





'''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 UpperCAmelCase							(		unittest.TestCase							):



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



    def 					__lowerCamelCase						(  self					):
     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 					__lowerCamelCase						(  self     ,			__A     ,			__A=False					):
     if not batched:
      __UpperCAmelCase					=   image_inputs[0]
      if isinstance(__A     ,			Image.Image					):
       __UpperCAmelCase					,    __UpperCAmelCase					=   image.size
      else:
       __UpperCAmelCase					,    __UpperCAmelCase					=   image.shape[1], image.shape[2]
      if w < h:
       __UpperCAmelCase					=   int(self.size['shortest_edge'] * h / w					)
       __UpperCAmelCase					=   self.size['shortest_edge']
      elif w > h:
       __UpperCAmelCase					=   self.size['shortest_edge']
       __UpperCAmelCase					=   int(self.size['shortest_edge'] * w / h					)
      else:
       __UpperCAmelCase					=   self.size['shortest_edge']
       __UpperCAmelCase					=   self.size['shortest_edge']

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

     return expected_height, expected_width






@require_torch
@require_vision
class UpperCAmelCase							(		UpperCAmelCase_					,			unittest.TestCase							):
    _A :			int						   =       DetrImageProcessor if is_vision_available() else None



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   DetrImageProcessingTester(self					)



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



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   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 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   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					)

     __UpperCAmelCase					=   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 					__lowerCamelCase						(  self					):
     pass



    def 					__lowerCamelCase						(  self					):
     # 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=__A					)
     for image in image_inputs:
      self.assertIsInstance(__A     ,			Image.Image					)

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

     __UpperCAmelCase					,    __UpperCAmelCase					=   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
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.image_processor_tester.get_expected_values(__A     ,			batched=__A					)

     __UpperCAmelCase					=   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 					__lowerCamelCase						(  self					):
     # 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=__A     ,			numpify=__A					)
     for image in image_inputs:
      self.assertIsInstance(__A     ,			np.ndarray					)

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

     __UpperCAmelCase					,    __UpperCAmelCase					=   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
     __UpperCAmelCase					=   image_processing(__A     ,			return_tensors='pt'					).pixel_values

     __UpperCAmelCase					,    __UpperCAmelCase					=   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 					__lowerCamelCase						(  self					):
     # 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=__A     ,			torchify=__A					)
     for image in image_inputs:
      self.assertIsInstance(__A     ,			torch.Tensor					)

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

     __UpperCAmelCase					,    __UpperCAmelCase					=   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
     __UpperCAmelCase					=   image_processing(__A     ,			return_tensors='pt'					).pixel_values

     __UpperCAmelCase					,    __UpperCAmelCase					=   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 					__lowerCamelCase						(  self					):
     # prepare image and target
     __UpperCAmelCase					=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
     with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'     ,			'r'					) as f:
      __UpperCAmelCase					=   json.loads(f.read()					)

     __UpperCAmelCase					=   {'image_id': 39_769, 'annotations': target}

     # encode them
     __UpperCAmelCase					=   DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'					)
     __UpperCAmelCase					=   image_processing(images=__A     ,			annotations=__A     ,			return_tensors='pt'					)

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

     __UpperCAmelCase					=   torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]					)
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,			__A     ,			atol=1E-4					)					)

     # verify area
     __UpperCAmelCase					=   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
     __UpperCAmelCase					=   torch.Size([6, 4]					)
     self.assertEqual(encoding['labels'][0]['boxes'].shape     ,			__A					)
     __UpperCAmelCase					=   torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]					)
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,			__A     ,			atol=1E-3					)					)
     # verify image_id
     __UpperCAmelCase					=   torch.tensor([39_769]					)
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,			__A					)					)
     # verify is_crowd
     __UpperCAmelCase					=   torch.tensor([0, 0, 0, 0, 0, 0]					)
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,			__A					)					)
     # verify class_labels
     __UpperCAmelCase					=   torch.tensor([75, 75, 63, 65, 17, 17]					)
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,			__A					)					)
     # verify orig_size
     __UpperCAmelCase					=   torch.tensor([480, 640]					)
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,			__A					)					)
     # verify size
     __UpperCAmelCase					=   torch.tensor([800, 1_066]					)
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,			__A					)					)




    @slow
    def 					__lowerCamelCase						(  self					):
     # prepare image, target and masks_path
     __UpperCAmelCase					=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
     with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'     ,			'r'					) as f:
      __UpperCAmelCase					=   json.loads(f.read()					)

     __UpperCAmelCase					=   {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

     __UpperCAmelCase					=   pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'					)

     # encode them
     __UpperCAmelCase					=   DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'					)
     __UpperCAmelCase					=   image_processing(images=__A     ,			annotations=__A     ,			masks_path=__A     ,			return_tensors='pt'					)

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

     __UpperCAmelCase					=   torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]					)
     self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,			__A     ,			atol=1E-4					)					)

     # verify area
     __UpperCAmelCase					=   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
     __UpperCAmelCase					=   torch.Size([6, 4]					)
     self.assertEqual(encoding['labels'][0]['boxes'].shape     ,			__A					)
     __UpperCAmelCase					=   torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]					)
     self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,			__A     ,			atol=1E-3					)					)
     # verify image_id
     __UpperCAmelCase					=   torch.tensor([39_769]					)
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,			__A					)					)
     # verify is_crowd
     __UpperCAmelCase					=   torch.tensor([0, 0, 0, 0, 0, 0]					)
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,			__A					)					)
     # verify class_labels
     __UpperCAmelCase					=   torch.tensor([17, 17, 63, 75, 75, 93]					)
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,			__A					)					)
     # verify masks
     __UpperCAmelCase					=   822_873
     self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,			__A					)
     # verify orig_size
     __UpperCAmelCase					=   torch.tensor([480, 640]					)
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,			__A					)					)
     # verify size
     __UpperCAmelCase					=   torch.tensor([800, 1_066]					)
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,			__A					)					)












'''simple docstring'''




from __future__ import annotations







def _lowerCAmelCase     (      _lowerCAmelCase    )->     int:
 if not nums:
  return 0
 __UpperCAmelCase					=   nums[0]
 __UpperCAmelCase					=   0
 for num in nums[1:]:
  __UpperCAmelCase					,    __UpperCAmelCase					=   (
      max_excluding + num,
      max(_lowerCAmelCase       ,    _lowerCAmelCase    ),
  )
 return max(_lowerCAmelCase       ,    _lowerCAmelCase    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





