




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 MobileViTImageProcessor


class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    def __init__(    self   :			Dict      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[Any]=7      ,				UpperCAmelCase__   :			Any=3      ,				UpperCAmelCase__   :			Tuple=18      ,				UpperCAmelCase__   :			str=30      ,				UpperCAmelCase__   :			List[str]=400      ,				UpperCAmelCase__   :			str=True      ,				UpperCAmelCase__   :			int=None      ,				UpperCAmelCase__   :			int=True      ,				UpperCAmelCase__   :			Tuple=None      ,				UpperCAmelCase__   :			Dict=True      ,				)		->str:





           '''simple docstring'''

           A__   				=					size if size is not None else {'''shortest_edge''': 20}
           A__   				=					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
           A__   				=					parent
           A__   				=					batch_size
           A__   				=					num_channels
           A__   				=					image_size
           A__   				=					min_resolution
           A__   				=					max_resolution
           A__   				=					do_resize
           A__   				=					size
           A__   				=					do_center_crop
           A__   				=					crop_size
           A__   				=					do_flip_channel_order





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->int:





           '''simple docstring'''

           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "do_flip_channel_order": self.do_flip_channel_order,
           }


@require_torch
@require_vision
class        UpperCamelCase_					(				UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   MobileViTImageProcessor if is_vision_available() else None


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Tuple:





           '''simple docstring'''

           A__   				=					MobileViTImageProcessingTester(self)


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[Any]:





           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Tuple:





           '''simple docstring'''

           A__   				=					self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(UpperCAmelCase__      ,				'''do_resize'''))
           self.assertTrue(hasattr(UpperCAmelCase__      ,				'''size'''))
           self.assertTrue(hasattr(UpperCAmelCase__      ,				'''do_center_crop'''))
           self.assertTrue(hasattr(UpperCAmelCase__      ,				'''center_crop'''))
           self.assertTrue(hasattr(UpperCAmelCase__      ,				'''do_flip_channel_order'''))


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Any:





           '''simple docstring'''

           A__   				=					self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size      ,				{'''shortest_edge''': 20})
           self.assertEqual(image_processor.crop_size      ,				{'''height''': 18, '''width''': 18})

           A__   				=					self.image_processing_class.from_dict(self.image_processor_dict      ,				size=42      ,				crop_size=84)
           self.assertEqual(image_processor.size      ,				{'''shortest_edge''': 42})
           self.assertEqual(image_processor.crop_size      ,				{'''height''': 84, '''width''': 84})


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Any:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Any:





           '''simple docstring'''

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

           # Test not batched input
           A__   				=					image_processing(image_inputs[0]      ,				return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape      ,				(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )      ,				)

           # Test batched
           A__   				=					image_processing(UpperCAmelCase__      ,				return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape      ,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )      ,				)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Any:





           '''simple docstring'''

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

           # Test not batched input
           A__   				=					image_processing(image_inputs[0]      ,				return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape      ,				(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )      ,				)

           # Test batched
           A__   				=					image_processing(UpperCAmelCase__      ,				return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape      ,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )      ,				)





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Dict:





           '''simple docstring'''

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

           # Test not batched input
           A__   				=					image_processing(image_inputs[0]      ,				return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape      ,				(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )      ,				)

           # Test batched
           A__   				=					image_processing(UpperCAmelCase__      ,				return_tensors='''pt''').pixel_values
           self.assertEqual(
               encoded_images.shape      ,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )      ,				)








def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> float:



       """simple docstring"""

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

              return "".join(lowercase_		)

       # matching characters
       A__   				=					get_matched_characters(lowercase_				,       lowercase_		)
       A__   				=					get_matched_characters(lowercase_				,       lowercase_		)
       A__   				=					len(lowercase_		)

       # transposition
       A__   				=					(
           len([(ca, ca) for ca, ca in zip(lowercase_				,       lowercase_		) if ca != ca]		) // 2
       )

       if not match_count:
              A__   				=					0.0
       else:
              A__   				=					(
                  1
                  / 3
                  * (
                      match_count / len(lowercase_		)
                      + match_count / len(lowercase_		)
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       A__   				=					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"""))


