


"""simple docstring"""

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor





class    lowerCamelCase__						(		unittest.TestCase    ):
      def __init__(		self	:	str      ,       A_	:	Optional[Any]      ,       A_	:	Union[str, Any]=7      ,       A_	:	List[Any]=3      ,       A_	:	Dict=3_0      ,       A_	:	Optional[int]=4_0_0      ,       A_	:	List[Any]=True      ,       A_	:	Optional[int]=None      ,       A_	:	Any=True      ,       A_	:	List[str]=1 / 2_5_5      ,       A_	:	Tuple=True      ,       A_	:	List[str]=[0.5, 0.5, 0.5]      ,       A_	:	Dict=[0.5, 0.5, 0.5]      ,       A_	:	Any=True      ,       ):






       '''simple docstring'''


       __lowercase									=	size if size is not None else {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}
       __lowercase									=	parent
       __lowercase									=	batch_size
       __lowercase									=	num_channels
       __lowercase									=	min_resolution
       __lowercase									=	max_resolution
       __lowercase									=	do_resize
       __lowercase									=	size
       __lowercase									=	do_rescale
       __lowercase									=	rescale_factor
       __lowercase									=	do_normalize
       __lowercase									=	image_mean
       __lowercase									=	image_std
       __lowercase									=	do_pad
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_pad": self.do_pad,
       }






      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	int      ,       A_	:	Union[str, Any]=False							):






       '''simple docstring'''


       if not batched:
        __lowercase									=	image_inputs[0]
        if isinstance(A_      ,       Image.Image							):
         __lowercase							,		__lowercase									=	image.size
        else:
         __lowercase							,		__lowercase									=	image.shape[1], image.shape[2]
        if w < h:
         __lowercase									=	int(self.size["""shortest_edge"""] * h / w							)
         __lowercase									=	self.size["""shortest_edge"""]
        elif w > h:
         __lowercase									=	self.size["""shortest_edge"""]
         __lowercase									=	int(self.size["""shortest_edge"""] * w / h							)
        else:
         __lowercase									=	self.size["""shortest_edge"""]
         __lowercase									=	self.size["""shortest_edge"""]

       else:
        __lowercase									=	[]
        for image in image_inputs:
         __lowercase							,		__lowercase									=	self.get_expected_values([image]							)
         expected_values.append((expected_height, expected_width)							)
        __lowercase									=	max(A_      ,       key=lambda A_							: item[0]							)[0]
        __lowercase									=	max(A_      ,       key=lambda A_							: item[1]							)[1]

       return expected_height, expected_width






@require_torch
@require_vision
class    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     str											=		DetrImageProcessor if is_vision_available() else None
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	DetrImageProcessingTester(self							)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       return self.image_processor_tester.prepare_image_processor_dict()
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(A_      ,       """image_mean"""							)							)
       self.assertTrue(hasattr(A_      ,       """image_std"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_normalize"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_rescale"""							)							)
       self.assertTrue(hasattr(A_      ,       """rescale_factor"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_resize"""							)							)
       self.assertTrue(hasattr(A_      ,       """size"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_pad"""							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class.from_dict(self.image_processor_dict							)
       self.assertEqual(image_processor.size      ,       {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}							)
       self.assertEqual(image_processor.do_pad      ,       A_							)

       __lowercase									=	self.image_processing_class.from_dict(
           self.image_processor_dict      ,       size=4_2      ,       max_size=8_4      ,       pad_and_return_pixel_mask=A_							)
       self.assertEqual(image_processor.size      ,       {"""shortest_edge""": 4_2, """longest_edge""": 8_4}							)
       self.assertEqual(image_processor.do_pad      ,       A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       __lowercase									=	prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=A_							)
       for image in image_inputs:
        self.assertIsInstance(A_      ,       Image.Image							)

       # Test not batched input
       __lowercase									=	image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values

       __lowercase							,		__lowercase									=	self.image_processor_tester.get_expected_values(A_							)

       self.assertEqual(
           encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

       # Test batched
       __lowercase							,		__lowercase									=	self.image_processor_tester.get_expected_values(A_      ,       batched=A_							)

       __lowercase									=	image_processing(A_      ,       return_tensors="""pt"""							).pixel_values
       self.assertEqual(
           encoded_images.shape      ,       (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       __lowercase									=	prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=A_      ,       numpify=A_							)
       for image in image_inputs:
        self.assertIsInstance(A_      ,       np.ndarray							)

       # Test not batched input
       __lowercase									=	image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values

       __lowercase							,		__lowercase									=	self.image_processor_tester.get_expected_values(A_							)

       self.assertEqual(
           encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

       # Test batched
       __lowercase									=	image_processing(A_      ,       return_tensors="""pt"""							).pixel_values

       __lowercase							,		__lowercase									=	self.image_processor_tester.get_expected_values(A_      ,       batched=A_							)

       self.assertEqual(
           encoded_images.shape      ,       (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       __lowercase									=	prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=A_      ,       torchify=A_							)
       for image in image_inputs:
        self.assertIsInstance(A_      ,       torch.Tensor							)

       # Test not batched input
       __lowercase									=	image_processing(image_inputs[0]      ,       return_tensors="""pt"""							).pixel_values

       __lowercase							,		__lowercase									=	self.image_processor_tester.get_expected_values(A_							)

       self.assertEqual(
           encoded_images.shape      ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,       )

       # Test batched
       __lowercase									=	image_processing(A_      ,       return_tensors="""pt"""							).pixel_values

