





'''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 ConditionalDetrImageProcessor


class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase=7						,     _UpperCAmelCase=3						,     _UpperCAmelCase=30						,     _UpperCAmelCase=400						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     _UpperCAmelCase=True						,     _UpperCAmelCase=[0.5, 0.5, 0.5]						,     _UpperCAmelCase=[0.5, 0.5, 0.5]						,     _UpperCAmelCase=True						,     _UpperCAmelCase=1 / 255						,     _UpperCAmelCase=True						,     ):

          '''simple docstring'''






          __A						:			Optional[int]								=	size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
          __A						:			Optional[Any]								=	parent
          __A						:			Union[str, Any]								=	batch_size
          __A						:			Union[str, Any]								=	num_channels
          __A						:			Optional[int]								=	min_resolution
          __A						:			Tuple								=	max_resolution
          __A						:			List[Any]								=	do_resize
          __A						:			Dict								=	size
          __A						:			List[str]								=	do_normalize
          __A						:			List[Any]								=	image_mean
          __A						:			int								=	image_std
          __A						:			int								=	do_rescale
          __A						:			str								=	rescale_factor
          __A						:			Dict								=	do_pad


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase=False):

          '''simple docstring'''






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

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

          return expected_height, expected_width




@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE   (__lowerCamelCase ,		unittest.TestCase							):
     lowerCAmelCase		      =	ConditionalDetrImageProcessor if is_vision_available() else None


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	ConditionalDetrImageProcessingTester(self)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return self.image_processor_tester.prepare_image_processor_dict()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(__lowercase						,     'image_mean'))
          self.assertTrue(hasattr(__lowercase						,     'image_std'))
          self.assertTrue(hasattr(__lowercase						,     'do_normalize'))
          self.assertTrue(hasattr(__lowercase						,     'do_resize'))
          self.assertTrue(hasattr(__lowercase						,     'size'))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			str								=	self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size						,     {'shortest_edge': 18, 'longest_edge': 1333})
          self.assertEqual(image_processor.do_pad						,     __lowercase)

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          __A						:			Dict								=	prepare_image_inputs(self.image_processor_tester						,     equal_resolution=__lowercase)
          for image in image_inputs:
               self.assertIsInstance(__lowercase						,     Image.Image)

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

          __A						:			Any								=	self.image_processor_tester.get_expected_values(__lowercase)

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

          # Test batched
          __A						:			Optional[int]								=	self.image_processor_tester.get_expected_values(__lowercase						,     batched=__lowercase)

          __A						:			Optional[int]								=	image_processing(__lowercase						,     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):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          __A						:			Tuple								=	prepare_image_inputs(self.image_processor_tester						,     equal_resolution=__lowercase						,     numpify=__lowercase)
          for image in image_inputs:
               self.assertIsInstance(__lowercase						,     np.ndarray)

          # Test not batched input
          __A						:			Optional[int]								=	image_processing(image_inputs[0]						,     return_tensors='pt').pixel_values

          __A						:			Tuple								=	self.image_processor_tester.get_expected_values(__lowercase)

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

          # Test batched
          __A						:			Optional[Any]								=	image_processing(__lowercase						,     return_tensors='pt').pixel_values

          __A						:			Optional[Any]								=	self.image_processor_tester.get_expected_values(__lowercase						,     batched=__lowercase)

          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):

          '''simple docstring'''






          __A						:			List[str]								=	self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          __A						:			List[Any]								=	prepare_image_inputs(self.image_processor_tester						,     equal_resolution=__lowercase						,     torchify=__lowercase)
          for image in image_inputs:
               self.assertIsInstance(__lowercase						,     torch.Tensor)

          # Test not batched input
          __A						:			Optional[int]								=	image_processing(image_inputs[0]						,     return_tensors='pt').pixel_values

          __A						:			List[str]								=	self.image_processor_tester.get_expected_values(__lowercase)

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

          # Test batched
          __A						:			Optional[Any]								=	image_processing(__lowercase						,     return_tensors='pt').pixel_values

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

          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):

          '''simple docstring'''






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

          __A						:			Any								=	{'''image_id''': 3_9769, '''annotations''': target}

          # encode them
          __A						:			Optional[Any]								=	ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50')
          __A						:			Optional[Any]								=	image_processing(images=__lowercase						,     annotations=__lowercase						,     return_tensors='pt')

          # verify pixel values
          __A						:			str								=	torch.Size([1, 3, 800, 1066])
          self.assertEqual(encoding['pixel_values'].shape						,     __lowercase)

