
'''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 YolosImageProcessor







class  __A				( unittest.TestCase			):
 def __init__(   self			:    List[Any]				,   lowerCamelCase			:    Union[str, Any]				,   lowerCamelCase			:    Tuple=7				,   lowerCamelCase			:    List[Any]=3				,   lowerCamelCase			:    int=30				,   lowerCamelCase			:    Dict=4_00				,   lowerCamelCase			:    str=True				,   lowerCamelCase			:    Optional[int]=None				,   lowerCamelCase			:    int=True				,   lowerCamelCase			:    Optional[Any]=[0.5, 0.5, 0.5]				,   lowerCamelCase			:    Any=[0.5, 0.5, 0.5]				,   lowerCamelCase			:    List[Any]=True				,   lowerCamelCase			:    Union[str, Any]=1 / 2_55				,   lowerCamelCase			:    List[Any]=True				,   ):



   """simple docstring"""





   __A		:     Optional[Any]												=		size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
   __A		:     Dict												=		parent
   __A		:     Any												=		batch_size
   __A		:     str												=		num_channels
   __A		:     List[Any]												=		min_resolution
   __A		:     Tuple												=		max_resolution
   __A		:     Union[str, Any]												=		do_resize
   __A		:     List[str]												=		size
   __A		:     Union[str, Any]												=		do_normalize
   __A		:     List[Any]												=		image_mean
   __A		:     List[str]												=		image_std
   __A		:     Optional[int]												=		do_rescale
   __A		:     List[str]												=		rescale_factor
   __A		:     List[Any]												=		do_pad
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   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 		lowercase_(   self			:    Any				,   lowerCamelCase			:    Any				,   lowerCamelCase			:    Tuple=False       ):



   """simple docstring"""





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

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

   return expected_height, expected_width







@require_torch
@require_vision
class  __A				( __a    ,						unittest.TestCase			):
 lowerCamelCase				       =YolosImageProcessor if is_vision_available() else None
 def 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   __A		:     Optional[int]												=		YolosImageProcessingTester(self       )
 @property
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





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



   """simple docstring"""





   __A		:     List[str]												=		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 		lowercase_(   self			:    Dict       ):



   """simple docstring"""





   __A		:     str												=		self.image_processing_class.from_dict(self.image_processor_dict       )
   self.assertEqual(image_processor.size				,   {"""shortest_edge""": 18, """longest_edge""": 13_33}       )
   self.assertEqual(image_processor.do_pad				,   a_       )

   __A		:     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 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   __A		:     List[str]												=		self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __A		:     Optional[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
   __A		:     Optional[Any]												=		image_processing(image_inputs[0]				,   return_tensors="""pt"""       ).pixel_values

   __A		:     Dict												=		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
   __A		:     Dict												=		self.image_processor_tester.get_expected_values(a_				,   batched=a_       )

   __A		:     Any												=		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 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   __A		:     str												=		self.image_processing_class(**self.image_processor_dict       )
   # create random numpy tensors
   __A		:     List[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
   __A		:     Any												=		image_processing(image_inputs[0]				,   return_tensors="""pt"""       ).pixel_values

   __A		:     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
   __A		:     Tuple												=		image_processing(a_				,   return_tensors="""pt"""       ).pixel_values

   __A		:     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 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   __A		:     int												=		self.image_processing_class(**self.image_processor_dict       )
   # create random PyTorch tensors
   __A		:     List[Any]												=		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
   __A		:     Dict												=		image_processing(image_inputs[0]				,   return_tensors="""pt"""       ).pixel_values

   __A		:     str												=		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
   __A		:     List[str]												=		image_processing(a_				,   return_tensors="""pt"""       ).pixel_values

   __A		:     List[str]												=		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 		lowercase_(   self			:    List[Any]       ):



   """simple docstring"""





