





UpperCamelCase__   : List[str]					  = """0.18.2"""

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
				if not is_onnx_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_onnx_objects import *  # noqa F403
else:
				from .pipelines import OnnxRuntimeModel

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_pt_objects import *  # noqa F403
else:
				from .models import (
				    AutoencoderKL,
				    ControlNetModel,
				    ModelMixin,
				    PriorTransformer,
				    TaFilmDecoder,
				    TransformeraDModel,
				    UNetaDModel,
				    UNetaDConditionModel,
				    UNetaDModel,
				    UNetaDConditionModel,
				    VQModel,
				)
				from .optimization import (
				    get_constant_schedule,
				    get_constant_schedule_with_warmup,
				    get_cosine_schedule_with_warmup,
				    get_cosine_with_hard_restarts_schedule_with_warmup,
				    get_linear_schedule_with_warmup,
				    get_polynomial_decay_schedule_with_warmup,
				    get_scheduler,
				)
				from .pipelines import (
				    AudioPipelineOutput,
				    ConsistencyModelPipeline,
				    DanceDiffusionPipeline,
				    DDIMPipeline,
				    DDPMPipeline,
				    DiffusionPipeline,
				    DiTPipeline,
				    ImagePipelineOutput,
				    KarrasVePipeline,
				    LDMPipeline,
				    LDMSuperResolutionPipeline,
				    PNDMPipeline,
				    RePaintPipeline,
				    ScoreSdeVePipeline,
				)
				from .schedulers import (
				    CMStochasticIterativeScheduler,
				    DDIMInverseScheduler,
				    DDIMParallelScheduler,
				    DDIMScheduler,
				    DDPMParallelScheduler,
				    DDPMScheduler,
				    DEISMultistepScheduler,
				    DPMSolverMultistepInverseScheduler,
				    DPMSolverMultistepScheduler,
				    DPMSolverSinglestepScheduler,
				    EulerAncestralDiscreteScheduler,
				    EulerDiscreteScheduler,
				    HeunDiscreteScheduler,
				    IPNDMScheduler,
				    KarrasVeScheduler,
				    KDPMaAncestralDiscreteScheduler,
				    KDPMaDiscreteScheduler,
				    PNDMScheduler,
				    RePaintScheduler,
				    SchedulerMixin,
				    ScoreSdeVeScheduler,
				    UnCLIPScheduler,
				    UniPCMultistepScheduler,
				    VQDiffusionScheduler,
				)
				from .training_utils import EMAModel

try:
				if not (is_torch_available() and is_scipy_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
				from .schedulers import LMSDiscreteScheduler

try:
				if not (is_torch_available() and is_torchsde_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
				from .schedulers import DPMSolverSDEScheduler

try:
				if not (is_torch_available() and is_transformers_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .pipelines import (
				    AltDiffusionImgaImgPipeline,
				    AltDiffusionPipeline,
				    AudioLDMPipeline,
				    CycleDiffusionPipeline,
				    IFImgaImgPipeline,
				    IFImgaImgSuperResolutionPipeline,
				    IFInpaintingPipeline,
				    IFInpaintingSuperResolutionPipeline,
				    IFPipeline,
				    IFSuperResolutionPipeline,
				    ImageTextPipelineOutput,
				    KandinskyImgaImgPipeline,
				    KandinskyInpaintPipeline,
				    KandinskyPipeline,
				    KandinskyPriorPipeline,
				    KandinskyVaaControlnetImgaImgPipeline,
				    KandinskyVaaControlnetPipeline,
				    KandinskyVaaImgaImgPipeline,
				    KandinskyVaaInpaintPipeline,
				    KandinskyVaaPipeline,
				    KandinskyVaaPriorEmbaEmbPipeline,
				    KandinskyVaaPriorPipeline,
				    LDMTextToImagePipeline,
				    PaintByExamplePipeline,
				    SemanticStableDiffusionPipeline,
				    ShapEImgaImgPipeline,
				    ShapEPipeline,
				    StableDiffusionAttendAndExcitePipeline,
				    StableDiffusionControlNetImgaImgPipeline,
				    StableDiffusionControlNetInpaintPipeline,
				    StableDiffusionControlNetPipeline,
				    StableDiffusionDepthaImgPipeline,
				    StableDiffusionDiffEditPipeline,
				    StableDiffusionImageVariationPipeline,
				    StableDiffusionImgaImgPipeline,
				    StableDiffusionInpaintPipeline,
				    StableDiffusionInpaintPipelineLegacy,
				    StableDiffusionInstructPixaPixPipeline,
				    StableDiffusionLatentUpscalePipeline,
				    StableDiffusionLDMaDPipeline,
				    StableDiffusionModelEditingPipeline,
				    StableDiffusionPanoramaPipeline,
				    StableDiffusionParadigmsPipeline,
				    StableDiffusionPipeline,
				    StableDiffusionPipelineSafe,
				    StableDiffusionPixaPixZeroPipeline,
				    StableDiffusionSAGPipeline,
				    StableDiffusionUpscalePipeline,
				    StableUnCLIPImgaImgPipeline,
				    StableUnCLIPPipeline,
				    TextToVideoSDPipeline,
				    TextToVideoZeroPipeline,
				    UnCLIPImageVariationPipeline,
				    UnCLIPPipeline,
				    UniDiffuserModel,
				    UniDiffuserPipeline,
				    UniDiffuserTextDecoder,
				    VersatileDiffusionDualGuidedPipeline,
				    VersatileDiffusionImageVariationPipeline,
				    VersatileDiffusionPipeline,
				    VersatileDiffusionTextToImagePipeline,
				    VideoToVideoSDPipeline,
				    VQDiffusionPipeline,
				)

