowercase_       , default=0.01   )
	parser.add_argument("""--output_dir"""       , type=lowercase_       , default="""./results"""   )
	return parser.parse_args()


A_			:					Any    			=     load('accuracy')

def 		UpperCamelCase   (lowercase_:					Dict   )	-> Any:
	A__  ,  A__				:       int	  =						eval_pred
	A__				:       List[str]	  =						np.argmax(lowercase_       , axis=1   )
	return metric.compute(predictions=lowercase_       , references=lowercase_   )



class        _a		(__magic_name__							):


					'''simple docstring'''






					def __init__(					self       ,			A__     ):
						super().__init__()
						A__				:       Any	  =						trainer






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			**A__     ):
						if control.should_evaluate:
							A__				:       Dict	  =						deepcopy(A__     )
							self._trainer.evaluate(eval_dataset=self._trainer.train_dataset       ,			metric_key_prefix="""train"""     )
							return control_copy



def 		UpperCamelCase   ()	-> Optional[int]:
	A__				:       Optional[Any]	  =						get_args()
	set_seed(args.seed   )

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

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

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

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

	def tokenize(lowercase_:					Optional[int]   ):
		A__				:       Union[str, Any]	  =						tokenizer(example["""src"""]       , truncation=lowercase_       , max_length=1024   )
		A__				:       Any	  =						labels.straint(example["""complexity"""]   )
		return {
		    "input_ids": inputs["input_ids"],
		    "attention_mask": inputs["attention_mask"],
		    "label": label,
		}

	A__				:       int	  =						train_test_validation.map(
	    lowercase_       , batched=lowercase_       , remove_columns=train_test_validation["""train"""].column_names       , )
	A__				:       List[str]	  =						DataCollatorWithPadding(tokenizer=lowercase_   )

	A__				:       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"""       , )

	A__				:       int	  =						Trainer(
	    model=lowercase_       , args=lowercase_       , train_dataset=tokenized_datasets["""train"""]       , eval_dataset=tokenized_datasets["""valid"""]       , tokenizer=lowercase_       , data_collator=lowercase_       , compute_metrics=lowercase_       , )

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


if __name__ == "__main__":
	main()



