
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 						__magic_name__			(   ):



     '''simple docstring'''




     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()


lowerCamelCase_						     =					load('''accuracy''')






def 						__magic_name__			(   __a      :	Dict						):



     '''simple docstring'''




     UpperCamelCase__		,	UpperCamelCase__				=    eval_pred
     UpperCamelCase__				=    np.argmax(__a	,		axis=1						)
     return metric.compute(predictions=__a	,		references=__a						)




class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_						):
       super().__init__()
       UpperCamelCase__				=    trainer







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






def 						__magic_name__			(   ):



     '''simple docstring'''




     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      :	Union[str, Any]						):
          UpperCamelCase__				=    tokenizer(example["""src"""]	,		truncation=__a	,		max_length=1_024						)
          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()







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import GLPNImageProcessor




class      __A(    unittest.TestCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=18	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=4_00	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=True	,      ):
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    min_resolution
       UpperCamelCase__				=    max_resolution
       UpperCamelCase__				=    do_resize
       UpperCamelCase__				=    size_divisor
       UpperCamelCase__				=    do_rescale







  def 	UpperCAmelCase_ (self						):
       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }






@require_torch
@require_vision
class      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      GLPNImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    GLPNImageProcessingTester(self						)







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_resize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """size_divisor"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """resample"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_rescale"""						)						)







  def 	UpperCAmelCase_ (self						):
       pass







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      numpify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      np.ndarray						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      torchify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      torch.Tensor						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)





