
from __future__ import annotations
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list[int]			,							UpperCAmelCase_	:     int				)			->						int:
       if len(UpperCAmelCase_				) < k or k < 0:
              raise ValueError('''Invalid Input'''				)
       SCREAMING_SNAKE_CASE_							:				str		=sum(array[:k]				)
       for i in range(len(UpperCAmelCase_				) - k				):
              SCREAMING_SNAKE_CASE_							:				List[str]		=current_sum - array[i] + array[i + k]
              SCREAMING_SNAKE_CASE_							:				List[Any]		=max(UpperCAmelCase_			,							UpperCAmelCase_				)
       return max_sum


if __name__ == "__main__":
 from doctest import testmod
 from random import randint

 testmod()
 _lowercase											=    [randint(-1000, 1000) for i in range(100)]
 _lowercase											=    randint(0, 110)
 print(F"The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}")


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 YolosImageProcessor


class 			lowercase_		(      unittest.TestCase       ):


       def __init__(    self						,       __A						,       __A=7						,       __A=3						,       __A=30						,       __A=400						,       __A=True						,       __A=None						,       __A=True						,       __A=[0.5, 0.5, 0.5]						,       __A=[0.5, 0.5, 0.5]						,       __A=True						,       __A=1 / 255						,       __A=True						,       )						->    Tuple:
              # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
              SCREAMING_SNAKE_CASE_							:				Any		=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
              SCREAMING_SNAKE_CASE_							:				Dict		=parent
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=batch_size
              SCREAMING_SNAKE_CASE_							:				List[Any]		=num_channels
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=min_resolution
              SCREAMING_SNAKE_CASE_							:				str		=max_resolution
              SCREAMING_SNAKE_CASE_							:				int		=do_resize
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=size
              SCREAMING_SNAKE_CASE_							:				str		=do_normalize
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=image_mean
              SCREAMING_SNAKE_CASE_							:				Any		=image_std
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=do_rescale
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=rescale_factor
              SCREAMING_SNAKE_CASE_							:				str		=do_pad


       def 	_snake_case				(    self				)						->    int:
              return {
                  "do_resize": self.do_resize,
                  "size": self.size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
                  "do_rescale": self.do_rescale,
                  "rescale_factor": self.rescale_factor,
                  "do_pad": self.do_pad,
              }





       def 	_snake_case				(    self						,       __A						,       __A=False				)						->    Any:
              if not batched:
                     SCREAMING_SNAKE_CASE_							:				str		=image_inputs[0]
                     if isinstance(__A						,       Image.Image				):
                            SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=image.size
                     else:
                            SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				str		=image.shape[1], image.shape[2]
                     if w < h:
                            SCREAMING_SNAKE_CASE_							:				List[Any]		=int(self.size['''shortest_edge'''] * h / w				)
                            SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.size['''shortest_edge''']
                     elif w > h:
                            SCREAMING_SNAKE_CASE_							:				List[Any]		=self.size['''shortest_edge''']
                            SCREAMING_SNAKE_CASE_							:				Dict		=int(self.size['''shortest_edge'''] * w / h				)
                     else:
                            SCREAMING_SNAKE_CASE_							:				str		=self.size['''shortest_edge''']
                            SCREAMING_SNAKE_CASE_							:				str		=self.size['''shortest_edge''']

              else:
                     SCREAMING_SNAKE_CASE_							:				Any		=[]
                     for image in image_inputs:
                            SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				str		=self.get_expected_values([image]				)
                            expected_values.append((expected_height, expected_width)				)
                     SCREAMING_SNAKE_CASE_							:				str		=max(__A						,       key=lambda __A				: item[0]				)[0]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=max(__A						,       key=lambda __A				: item[1]				)[1]

              return expected_height, expected_width






@require_torch
@require_vision
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						YolosImageProcessor if is_vision_available() else None


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				str		=YolosImageProcessingTester(self				)


       @property
       def 	_snake_case				(    self				)						->    Dict:
              return self.image_processor_tester.prepare_image_processor_dict()


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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_resize'''				)				)
              self.assertTrue(hasattr(__A						,       '''size'''				)				)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class.from_dict(self.image_processor_dict				)
              self.assertEqual(image_processor.size						,       {'''shortest_edge''': 18, '''longest_edge''': 1_333}				)
              self.assertEqual(image_processor.do_pad						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.image_processing_class.from_dict(
                  self.image_processor_dict						,       size=42						,       max_size=84						,       pad_and_return_pixel_mask=__A				)
              self.assertEqual(image_processor.size						,       {'''shortest_edge''': 42, '''longest_edge''': 84}				)
              self.assertEqual(image_processor.do_pad						,       __A				)


       def 	_snake_case				(    self				)						->    List[str]:
              pass


       def 	_snake_case				(    self				)						->    int:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class(**self.image_processor_dict				)
              # create random PIL images
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=prepare_image_inputs(self.image_processor_tester						,       equal_resolution=__A				)
              for image in image_inputs:
                     self.assertIsInstance(__A						,       Image.Image				)

              # Test not batched input
              SCREAMING_SNAKE_CASE_							:				List[str]		=image_processing(image_inputs[0]						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=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
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[str]		=self.image_processor_tester.get_expected_values(__A						,       batched=__A				)

