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	(  ):
					_UpperCamelCase			     =     argparse.ArgumentParser()
					parser.add_argument('''--model_ckpt'''  ,     type=__snake_case  ,     default='''microsoft/unixcoder-base-nine'''						)
					parser.add_argument('''--num_epochs'''  ,     type=__snake_case  ,     default=5						)
					parser.add_argument('''--batch_size'''  ,     type=__snake_case  ,     default=6						)
					parser.add_argument('''--gradient_accumulation_steps'''  ,     type=__snake_case  ,     default=1						)
					parser.add_argument('''--freeze'''  ,     type=__snake_case  ,     default=__snake_case						)
					parser.add_argument('''--learning_rate'''  ,     type=__snake_case  ,     default=5E-4						)
					parser.add_argument('''--seed'''  ,     type=__snake_case  ,     default=0						)
					parser.add_argument('''--lr_scheduler_type'''  ,     type=__snake_case  ,     default='''cosine'''						)
					parser.add_argument('''--num_warmup_steps'''  ,     type=__snake_case  ,     default=10						)
					parser.add_argument('''--weight_decay'''  ,     type=__snake_case  ,     default=0.01						)
					parser.add_argument('''--output_dir'''  ,     type=__snake_case  ,     default='''./results'''						)
					return parser.parse_args()


_lowerCAmelCase       =		load("accuracy")
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase     ,    _UpperCamelCase			     =     eval_pred
					_UpperCamelCase			     =     np.argmax(__snake_case  ,     axis=1						)
					return metric.compute(predictions=__snake_case  ,     references=__snake_case						)
class     lowerCAmelCase_     (       __lowercase ):







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







							def  UpperCamelCase_						(   self      :     int ,			_A      :     List[str] ,			_A      :     List[str] ,			_A      :     Union[str, Any] ,			**_A      :     Tuple		):
												if control.should_evaluate:
																	_UpperCamelCase			     =     deepcopy(_A		)
																	self._trainer.evaluate(eval_dataset=self._trainer.train_dataset ,			metric_key_prefix='''train'''		)
																	return control_copy




def 					_snake_case	(  ):
					_UpperCamelCase			     =     get_args()
					set_seed(args.seed						)

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

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

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

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

					def tokenize(__snake_case						):
										_UpperCamelCase			     =     tokenizer(example['''src''']  ,     truncation=__snake_case  ,     max_length=1024						)
										_UpperCamelCase			     =     labels.straint(example['''complexity''']						)
										return {
										    "input_ids": inputs["input_ids"],
										    "attention_mask": inputs["attention_mask"],
										    "label": label,
										}

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

					_UpperCamelCase			     =     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'''  ,     )

					_UpperCamelCase			     =     Trainer(
					    model=__snake_case  ,     args=__snake_case  ,     train_dataset=tokenized_datasets['''train''']  ,     eval_dataset=tokenized_datasets['''valid''']  ,     tokenizer=__snake_case  ,     data_collator=__snake_case  ,     compute_metrics=__snake_case  ,     )

					print('''Training...'''						)
					trainer.add_callback(CustomCallback(__snake_case						)						)
					trainer.train()


if __name__ == "__main__":
					main()

import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_lowerCAmelCase       =		logging.get_logger(__name__)
def 					_snake_case	(  __snake_case=None  ,     __snake_case=None						):
					return field(default_factory=lambda: default  ,     metadata=__snake_case						)







@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     list_field(
							    default=[],  metadata={
							        "help": (
							            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
							            " of all available models"
							        )
							    },  )

							UpperCAmelCase				   =     list_field(
							    default=[8],  metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )

							UpperCAmelCase				   =     list_field(
							    default=[8, 32, 128, 512],  metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"},  )

							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Use FP16 to accelerate inference."} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Benchmark training of model"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Verbose memory tracing"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."},  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
							    },  )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Trace memory line by line"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Save result to a CSV file"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Save all print statements in a log file"} )
							UpperCAmelCase				   =     field(default=__lowercase,  metadata={"help": "Whether to print environment information"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": (
							            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
							            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
							            " for debugging / testing and on TPU."
							        )
							    },  )
							UpperCAmelCase				   =     field(
							    default=F"""inference_time_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving time results to csv."},  )
							UpperCAmelCase				   =     field(
							    default=F"""inference_memory_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving memory results to csv."},  )
							UpperCAmelCase				   =     field(
							    default=F"""train_time_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving time results to csv for training."},  )
							UpperCAmelCase				   =     field(
							    default=F"""train_memory_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving memory results to csv for training."},  )
							UpperCAmelCase				   =     field(
							    default=F"""env_info_{round(time() )}.csv""",  metadata={"help": "CSV filename used if saving environment information."},  )
							UpperCAmelCase				   =     field(
							    default=F"""log_{round(time() )}.csv""",  metadata={"help": "Log filename used if print statements are saved in log."},  )
							UpperCAmelCase				   =     field(default=3,  metadata={"help": "Times an experiment will be run."} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={
							        "help": (
							            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
							            " model weights."
							        )
							    },  )







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												warnings.warn(
												    F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
												    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
												    ''' to benchmark Transformer models.''' ,			_A ,			)







							def  UpperCamelCase_						(   self      :     str		):
												return json.dumps(dataclasses.asdict(self		) ,			indent=2		)







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												if len(self.models		) <= 0:
																	raise ValueError(
																	    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																	    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''		)
												return self.models







							@property
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												if not self.multi_process:
																	return False
												elif self.is_tpu:
																	logger.info('''Multiprocessing is currently not possible on TPU.'''		)
																	return False
												else:
																	return True
