






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      snake_case		(							):
				A	 =      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=0.01   )
				parser.add_argument('--output_dir',						type=UpperCAmelCase,						default='./results'   )
				return parser.parse_args()


lowerCAmelCase_           = load('accuracy')







def      snake_case		(							UpperCAmelCase	:       Optional[int]   ):
				A ,		A	 =      eval_pred
				A	 =      np.argmax(UpperCAmelCase,						axis=1   )
				return metric.compute(predictions=UpperCAmelCase,						references=UpperCAmelCase   )
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def __init__(  self		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   None:


						'''simple docstring'''
						super().__init__()
						A	 =      trainer







		def       A(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       List[Any]						,**_SCREAMING_SNAKE_CASE		:       str		)    ->   Optional[Any]:


						'''simple docstring'''
						if control.should_evaluate:
										A	 =      deepcopy(_SCREAMING_SNAKE_CASE		)
										self._trainer.evaluate(eval_dataset=self._trainer.train_dataset						,metric_key_prefix='train'		)
										return control_copy







def      snake_case		(							):
				A	 =      get_args()
				set_seed(args.seed   )

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

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

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

				A	 =      ClassLabel(num_classes=7,						names=list(set(train_test_validation['train']['complexity']   )   )   )

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

				A	 =      train_test_validation.map(
				    UpperCAmelCase,						batched=UpperCAmelCase,						remove_columns=train_test_validation['train'].column_names,						)
				A	 =      DataCollatorWithPadding(tokenizer=UpperCAmelCase   )

				A	 =      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	 =      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()













import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


lowerCAmelCase_           = logging.get_logger(__name__)

lowerCAmelCase_           = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_           = {
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase_           = {
    'gpt-neox-20b': 2_048,
}
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   VOCAB_FILES_NAMES
		snake_case    =   PRETRAINED_VOCAB_FILES_MAP
		snake_case    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		snake_case    =   ["input_ids", "attention_mask"]
		def __init__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=None						,_SCREAMING_SNAKE_CASE		:       Dict="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       int="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       Optional[int]="<|endoftext|>"						,_SCREAMING_SNAKE_CASE		:       List[Any]=False						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,)    ->   int:


						'''simple docstring'''
						super().__init__(
						    _SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE						,tokenizer_file=_SCREAMING_SNAKE_CASE						,unk_token=_SCREAMING_SNAKE_CASE						,bos_token=_SCREAMING_SNAKE_CASE						,eos_token=_SCREAMING_SNAKE_CASE						,add_prefix_space=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE						,)

						A	 =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
						if pre_tok_state.get('add_prefix_space'						,_SCREAMING_SNAKE_CASE		) != add_prefix_space:
										A	 =      getattr(_SCREAMING_SNAKE_CASE						,pre_tok_state.pop('type'		)		)
										A	 =      add_prefix_space
										A	 =      pre_tok_class(**_SCREAMING_SNAKE_CASE		)

						A	 =      add_prefix_space
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       str						,_SCREAMING_SNAKE_CASE		:       Optional[str] = None		)    ->   Tuple[str]:


						'''simple docstring'''
						A	 =      self._tokenizer.model.save(_SCREAMING_SNAKE_CASE						,name=_SCREAMING_SNAKE_CASE		)
						return tuple(_SCREAMING_SNAKE_CASE		)







		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       "Conversation"		)    ->   List[int]:


						'''simple docstring'''
						A	 =      []
						for is_user, text in conversation.iter_texts():
										input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE						,add_special_tokens=_SCREAMING_SNAKE_CASE		) + [self.eos_token_id]		)

						if len(_SCREAMING_SNAKE_CASE		) > self.model_max_length:
										A	 =      input_ids[-self.model_max_length :]
						return input_ids