          __A						:			List[str]								=	torch.tensor([0.2796, 0.3138, 0.3481])
          self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,     __lowercase						,     atol=1e-4))

          # verify area
          __A						:			List[Any]								=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438])
          self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,     __lowercase))
          # verify boxes
          __A						:			List[Any]								=	torch.Size([6, 4])
          self.assertEqual(encoding['labels'][0]['boxes'].shape						,     __lowercase)
          __A						:			List[str]								=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
          self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,     __lowercase						,     atol=1e-3))
          # verify image_id
          __A						:			Optional[Any]								=	torch.tensor([3_9769])
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,     __lowercase))
          # verify is_crowd
          __A						:			Tuple								=	torch.tensor([0, 0, 0, 0, 0, 0])
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,     __lowercase))
          # verify class_labels
          __A						:			str								=	torch.tensor([75, 75, 63, 65, 17, 17])
          self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,     __lowercase))
          # verify orig_size
          __A						:			Any								=	torch.tensor([480, 640])
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,     __lowercase))
          # verify size
          __A						:			Optional[int]								=	torch.tensor([800, 1066])
          self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,     __lowercase))


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						:			List[Any]								=	{'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

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

          # encode them
          __A						:			Union[str, Any]								=	ConditionalDetrImageProcessor(format='coco_panoptic')
          __A						:			Union[str, Any]								=	image_processing(images=__lowercase						,     annotations=__lowercase						,     masks_path=__lowercase						,     return_tensors='pt')

          # verify pixel values
          __A						:			int								=	torch.Size([1, 3, 800, 1066])
          self.assertEqual(encoding['pixel_values'].shape						,     __lowercase)

          __A						:			Any								=	torch.tensor([0.2796, 0.3138, 0.3481])
          self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,     __lowercase						,     atol=1e-4))

          # verify area
          __A						:			Tuple								=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147])
          self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,     __lowercase))
          # verify boxes
          __A						:			Tuple								=	torch.Size([6, 4])
          self.assertEqual(encoding['labels'][0]['boxes'].shape						,     __lowercase)
          __A						:			int								=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
          self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,     __lowercase						,     atol=1e-3))
          # verify image_id
          __A						:			Tuple								=	torch.tensor([3_9769])
          self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,     __lowercase))
          # verify is_crowd
          __A						:			List[str]								=	torch.tensor([0, 0, 0, 0, 0, 0])
          self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,     __lowercase))
          # verify class_labels
          __A						:			List[Any]								=	torch.tensor([17, 17, 63, 75, 75, 93])
          self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,     __lowercase))
          # verify masks
          __A						:			int								=	82_2873
          self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,     __lowercase)
          # verify orig_size
          __A						:			Any								=	torch.tensor([480, 640])
          self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,     __lowercase))
          # verify size
          __A						:			List[Any]								=	torch.tensor([800, 1066])
          self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,     __lowercase))






'''simple docstring'''




def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			str      )					->					float:

     def get_matched_characters(__snake_case  :			str			,							__snake_case  :			str      ) -> str:
          __A						:			Optional[int]								=	[]
          __A						:			Optional[Any]								=	min(len(_stra      )			,							len(_stra      )      ) // 2
          for i, l in enumerate(_stra      ):
               __A						:			str								=	int(max(0			,							i - limit      )      )
               __A						:			str								=	int(min(i + limit + 1			,							len(_stra      )      )      )
               if l in _stra[left:right]:
                    matched.append(__snake_case      )
                    __A						:			Dict								=	f'{_stra[0:_stra.index(__snake_case      )]} {_stra[_stra.index(__snake_case      ) + 1:]}'

          return "".join(__snake_case      )

     # matching characters
     __A						:			Any								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	len(__snake_case      )

     # transposition
     __A						:			Tuple								=	(
         len([(ca, ca) for ca, ca in zip(__snake_case			,							__snake_case      ) if ca != ca]      ) // 2
     )

     if not match_count:
          __A						:			int								=	0.0
     else:
          __A						:			Union[str, Any]								=	(
              1
              / 3
              * (
                  match_count / len(__snake_case      )
                  + match_count / len(__snake_case      )
                  + (match_count - transpositions) / match_count
              )
          )

     # common prefix up to 4 characters
     __A						:			Tuple								=	0
     for ca, ca in zip(stra[:4]			,							stra[:4]      ):
          if ca == ca:
               prefix_len += 1
          else:
               break

     return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler('''hello''', '''world'''))