"""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 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					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=0.01						)
  parser.add_argument("""--output_dir"""      ,type=lowercase      ,default="""./results"""						)
  return parser.parse_args()


UpperCAmelCase__     						=  load("""accuracy""")






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase       , _UpperCAmelCase         =					eval_pred
  _UpperCAmelCase         =					np.argmax(lowercase      ,axis=1						)
  return metric.compute(predictions=lowercase      ,references=lowercase						)


class 				a (						lowerCAmelCase_      ):


    def __init__(    self		:     List[str]      , __lowerCAmelCase		:     Tuple    ):
      super().__init__()
      _UpperCAmelCase         =					trainer


    def    lowerCAmelCase_     (    self		:     Union[str, Any]      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     Tuple      , **__lowerCAmelCase		:     Dict    ):
      if control.should_evaluate:
        _UpperCAmelCase         =					deepcopy(__lowerCAmelCase    )
        self._trainer.evaluate(eval_dataset=self._trainer.train_dataset      , metric_key_prefix="""train"""    )
        return control_copy






def 			__UpperCAmelCase    (	):







  """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(lowercase						):
    _UpperCAmelCase         =					tokenizer(example["""src"""]      ,truncation=lowercase      ,max_length=10_24						)
    _UpperCAmelCase         =					labels.straint(example["""complexity"""]						)
    return {
        "input_ids": inputs["input_ids"],
        "attention_mask": inputs["attention_mask"],
        "label": label,
    }

  _UpperCAmelCase         =					train_test_validation.map(
      lowercase      ,batched=lowercase      ,remove_columns=train_test_validation["""train"""].column_names      ,)
  _UpperCAmelCase         =					DataCollatorWithPadding(tokenizer=lowercase						)

  _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=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()

"""simple docstring"""


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 MobileViTImageProcessor


class 				a (						unittest.TestCase      ):


    def __init__(    self		:     Dict      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Optional[Any]=7      , __lowerCAmelCase		:     Optional[Any]=3      , __lowerCAmelCase		:     Optional[Any]=18      , __lowerCAmelCase		:     str=30      , __lowerCAmelCase		:     List[str]=400      , __lowerCAmelCase		:     Union[str, Any]=True      , __lowerCAmelCase		:     str=None      , __lowerCAmelCase		:     Optional[int]=True      , __lowerCAmelCase		:     int=None      , __lowerCAmelCase		:     List[str]=True      , ):
      _UpperCAmelCase         =					size if size is not None else {"""shortest_edge""": 20}
      _UpperCAmelCase         =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
      _UpperCAmelCase         =					parent
      _UpperCAmelCase         =					batch_size
      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					min_resolution
      _UpperCAmelCase         =					max_resolution
      _UpperCAmelCase         =					do_resize
      _UpperCAmelCase         =					size
      _UpperCAmelCase         =					do_center_crop
      _UpperCAmelCase         =					crop_size
      _UpperCAmelCase         =					do_flip_channel_order


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_flip_channel_order": self.do_flip_channel_order,
      }



@require_torch
@require_vision
class 				a (						lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Optional[int]   							=							MobileViTImageProcessor if is_vision_available() else None


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					MobileViTImageProcessingTester(self    )


    @property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return self.image_processor_tester.prepare_image_processor_dict()


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_resize"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """size"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_flip_channel_order"""    )    )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 20}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 18, """width""": 18}    )

      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 42}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 84, """width""": 84}    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      pass


    def    lowerCAmelCase_     (    self		:     Dict    ):
      # 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=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , Image.Image    )

      # Test not batched input
      _UpperCAmelCase         =					image_processing(image_inputs[0]      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )


    def    lowerCAmelCase_     (    self		:     str    ):
      # 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=__lowerCAmelCase      , numpify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , np.ndarray    )

      # Test not batched input
      _UpperCAmelCase         =					image_processing(image_inputs[0]      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      # 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=__lowerCAmelCase      , torchify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , torch.Tensor    )

      # Test not batched input
      _UpperCAmelCase         =					image_processing(image_inputs[0]      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , return_tensors="""pt"""    ).pixel_values
      self.assertEqual(
          encoded_images.shape      , (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )
