


"""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 _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			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()


A     :   Dict			    =			load("accuracy")


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			eval_pred
			__lowerCAmelCase				    =			np.argmax(UpperCamelCase__    , axis=1      )
			return metric.compute(predictions=UpperCamelCase__    , references=UpperCamelCase__      )
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def __init__(     self    ,   __a	):
										super().__init__()
										__lowerCAmelCase				    =			trainer



							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   **__a	):
										if control.should_evaluate:
													__lowerCAmelCase				    =			deepcopy(__lowerCamelCase	)
													self._trainer.evaluate(eval_dataset=self._trainer.train_dataset    ,   metric_key_prefix="train"	)
													return control_copy


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			get_args()
			set_seed(args.seed      )

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

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

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

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

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

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

			__lowerCAmelCase				    =			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"    , )

			__lowerCAmelCase				    =			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()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase__      		= {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['SpeechT5Tokenizer']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= [
        'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
        'SpeechT5ForSpeechToText',
        'SpeechT5ForSpeechToSpeech',
        'SpeechT5ForTextToSpeech',
        'SpeechT5Model',
        'SpeechT5PreTrainedModel',
        'SpeechT5HifiGan',
    ]

if TYPE_CHECKING:
    from .configuration_speechta import (
        SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
        SpeechTaConfig,
        SpeechTaHifiGanConfig,
    )
    from .feature_extraction_speechta import SpeechTaFeatureExtractor
    from .processing_speechta import SpeechTaProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speechta import SpeechTaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speechta import (
            SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechTaForSpeechToSpeech,
            SpeechTaForSpeechToText,
            SpeechTaForTextToSpeech,
            SpeechTaHifiGan,
            SpeechTaModel,
            SpeechTaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__      		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
