



'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

UpperCamelCase__:     Optional[Any]										=    logging.get_logger(__name__)


def 				snake_case_  (      _lowerCAmelCase						:		Optional[int]						)   ->     List[List[ImageInput]]:
							if isinstance(_lowerCAmelCase     ,   (list, tuple)						) and isinstance(videos[0]     ,   (list, tuple)						) and is_valid_image(videos[0][0]						):
														return videos

							elif isinstance(_lowerCAmelCase     ,   (list, tuple)						) and is_valid_image(videos[0]						):
														return [videos]

							elif is_valid_image(_lowerCAmelCase						):
														return [[videos]]

							raise ValueError(f"""Could not make batched video from {videos}"""						)




class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= ["""pixel_values"""]







	def __init__(		self    :  int	,    __snake_case    :  bool = True	,    __snake_case    :  Dict[str, int] = None	,    __snake_case    :  PILImageResampling = PILImageResampling.BILINEAR	,    __snake_case    :  bool = True	,    __snake_case    :  Dict[str, int] = None	,    __snake_case    :  bool = True	,    __snake_case    :  Union[int, float] = 1 / 255	,    __snake_case    :  bool = True	,    __snake_case    :  bool = True	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    **__snake_case    :  Dict	,    )    ->	None:
								super().__init__(**__snake_case	)
								UpperCAmelCase				: Tuple											=	size if size is not None else {'''shortest_edge''': 256}
								UpperCAmelCase				: Union[str, Any]											=	get_size_dict(__snake_case	,    default_to_square=__snake_case	)
								UpperCAmelCase				: int											=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								UpperCAmelCase				: List[str]											=	get_size_dict(__snake_case	,    param_name='''crop_size'''	)

								UpperCAmelCase				: str											=	do_resize
								UpperCAmelCase				: Any											=	size
								UpperCAmelCase				: Optional[Any]											=	do_center_crop
								UpperCAmelCase				: int											=	crop_size
								UpperCAmelCase				: Union[str, Any]											=	resample
								UpperCAmelCase				: str											=	do_rescale
								UpperCAmelCase				: Union[str, Any]											=	rescale_factor
								UpperCAmelCase				: str											=	offset
								UpperCAmelCase				: str											=	do_normalize
								UpperCAmelCase				: Any											=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase				: Optional[int]											=	image_std if image_std is not None else IMAGENET_STANDARD_STD







	def   A			(		self    :  List[str]	,    __snake_case    :  np.ndarray	,    __snake_case    :  Dict[str, int]	,    __snake_case    :  PILImageResampling = PILImageResampling.BILINEAR	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  List[Any]	,    )    ->	np.ndarray:
								UpperCAmelCase				: Dict											=	get_size_dict(__snake_case	,    default_to_square=__snake_case	)
								if "shortest_edge" in size:
															UpperCAmelCase				: List[str]											=	get_resize_output_image_size(__snake_case	,    size['''shortest_edge''']	,    default_to_square=__snake_case	)
								elif "height" in size and "width" in size:
															UpperCAmelCase				: Optional[Any]											=	(size['''height'''], size['''width'''])
								else:
															raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""	)
								return resize(__snake_case	,    size=__snake_case	,    resample=__snake_case	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  Tuple	,    __snake_case    :  np.ndarray	,    __snake_case    :  Dict[str, int]	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  Dict	,    )    ->	np.ndarray:
								UpperCAmelCase				: List[str]											=	get_size_dict(__snake_case	)
								if "height" not in size or "width" not in size:
															raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""	)
								return center_crop(__snake_case	,    size=(size['''height'''], size['''width'''])	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  List[Any]	,    __snake_case    :  np.ndarray	,    __snake_case    :  Union[int, float]	,    __snake_case    :  bool = True	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  Dict	,    )    ->	str:
								UpperCAmelCase				: Any											=	image.astype(np.floataa	)
								if offset:
															UpperCAmelCase				: Optional[Any]											=	image - (scale / 2)
								return rescale(__snake_case	,    scale=__snake_case	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  str	,    __snake_case    :  np.ndarray	,    __snake_case    :  Union[float, List[float]]	,    __snake_case    :  Union[float, List[float]]	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  Any	,    )    ->	np.ndarray:
								return normalize(__snake_case	,    mean=__snake_case	,    std=__snake_case	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  Optional[int]	,    __snake_case    :  ImageInput	,    __snake_case    :  bool = None	,    __snake_case    :  Dict[str, int] = None	,    __snake_case    :  PILImageResampling = None	,    __snake_case    :  bool = None	,    __snake_case    :  Dict[str, int] = None	,    __snake_case    :  bool = None	,    __snake_case    :  float = None	,    __snake_case    :  bool = None	,    __snake_case    :  bool = None	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  Optional[ChannelDimension] = ChannelDimension.FIRST	,    )    ->	np.ndarray:
								if do_resize and size is None or resample is None:
															raise ValueError('''Size and resample must be specified if do_resize is True.'''	)

