
"""simple docstring"""



import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_pytesseract_available():
    from PIL import Image

    from transformers import LayoutLMvaImageProcessor





class 				a			(					unittest.TestCase      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=7     ,	UpperCamelCase_=3     ,	UpperCamelCase_=18     ,	UpperCamelCase_=30     ,	UpperCamelCase_=400     ,	UpperCamelCase_=True     ,	UpperCamelCase_=None     ,	UpperCamelCase_=True     ,	):
          UpperCAmelCase__	:      Tuple     							=					size if size is not None else {'height': 18, 'width': 18}
          UpperCAmelCase__	:      Any     							=					parent
          UpperCAmelCase__	:      Any     							=					batch_size
          UpperCAmelCase__	:      List[str]     							=					num_channels
          UpperCAmelCase__	:      str     							=					image_size
          UpperCAmelCase__	:      Optional[int]     							=					min_resolution
          UpperCAmelCase__	:      Union[str, Any]     							=					max_resolution
          UpperCAmelCase__	:      List[Any]     							=					do_resize
          UpperCAmelCase__	:      Optional[int]     							=					size
          UpperCAmelCase__	:      Dict     							=					apply_ocr



      def  __snake_case							(   self      ):
          return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Union[str, Any]           =     LayoutLMvaImageProcessor if is_pytesseract_available() else None



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					LayoutLMvaImageProcessingTester(self      )



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



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(UpperCamelCase_     ,	'do_resize'      )      )
          self.assertTrue(hasattr(UpperCamelCase_     ,	'size'      )      )
          self.assertTrue(hasattr(UpperCamelCase_     ,	'apply_ocr'      )      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					self.image_processing_class.from_dict(self.image_processor_dict      )
          self.assertEqual(image_processor.size     ,	{'height': 18, 'width': 18}      )

          UpperCAmelCase__	:      Tuple     							=					self.image_processing_class.from_dict(self.image_processor_dict     ,	size=42      )
          self.assertEqual(image_processor.size     ,	{'height': 42, 'width': 42}      )



      def  __snake_case							(   self      ):
          pass



      def  __snake_case							(   self      ):
          # Initialize image_processing
          UpperCAmelCase__	:      Optional[Any]     							=					self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          UpperCAmelCase__	:      int     							=					prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCamelCase_      )
          for image in image_inputs:
              self.assertIsInstance(UpperCamelCase_     ,	Image.Image      )

          # Test not batched input
          UpperCAmelCase__	:      str     							=					image_processing(image_inputs[0]     ,	return_tensors='pt'      )
          self.assertEqual(
              encoding.pixel_values.shape     ,	(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['height'],
                  self.image_processor_tester.size['width'],
              )     ,	)

          self.assertIsInstance(encoding.words     ,	UpperCamelCase_      )
          self.assertIsInstance(encoding.boxes     ,	UpperCamelCase_      )

          # Test batched
          UpperCAmelCase__	:      Optional[int]     							=					image_processing(UpperCamelCase_     ,	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.size['height'],
                  self.image_processor_tester.size['width'],
              )     ,	)



      def  __snake_case							(   self      ):
          # Initialize image_processing
          UpperCAmelCase__	:      Optional[Any]     							=					self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          UpperCAmelCase__	:      Tuple     							=					prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCamelCase_     ,	numpify=UpperCamelCase_      )
          for image in image_inputs:
              self.assertIsInstance(UpperCamelCase_     ,	np.ndarray      )

          # Test not batched input
          UpperCAmelCase__	:      Any     							=					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.size['height'],
                  self.image_processor_tester.size['width'],
              )     ,	)

          # Test batched
          UpperCAmelCase__	:      Tuple     							=					image_processing(UpperCamelCase_     ,	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.size['height'],
                  self.image_processor_tester.size['width'],
              )     ,	)



      def  __snake_case							(   self      ):
          # Initialize image_processing
          UpperCAmelCase__	:      str     							=					self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          UpperCAmelCase__	:      int     							=					prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=UpperCamelCase_     ,	torchify=UpperCamelCase_      )
          for image in image_inputs:
              self.assertIsInstance(UpperCamelCase_     ,	torch.Tensor      )

          # Test not batched input
          UpperCAmelCase__	:      Any     							=					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.size['height'],
                  self.image_processor_tester.size['width'],
              )     ,	)

          # Test batched
          UpperCAmelCase__	:      List[str]     							=					image_processing(UpperCamelCase_     ,	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.size['height'],
                  self.image_processor_tester.size['width'],
              )     ,	)



      def  __snake_case							(   self      ):
          # with apply_OCR = True
          UpperCAmelCase__	:      List[Any]     							=					LayoutLMvaImageProcessor()

          from datasets import load_dataset

          UpperCAmelCase__	:      List[Any]     							=					load_dataset('hf-internal-testing/fixtures_docvqa'     ,	split='test'      )

          UpperCAmelCase__	:      Optional[Any]     							=					Image.open(ds[0]['file']      ).convert('RGB'      )

          UpperCAmelCase__	:      Union[str, Any]     							=					image_processing(UpperCamelCase_     ,	return_tensors='pt'      )

          self.assertEqual(encoding.pixel_values.shape     ,	(1, 3, 224, 224)      )
          self.assertEqual(len(encoding.words      )     ,	len(encoding.boxes      )      )

          # fmt: off
          # the words and boxes were obtained with Tesseract 4.1.1
          UpperCAmelCase__	:      Union[str, Any]     							=					[['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']]  # noqa: E231
          UpperCAmelCase__	:      Optional[int]     							=					[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
          # fmt: on

          self.assertListEqual(encoding.words     ,	UpperCamelCase_      )
          self.assertListEqual(encoding.boxes     ,	UpperCamelCase_      )

          # with apply_OCR = False
          UpperCAmelCase__	:      Tuple     							=					LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase_      )

          UpperCAmelCase__	:      str     							=					image_processing(UpperCamelCase_     ,	return_tensors='pt'      )

          self.assertEqual(encoding.pixel_values.shape     ,	(1, 3, 224, 224)      )



"""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   (     ):
    UpperCAmelCase__	:      List[Any]     							=					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()


UpperCamelCase__          =	load('accuracy')
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					eval_pred
    UpperCAmelCase__	:      Optional[int]     							=					np.argmax(_snake_case    ,axis=1 )
    return metric.compute(predictions=_snake_case    ,references=_snake_case )





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_      ):
          super().__init__()
          UpperCAmelCase__	:      List[str]     							=					trainer



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ):
          if control.should_evaluate:
              UpperCAmelCase__	:      int     							=					deepcopy(UpperCamelCase_      )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,	metric_key_prefix='train'      )
              return control_copy




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      int     							=					get_args()
    set_seed(args.seed )

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

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

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

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

    def tokenize(_snake_case ):
        UpperCAmelCase__	:      Dict     							=					tokenizer(example['src']    ,truncation=_snake_case    ,max_length=1024 )
        UpperCAmelCase__	:      Any     							=					labels.straint(example['complexity'] )
        return {
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"],
            "label": label,
        }

    UpperCAmelCase__	:      List[Any]     							=					train_test_validation.map(
        _snake_case    ,batched=_snake_case    ,remove_columns=train_test_validation['train'].column_names    ,)
    UpperCAmelCase__	:      Tuple     							=					DataCollatorWithPadding(tokenizer=_snake_case )

    UpperCAmelCase__	:      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'    ,)

    UpperCAmelCase__	:      Dict     							=					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()

