


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


__lowercase      :		Optional[Any]			=      load('accuracy')






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Optional[int]  ):
					__a     :			List[str]          =    eval_pred
					__a     :			str          =    np.argmax(_lowercase		, axis=1  )
					return metric.compute(predictions=_lowercase		, references=_lowercase  )





class 				__UpperCamelCase							(						_a   ):




	def __init__(  self		,							__a				):

						'''simple docstring'''





						super().__init__()
						__a     :			List[Any]          =    trainer





	def  __UpperCAmelCase				(  self		,							__a		,							__a		,							__a		,							**__a				):

						'''simple docstring'''





						if control.should_evaluate:
											__a     :			Union[str, Any]          =    deepcopy(_SCREAMING_SNAKE_CASE				)
											self._trainer.evaluate(eval_dataset=self._trainer.train_dataset		,							metric_key_prefix='train'				)
											return control_copy






def     lowerCamelCase				():
					__a     :			str          =    get_args()
					set_seed(args.seed  )

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

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

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

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

					def tokenize(_SCREAMING_SNAKE_CASE :  List[str]  ):
										__a     :			Dict          =    tokenizer(example['src']		, truncation=_lowercase		, max_length=1_024  )
										__a     :			Tuple          =    labels.straint(example['complexity']  )
										return {
										    "input_ids": inputs["input_ids"],
										    "attention_mask": inputs["attention_mask"],
										    "label": label,
										}

					__a     :			Dict          =    train_test_validation.map(
					    _lowercase		, batched=_lowercase		, remove_columns=train_test_validation['train'].column_names		, )
					__a     :			List[Any]          =    DataCollatorWithPadding(tokenizer=_lowercase  )

					__a     :			int          =    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'		, )

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

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


if __name__ == "__main__":
	main()









# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
     from PIL import Image

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = (
        '''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
        '''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
        '''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
    )
    lowerCAmelCase         = '''CIDAS/clipseg-rd64-refined'''
    lowerCAmelCase         = '''image_segmenter'''
    lowerCAmelCase         = CLIPSegForImageSegmentation

    lowerCAmelCase         = ['''image''', '''text''']
    lowerCAmelCase         = ['''image''']





    def __init__( self  ,*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						Dict:
         requires_backends(self  ,['''vision''']    )
         super().__init__(*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Tuple:
         return self.pre_processor(text=[label]  ,images=[image]  ,padding=_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						str:
         with torch.no_grad():
              UpperCAmelCase_     :				Dict  =     self.model(**_SCREAMING_SNAKE_CASE    ).logits
         return logits





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						Dict:
         UpperCAmelCase_     :				Dict  =     outputs.cpu().detach().numpy()
         UpperCAmelCase_     :				Any  =     0
         UpperCAmelCase_     :				List[Any]  =     1
         return Image.fromarray((array * 255).astype(np.uinta    )    )