								if do_center_crop and crop_size is None:
															raise ValueError('''Crop size must be specified if do_center_crop is True.'''	)

								if do_rescale and rescale_factor is None:
															raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

								if do_normalize and (image_mean is None or image_std is None):
															raise ValueError('''Image mean and std must be specified if do_normalize is True.'''	)

								if offset and not do_rescale:
															raise ValueError('''For offset, do_rescale must also be set to True.'''	)

								# All transformations expect numpy arrays.
								UpperCAmelCase				: List[str]											=	to_numpy_array(__snake_case	)

								if do_resize:
															UpperCAmelCase				: str											=	self.resize(image=__snake_case	,    size=__snake_case	,    resample=__snake_case	)

								if do_center_crop:
															UpperCAmelCase				: Optional[int]											=	self.center_crop(__snake_case	,    size=__snake_case	)

								if do_rescale:
															UpperCAmelCase				: int											=	self.rescale(image=__snake_case	,    scale=__snake_case	,    offset=__snake_case	)

								if do_normalize:
															UpperCAmelCase				: List[str]											=	self.normalize(image=__snake_case	,    mean=__snake_case	,    std=__snake_case	)

								UpperCAmelCase				: Union[str, Any]											=	to_channel_dimension_format(__snake_case	,    __snake_case	)
								return image







	def   A			(		self    :  List[Any]	,    __snake_case    :  ImageInput	,    __snake_case    :  bool = None	,    __snake_case    :  Dict[str, int] = None	,    __snake_case    :  PILImageResampling = None	,    __snake_case    :  bool = None	,    __snake_case    :  Dict[str, int] = None	,    __snake_case    :  bool = None	,    __snake_case    :  float = None	,    __snake_case    :  bool = None	,    __snake_case    :  bool = None	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  Optional[Union[str, TensorType]] = None	,    __snake_case    :  ChannelDimension = ChannelDimension.FIRST	,    **__snake_case    :  Tuple	,    )    ->	PIL.Image.Image:
								UpperCAmelCase				: List[Any]											=	do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase				: Union[str, Any]											=	resample if resample is not None else self.resample
								UpperCAmelCase				: List[str]											=	do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase				: List[Any]											=	do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase				: Optional[int]											=	rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase				: List[Any]											=	offset if offset is not None else self.offset
								UpperCAmelCase				: Union[str, Any]											=	do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase				: Any											=	image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase				: List[str]											=	image_std if image_std is not None else self.image_std

								UpperCAmelCase				: int											=	size if size is not None else self.size
								UpperCAmelCase				: List[Any]											=	get_size_dict(__snake_case	,    default_to_square=__snake_case	)
								UpperCAmelCase				: int											=	crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase				: Any											=	get_size_dict(__snake_case	,    param_name='''crop_size'''	)

								if not valid_images(__snake_case	):
															raise ValueError(
															    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
															    '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

								UpperCAmelCase				: Tuple											=	make_batched(__snake_case	)

								UpperCAmelCase				: List[str]											=	[
								    [
								        self._preprocess_image(
								            image=__snake_case	,    do_resize=__snake_case	,    size=__snake_case	,    resample=__snake_case	,    do_center_crop=__snake_case	,    crop_size=__snake_case	,    do_rescale=__snake_case	,    rescale_factor=__snake_case	,    offset=__snake_case	,    do_normalize=__snake_case	,    image_mean=__snake_case	,    image_std=__snake_case	,    data_format=__snake_case	,    )
								        for img in video
								    ]
								    for video in videos
								]

								UpperCAmelCase				: str											=	{'''pixel_values''': videos}
								return BatchFeature(data=__snake_case	,    tensor_type=__snake_case	)