   __A		:     Tuple												=		self.image_processing_class(**self.image_processor_dict       )
   __A		:     Dict												=		self.image_processing_class(do_resize=a_				,   do_normalize=a_				,   do_rescale=a_       )
   # create random PyTorch tensors
   __A		:     Union[str, Any]												=		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
   __A		:     int												=		image_processing_a.pad(a_				,   return_tensors="""pt"""       )
   __A		:     int												=		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 		lowercase_(   self			:    str       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
   with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""				,   """r"""       ) as f:
     __A		:     int												=		json.loads(f.read()       )

   __A		:     int												=		{"""image_id""": 3_97_69, """annotations""": target}

   # encode them
   __A		:     Optional[Any]												=		YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""       )
   __A		:     int												=		image_processing(images=a_				,   annotations=a_				,   return_tensors="""pt"""       )

   # verify pixel values
   __A		:     Any												=		torch.Size([1, 3, 8_00, 10_66]       )
   self.assertEqual(encoding["""pixel_values"""].shape				,   a_       )

   __A		:     int												=		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
   __A		:     List[str]												=		torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]       )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				,   a_       )       )
   # verify boxes
   __A		:     Union[str, Any]												=		torch.Size([6, 4]       )
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,   a_       )
   __A		:     Union[str, 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
   __A		:     Dict												=		torch.tensor([3_97_69]       )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,   a_       )       )
   # verify is_crowd
   __A		:     Optional[int]												=		torch.tensor([0, 0, 0, 0, 0, 0]       )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,   a_       )       )
   # verify class_labels
   __A		:     Union[str, Any]												=		torch.tensor([75, 75, 63, 65, 17, 17]       )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,   a_       )       )
   # verify orig_size
   __A		:     Union[str, Any]												=		torch.tensor([4_80, 6_40]       )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,   a_       )       )
   # verify size
   __A		:     Tuple												=		torch.tensor([8_00, 10_66]       )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,   a_       )       )






 @slow
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





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

   __A		:     Optional[Any]												=		{"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

   __A		:     List[Any]												=		pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""       )

   # encode them
   __A		:     List[Any]												=		YolosImageProcessor(format="""coco_panoptic"""       )
   __A		:     str												=		image_processing(images=a_				,   annotations=a_				,   masks_path=a_				,   return_tensors="""pt"""       )

   # verify pixel values
   __A		:     Union[str, Any]												=		torch.Size([1, 3, 8_00, 10_66]       )
   self.assertEqual(encoding["""pixel_values"""].shape				,   a_       )

   __A		:     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
   __A		:     Any												=		torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]       )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]				,   a_       )       )
   # verify boxes
   __A		:     int												=		torch.Size([6, 4]       )
   self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape				,   a_       )
   __A		:     str												=		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
   __A		:     int												=		torch.tensor([3_97_69]       )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]				,   a_       )       )
   # verify is_crowd
   __A		:     Optional[int]												=		torch.tensor([0, 0, 0, 0, 0, 0]       )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]				,   a_       )       )
   # verify class_labels
   __A		:     Optional[int]												=		torch.tensor([17, 17, 63, 75, 75, 93]       )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]				,   a_       )       )
   # verify masks
   __A		:     Tuple												=		82_28_73
   self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()				,   a_       )
   # verify orig_size
   __A		:     Optional[int]												=		torch.tensor([4_80, 6_40]       )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]				,   a_       )       )
   # verify size
   __A		:     List[Any]												=		torch.tensor([8_00, 10_66]       )
   self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]				,   a_       )       )





'''simple docstring'''
from collections.abc import Sequence







def        A_     (						__SCREAMING_SNAKE_CASE						:  Sequence[float]			,     __SCREAMING_SNAKE_CASE						:  bool = False )				->       float:





  """simple docstring"""

  if not arr:
    return 0

  __A		:     Any												=		0 if allow_empty_subarrays else float("""-inf""" )
  __A		:     List[Any]												=		0.0
  for num in arr:
    __A		:     Tuple												=		max(0 if allow_empty_subarrays else num			,     curr_sum + num )
    __A		:     Optional[int]												=		max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  return max_sum


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

     testmod()

     A__   :		Any 		=[-2, 1, -3, 4, -1, 2, 1, -5, 4]
     print(F"{max_subarray_sum(nums) = }")