       __lowercase							,		__lowercase									=	self.image_processor_tester.get_expected_values(A_      ,       batched=A_							)

       self.assertEqual(
           encoded_images.shape      ,       (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )      ,       )
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
       with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""      ,       """r"""							) as f:
        __lowercase									=	json.loads(f.read()							)

       __lowercase									=	{"""image_id""": 3_9_7_6_9, """annotations""": target}

       # encode them
       __lowercase									=	DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""							)
       __lowercase									=	image_processing(images=A_      ,       annotations=A_      ,       return_tensors="""pt"""							)

       # verify pixel values
       __lowercase									=	torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
       self.assertEqual(encoding["""pixel_values"""].shape      ,       A_							)

       __lowercase									=	torch.tensor([0.27_96, 0.31_38, 0.34_81]							)
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,       A_      ,       atol=1e-4							)							)

       # verify area
       __lowercase									=	torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,       A_							)							)
       # verify boxes
       __lowercase									=	torch.Size([6, 4]							)
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       A_							)
       __lowercase									=	torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,       A_      ,       atol=1e-3							)							)
       # verify image_id
       __lowercase									=	torch.tensor([3_9_7_6_9]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       A_							)							)
       # verify is_crowd
       __lowercase									=	torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       A_							)							)
       # verify class_labels
       __lowercase									=	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       A_							)							)
       # verify orig_size
       __lowercase									=	torch.tensor([4_8_0, 6_4_0]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       A_							)							)
       # verify size
       __lowercase									=	torch.tensor([8_0_0, 1_0_6_6]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       A_							)							)






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
       with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""      ,       """r"""							) as f:
        __lowercase									=	json.loads(f.read()							)

       __lowercase									=	{"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}

       __lowercase									=	pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

       # encode them
       __lowercase									=	DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""							)
       __lowercase									=	image_processing(images=A_      ,       annotations=A_      ,       masks_path=A_      ,       return_tensors="""pt"""							)

       # verify pixel values
       __lowercase									=	torch.Size([1, 3, 8_0_0, 1_0_6_6]							)
       self.assertEqual(encoding["""pixel_values"""].shape      ,       A_							)

       __lowercase									=	torch.tensor([0.27_96, 0.31_38, 0.34_81]							)
       self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,       A_      ,       atol=1e-4							)							)

       # verify area
       __lowercase									=	torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,       A_							)							)
       # verify boxes
       __lowercase									=	torch.Size([6, 4]							)
       self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       A_							)
       __lowercase									=	torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,       A_      ,       atol=1e-3							)							)
       # verify image_id
       __lowercase									=	torch.tensor([3_9_7_6_9]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       A_							)							)
       # verify is_crowd
       __lowercase									=	torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       A_							)							)
       # verify class_labels
       __lowercase									=	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       A_							)							)
       # verify masks
       __lowercase									=	8_2_2_8_7_3
       self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,       A_							)
       # verify orig_size
       __lowercase									=	torch.tensor([4_8_0, 6_4_0]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       A_							)							)
       # verify size
       __lowercase									=	torch.tensor([8_0_0, 1_0_6_6]							)
       self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       A_							)							)




"""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_      (				):






 """simple docstring"""







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


UpperCAmelCase__  							=load("accuracy")


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any] ):






 """simple docstring"""







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





class    lowerCamelCase__						(		_a    ):
      def __init__(		self	:	Any      ,       A_	:	int							):






       '''simple docstring'''


       super().__init__()
       __lowercase									=	trainer






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	int      ,       A_	:	str      ,       A_	:	Any      ,       **A_	:	Optional[int]							):






       '''simple docstring'''


       if control.should_evaluate:
        __lowercase									=	deepcopy(A_							)
        self._trainer.evaluate(eval_dataset=self._trainer.train_dataset      ,       metric_key_prefix="""train"""							)
        return control_copy






def lowerCAmelCase_      (				):






 """simple docstring"""







 __lowercase									=	get_args()
 set_seed(args.seed )

 __lowercase									=	load_dataset("""codeparrot/codecomplex"""				,   split="""train""" )
 __lowercase									=	dataset.train_test_split(test_size=0.2 )
 __lowercase									=	train_test["""test"""].train_test_split(test_size=0.5 )
 __lowercase									=	DatasetDict(
     {
         """train""": train_test["""train"""],
         """test""": test_validation["""train"""],
         """valid""": test_validation["""test"""],
     } )

 print("""Loading tokenizer and model""" )
 __lowercase									=	AutoTokenizer.from_pretrained(args.model_ckpt )
 __lowercase									=	tokenizer.eos_token
 __lowercase									=	AutoModelForSequenceClassification.from_pretrained(args.model_ckpt				,   num_labels=7 )
 __lowercase									=	model.config.eos_token_id

 if args.freeze:
  for param in model.roberta.parameters():
   __lowercase									=	False

 __lowercase									=	ClassLabel(num_classes=7				,   names=list(set(train_test_validation["""train"""]["""complexity"""] ) ) )

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

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

 __lowercase									=	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"""				,   )

 __lowercase									=	Trainer(
     model=UpperCamelCase__				,   args=UpperCamelCase__				,   train_dataset=tokenized_datasets["""train"""]				,   eval_dataset=tokenized_datasets["""valid"""]				,   tokenizer=UpperCamelCase__				,   data_collator=UpperCamelCase__				,   compute_metrics=UpperCamelCase__				,   )

 print("""Training...""" )
 trainer.add_callback(CustomCallback(UpperCamelCase__ ) )
 trainer.train()


if __name__ == "__main__":
       main()
