

"""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   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Optional[Any]  				=	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  :      List[Any]							      =		load('''accuracy''')

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	eval_pred
	lowerCAmelCase__	:						str  				=	np.argmax(A_						,     axis=1 )
	return metric.compute(predictions=A_						,     references=A_ )







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Optional[Any]   ,lowercase_			:				Optional[Any]		):
						super().__init__()
						lowerCAmelCase__	:						List[Any]  				=	trainer







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				str   ,lowercase_			:				Any   ,lowercase_			:				Dict   ,**lowercase_			:				Dict		):
						if control.should_evaluate:
							lowerCAmelCase__	:						Tuple  				=	deepcopy(lowercase_		)
							self._trainer.evaluate(eval_dataset=self._trainer.train_dataset   ,metric_key_prefix='''train'''		)
							return control_copy

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						int  				=	get_args()
	set_seed(args.seed )

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

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

	if args.freeze:
		for param in model.roberta.parameters():
			lowerCAmelCase__	:						Optional[int]  				=	False

	lowerCAmelCase__	:						Union[str, Any]  				=	ClassLabel(num_classes=7						,     names=list(set(train_test_validation['''train''']['''complexity'''] ) ) )

	def tokenize(A_ ):
		lowerCAmelCase__	:						Dict  				=	tokenizer(example['''src''']						,     truncation=A_						,     max_length=10_24 )
		lowerCAmelCase__	:						str  				=	labels.straint(example['''complexity'''] )
		return {
		    "input_ids": inputs["input_ids"],
		    "attention_mask": inputs["attention_mask"],
		    "label": label,
		}

	lowerCAmelCase__	:						Optional[int]  				=	train_test_validation.map(
	    A_						,     batched=A_						,     remove_columns=train_test_validation['''train'''].column_names						,     )
	lowerCAmelCase__	:						Optional[int]  				=	DataCollatorWithPadding(tokenizer=A_ )

	lowerCAmelCase__	:						Union[str, Any]  				=	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__	:						Any  				=	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()







"""simple docstring"""





import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY







@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				int   ,lowercase_			:				str		):
						lowerCAmelCase__	:						Optional[int]  				=	hf_hub_download(
						    repo_id='''nateraw/video-demo'''   ,filename='''archery.mp4'''   ,repo_type='''dataset'''		)
						lowerCAmelCase__	:						Tuple  				=	VideoClassificationPipeline(model=lowercase_   ,image_processor=lowercase_   ,top_k=2		)
						lowerCAmelCase__	:						Optional[int]  				=	[
						    example_video_filepath,
						    '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
						]
						return video_classifier, examples







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				int   ,lowercase_			:				Dict		):
						for example in examples:
							lowerCAmelCase__	:						Dict  				=	video_classifier(lowercase_		)

							self.assertEqual(
							    lowercase_   ,[
							        {'''score''': ANY(lowercase_		), '''label''': ANY(lowercase_		)},
							        {'''score''': ANY(lowercase_		), '''label''': ANY(lowercase_		)},
							    ]   ,)







					@require_torch
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						Optional[int]  				=	'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
						lowerCAmelCase__	:						List[str]  				=	VideoMAEFeatureExtractor(
						    size={'''shortest_edge''': 1_0}   ,crop_size={'''height''': 1_0, '''width''': 1_0}		)
						lowerCAmelCase__	:						Optional[Any]  				=	pipeline(
						    '''video-classification'''   ,model=lowercase_   ,feature_extractor=lowercase_   ,frame_sampling_rate=4		)

						lowerCAmelCase__	:						Optional[int]  				=	hf_hub_download(repo_id='''nateraw/video-demo'''   ,filename='''archery.mp4'''   ,repo_type='''dataset'''		)
						lowerCAmelCase__	:						Optional[int]  				=	video_classifier(lowercase_   ,top_k=2		)
						self.assertEqual(
						    nested_simplify(lowercase_   ,decimals=4		)   ,[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}]   ,)

						lowerCAmelCase__	:						Dict  				=	video_classifier(
						    [
						        video_file_path,
						        video_file_path,
						    ]   ,top_k=2   ,)
						self.assertEqual(
						    nested_simplify(lowercase_   ,decimals=4		)   ,[
						        [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
						        [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
						    ]   ,)







					@require_tf
					def        __lowerCAmelCase (       self			:				int		):
						pass