'''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 				snake_case_  (      )   ->     Optional[int]:
							UpperCAmelCase				: Any											=	argparse.ArgumentParser()
							parser.add_argument('''--model_ckpt'''     ,   type=_lowerCAmelCase     ,   default='''microsoft/unixcoder-base-nine'''						)
							parser.add_argument('''--num_epochs'''     ,   type=_lowerCAmelCase     ,   default=5						)
							parser.add_argument('''--batch_size'''     ,   type=_lowerCAmelCase     ,   default=6						)
							parser.add_argument('''--gradient_accumulation_steps'''     ,   type=_lowerCAmelCase     ,   default=1						)
							parser.add_argument('''--freeze'''     ,   type=_lowerCAmelCase     ,   default=_lowerCAmelCase						)
							parser.add_argument('''--learning_rate'''     ,   type=_lowerCAmelCase     ,   default=5e-4						)
							parser.add_argument('''--seed'''     ,   type=_lowerCAmelCase     ,   default=0						)
							parser.add_argument('''--lr_scheduler_type'''     ,   type=_lowerCAmelCase     ,   default='''cosine'''						)
							parser.add_argument('''--num_warmup_steps'''     ,   type=_lowerCAmelCase     ,   default=10						)
							parser.add_argument('''--weight_decay'''     ,   type=_lowerCAmelCase     ,   default=0.0_1						)
							parser.add_argument('''--output_dir'''     ,   type=_lowerCAmelCase     ,   default='''./results'''						)
							return parser.parse_args()


UpperCamelCase__:     Any										=    load("accuracy")


def 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     int:
							UpperCAmelCase							,	UpperCAmelCase				: Dict											=	eval_pred
							UpperCAmelCase				: Optional[Any]											=	np.argmax(_lowerCAmelCase     ,   axis=1						)
							return metric.compute(predictions=_lowerCAmelCase     ,   references=_lowerCAmelCase						)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  List[Any]	,    __snake_case    :  Optional[int]	)    ->	None:
								super().__init__()
								UpperCAmelCase				: Dict											=	trainer







	def   A			(		self    :  str	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  int	,    __snake_case    :  Any	,    **__snake_case    :  List[Any]	)    ->	Dict:
								if control.should_evaluate:
															UpperCAmelCase				: Optional[int]											=	deepcopy(__snake_case	)
															self._trainer.evaluate(eval_dataset=self._trainer.train_dataset	,    metric_key_prefix='''train'''	)
															return control_copy


def 				snake_case_  (      )   ->     List[Any]:
							UpperCAmelCase				: Tuple											=	get_args()
							set_seed(args.seed						)

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

							print('''Loading tokenizer and model'''						)
							UpperCAmelCase				: Tuple											=	AutoTokenizer.from_pretrained(args.model_ckpt						)
							UpperCAmelCase				: Optional[int]											=	tokenizer.eos_token
							UpperCAmelCase				: Optional[int]											=	AutoModelForSequenceClassification.from_pretrained(args.model_ckpt     ,   num_labels=7						)
							UpperCAmelCase				: List[Any]											=	model.config.eos_token_id

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

							UpperCAmelCase				: Optional[Any]											=	ClassLabel(num_classes=7     ,   names=list(set(train_test_validation['''train''']['''complexity''']						)						)						)

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

							UpperCAmelCase				: List[str]											=	train_test_validation.map(
							    _lowerCAmelCase     ,   batched=_lowerCAmelCase     ,   remove_columns=train_test_validation['''train'''].column_names     ,   )
							UpperCAmelCase				: int											=	DataCollatorWithPadding(tokenizer=_lowerCAmelCase						)

							UpperCAmelCase				: Tuple											=	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=0.0_1     ,   metric_for_best_model='''accuracy'''     ,   run_name='''complexity-java'''     ,   report_to='''wandb'''     ,   )

							UpperCAmelCase				: int											=	Trainer(
							    model=_lowerCAmelCase     ,   args=_lowerCAmelCase     ,   train_dataset=tokenized_datasets['''train''']     ,   eval_dataset=tokenized_datasets['''valid''']     ,   tokenizer=_lowerCAmelCase     ,   data_collator=_lowerCAmelCase     ,   compute_metrics=_lowerCAmelCase     ,   )

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


if __name__ == "__main__":
						main()





