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


_lowerCAmelCase       =		load("accuracy")
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase     ,    _UpperCamelCase			     =     eval_pred
					_UpperCamelCase			     =     np.argmax(__snake_case  ,     axis=1						)
					return metric.compute(predictions=__snake_case  ,     references=__snake_case						)
class     lowerCAmelCase_     (       __lowercase ):







							def __init__(   self      :     Optional[int] ,			_A      :     Any		):
												super().__init__()
												_UpperCamelCase			     =     trainer







							def  UpperCamelCase_						(   self      :     int ,			_A      :     List[str] ,			_A      :     List[str] ,			_A      :     Union[str, Any] ,			**_A      :     Tuple		):
												if control.should_evaluate:
																	_UpperCamelCase			     =     deepcopy(_A		)
																	self._trainer.evaluate(eval_dataset=self._trainer.train_dataset ,			metric_key_prefix='''train'''		)
																	return control_copy




def 					_snake_case	(  ):
					_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(__snake_case						):
										_UpperCamelCase			     =     tokenizer(example['''src''']  ,     truncation=__snake_case  ,     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(
					    __snake_case  ,     batched=__snake_case  ,     remove_columns=train_test_validation['''train'''].column_names  ,     )
					_UpperCamelCase			     =     DataCollatorWithPadding(tokenizer=__snake_case						)

					_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=__snake_case  ,     args=__snake_case  ,     train_dataset=tokenized_datasets['''train''']  ,     eval_dataset=tokenized_datasets['''valid''']  ,     tokenizer=__snake_case  ,     data_collator=__snake_case  ,     compute_metrics=__snake_case  ,     )

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


if __name__ == "__main__":
					main()




'''simple docstring'''






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
 from PIL import Image
else:






 class    __magic_name__				:






        @staticmethod
        def      lowerCAmelCase						(     *snake_case    ,				**snake_case)		->    str:




           '''simple docstring'''
           pass





def lowerCamelCase__     (		__lowerCamelCase			:				Image				):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =hashlib.mda(image.tobytes()				)
   return m.hexdigest()[:1_0]





def lowerCamelCase__     (		__lowerCamelCase			:				Image				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =np.array(__lowerCamelCase				)
   _UpperCAmelCase  :  List[str]				       =npimg.shape
   return {"hash": hashimage(__lowerCamelCase				), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class    __magic_name__				(						unittest.TestCase							):
       UpperCAmelCase		 =dict(
           (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
       UpperCAmelCase		 =dict(
           (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =MaskGenerationPipeline(model=snake_case    ,				image_processor=snake_case)
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          pass






       @require_tf
       @unittest.skip('Image segmentation not implemented in TF')
       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          pass






       @slow
       @require_torch
       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =pipeline('mask-generation'    ,				model='facebook/sam-vit-huge')

          _UpperCAmelCase  :  Union[str, Any]				       =image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'    ,				points_per_batch=2_5_6)

          # Shortening by hashing
          _UpperCAmelCase  :  str				       =[]
          for i, o in enumerate(outputs['masks']):
             new_outupt += [{"mask": mask_to_test_readable(snake_case), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(snake_case    ,				decimals=4)    ,				[
                  {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_21},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
                  {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_67},
                  {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_93},
                  {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_09},
                  {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_79},
                  {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_34},
                  {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.97_16},
                  {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.96_12},
                  {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_99},
                  {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_52},
                  {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_32},
                  {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_16},
                  {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_99},
                  {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_83},
                  {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_64},
                  {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                  {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                  {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_08},
                  {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_35},
                  {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_26},
                  {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.92_62},
                  {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_99},
                  {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_86},
                  {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_84},
                  {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_73},
                  {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_71}
              ]    ,				)
          # fmt: on






       @require_torch
       @slow
       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       ='facebook/sam-vit-huge'
          _UpperCAmelCase  :  Optional[int]				       =pipeline('mask-generation'    ,				model=snake_case)

          _UpperCAmelCase  :  str				       =image_segmenter(
              'http://images.cocodataset.org/val2017/000000039769.jpg'    ,				pred_iou_thresh=1    ,				points_per_batch=2_5_6)

          # Shortening by hashing
          _UpperCAmelCase  :  Tuple				       =[]
          for i, o in enumerate(outputs['masks']):
             new_outupt += [{"mask": mask_to_test_readable(snake_case), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(snake_case    ,				decimals=4)    ,				[
                  {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.02_10},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
              ]    ,				)