              SCREAMING_SNAKE_CASE_							:				str		=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 	_snake_case				(    self				)						->    Optional[Any]:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.image_processing_class(**self.image_processor_dict				)
              # create random numpy tensors
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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
              SCREAMING_SNAKE_CASE_							:				Dict		=image_processing(image_inputs[0]						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=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
              SCREAMING_SNAKE_CASE_							:				Dict		=image_processing(__A						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				List[Any]		=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 	_snake_case				(    self				)						->    Dict:
              # Initialize image_processing
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class(**self.image_processor_dict				)
              # create random PyTorch tensors
              SCREAMING_SNAKE_CASE_							:				Dict		=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
              SCREAMING_SNAKE_CASE_							:				Any		=image_processing(image_inputs[0]						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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
              SCREAMING_SNAKE_CASE_							:				str		=image_processing(__A						,       return_tensors='''pt'''				).pixel_values

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=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 	_snake_case				(    self				)						->    List[Any]:
              # Initialize image_processings
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.image_processing_class(**self.image_processor_dict				)
              SCREAMING_SNAKE_CASE_							:				str		=self.image_processing_class(do_resize=__A						,       do_normalize=__A						,       do_rescale=__A				)
              # create random PyTorch tensors
              SCREAMING_SNAKE_CASE_							:				Dict		=prepare_image_inputs(self.image_processor_tester						,       equal_resolution=__A						,       torchify=__A				)
              for image in image_inputs:
                     self.assertIsInstance(__A						,       torch.Tensor				)

              # Test whether the method "pad" and calling the image processor return the same tensors
              SCREAMING_SNAKE_CASE_							:				List[Any]		=image_processing_a.pad(__A						,       return_tensors='''pt'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=image_processing_a(__A						,       return_tensors='''pt'''				)

              self.assertTrue(
                  torch.allclose(encoded_images_with_method['''pixel_values''']						,       encoded_images['''pixel_values''']						,       atol=1e-4				)				)


       @slow
       def 	_snake_case				(    self				)						->    Any:
              # prepare image and target
              SCREAMING_SNAKE_CASE_							:				Dict		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
              with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''						,       '''r'''				) as f:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=json.loads(f.read()				)

              SCREAMING_SNAKE_CASE_							:				Dict		={'''image_id''': 39_769, '''annotations''': target}

              # encode them
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''				)
              SCREAMING_SNAKE_CASE_							:				Dict		=image_processing(images=__A						,       annotations=__A						,       return_tensors='''pt'''				)

              # verify pixel values
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.Size([1, 3, 800, 1_066]				)
              self.assertEqual(encoding['''pixel_values'''].shape						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
              self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,       __A						,       atol=1e-4				)				)

              # verify area
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,       __A				)				)
              # verify boxes
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.Size([6, 4]				)
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,       __A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,       __A						,       atol=1e-3				)				)
              # verify image_id
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.tensor([39_769]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,       __A				)				)
              # verify is_crowd
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.tensor([0, 0, 0, 0, 0, 0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,       __A				)				)
              # verify class_labels
              SCREAMING_SNAKE_CASE_							:				str		=torch.tensor([75, 75, 63, 65, 17, 17]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,       __A				)				)
              # verify orig_size
              SCREAMING_SNAKE_CASE_							:				str		=torch.tensor([480, 640]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,       __A				)				)
              # verify size
              SCREAMING_SNAKE_CASE_							:				Dict		=torch.tensor([800, 1_066]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,       __A				)				)





       @slow
       def 	_snake_case				(    self				)						->    Tuple:
              # prepare image, target and masks_path
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
              with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''						,       '''r'''				) as f:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=json.loads(f.read()				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		={'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

              SCREAMING_SNAKE_CASE_							:				Tuple		=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''				)

              # encode them
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=YolosImageProcessor(format='''coco_panoptic'''				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=image_processing(images=__A						,       annotations=__A						,       masks_path=__A						,       return_tensors='''pt'''				)

              # verify pixel values
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.Size([1, 3, 800, 1_066]				)
              self.assertEqual(encoding['''pixel_values'''].shape						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
              self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]						,       __A						,       atol=1e-4				)				)

              # verify area
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']						,       __A				)				)
              # verify boxes
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.Size([6, 4]				)
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape						,       __A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]						,       __A						,       atol=1e-3				)				)
              # verify image_id
              SCREAMING_SNAKE_CASE_							:				Any		=torch.tensor([39_769]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']						,       __A				)				)
              # verify is_crowd
              SCREAMING_SNAKE_CASE_							:				Tuple		=torch.tensor([0, 0, 0, 0, 0, 0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']						,       __A				)				)
              # verify class_labels
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=torch.tensor([17, 17, 63, 75, 75, 93]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']						,       __A				)				)
              # verify masks
              SCREAMING_SNAKE_CASE_							:				List[Any]		=822_873
              self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()						,       __A				)
              # verify orig_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.tensor([480, 640]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']						,       __A				)				)
              # verify size
              SCREAMING_SNAKE_CASE_							:				Any		=torch.tensor([800, 1_066]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']						,       __A				)				)