try:
				if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
				from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
				if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
				from .pipelines import StableDiffusionKDiffusionPipeline

try:
				if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
				from .pipelines import (
				    OnnxStableDiffusionImgaImgPipeline,
				    OnnxStableDiffusionInpaintPipeline,
				    OnnxStableDiffusionInpaintPipelineLegacy,
				    OnnxStableDiffusionPipeline,
				    OnnxStableDiffusionUpscalePipeline,
				    StableDiffusionOnnxPipeline,
				)

try:
				if not (is_torch_available() and is_librosa_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
				from .pipelines import AudioDiffusionPipeline, Mel

try:
				if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
				from .pipelines import SpectrogramDiffusionPipeline

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_flax_objects import *  # noqa F403
else:
				from .models.controlnet_flax import FlaxControlNetModel
				from .models.modeling_flax_utils import FlaxModelMixin
				from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
				from .models.vae_flax import FlaxAutoencoderKL
				from .pipelines import FlaxDiffusionPipeline
				from .schedulers import (
				    FlaxDDIMScheduler,
				    FlaxDDPMScheduler,
				    FlaxDPMSolverMultistepScheduler,
				    FlaxKarrasVeScheduler,
				    FlaxLMSDiscreteScheduler,
				    FlaxPNDMScheduler,
				    FlaxSchedulerMixin,
				    FlaxScoreSdeVeScheduler,
				)


try:
				if not (is_flax_available() and is_transformers_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
				from .pipelines import (
				    FlaxStableDiffusionControlNetPipeline,
				    FlaxStableDiffusionImgaImgPipeline,
				    FlaxStableDiffusionInpaintPipeline,
				    FlaxStableDiffusionPipeline,
				)

try:
				if not (is_note_seq_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from .utils.dummy_note_seq_objects import *  # noqa F403
else:
				from .pipelines import MidiProcessor









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


UpperCamelCase__   : Any					  = load("""accuracy""")




def    A_(			A   ):
	UpperCAmelCase_						,	UpperCAmelCase_								=     eval_pred
	UpperCAmelCase_								=     np.argmax(A				,      axis=1   )
	return metric.compute(predictions=A				,      references=A   )







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					def __init__(						self    :  str ,					__lowercase    :  Any		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_								=     trainer





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  int ,					__lowercase    :  Tuple ,					__lowercase    :  Dict ,					**__lowercase    :  Dict		):






						'''simple docstring'''







						if control.should_evaluate:
							UpperCAmelCase_								=     deepcopy(__lowercase		)
							self._trainer.evaluate(eval_dataset=self._trainer.train_dataset ,					metric_key_prefix="""train"""		)
							return control_copy




def    A_(			):
	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(A   ):
		UpperCAmelCase_								=     tokenizer(example["""src"""]				,      truncation=A				,      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(
	    A				,      batched=A				,      remove_columns=train_test_validation["""train"""].column_names				,      )
	UpperCAmelCase_								=     DataCollatorWithPadding(tokenizer=A   )

	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=A				,      args=A				,      train_dataset=tokenized_datasets["""train"""]				,      eval_dataset=tokenized_datasets["""valid"""]				,      tokenizer=A				,      data_collator=A				,      compute_metrics=A				,      )

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


if __name__ == "__main__":
				main()


