


"""simple docstring"""

import unittest

import numpy as np
import requests

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

     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     UpperCAmelCase__  							=False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor





class    lowerCamelCase__						(		unittest.TestCase    ):
      def __init__(		self	:	List[Any]      ,       A_	:	Dict      ,       A_	:	List[Any]=7      ,       A_	:	int=3      ,       A_	:	Optional[Any]=1_8      ,       A_	:	List[str]=3_0      ,       A_	:	Union[str, Any]=4_0_0      ,       A_	:	Optional[int]=None      ,       A_	:	List[Any]=True      ,       A_	:	Dict=True      ,       A_	:	Tuple=None      ,       ):






       '''simple docstring'''


       __lowercase									=	size if size is not None else {"""height""": 2_0, """width""": 2_0}
       __lowercase									=	parent
       __lowercase									=	batch_size
       __lowercase									=	num_channels
       __lowercase									=	image_size
       __lowercase									=	min_resolution
       __lowercase									=	max_resolution
       __lowercase									=	size
       __lowercase									=	do_normalize
       __lowercase									=	do_convert_rgb
       __lowercase									=	[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
       __lowercase									=	patch_size if patch_size is not None else {"""height""": 1_6, """width""": 1_6}
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
       __lowercase									=	Image.open(requests.get(_A      ,       stream=_A							).raw							).convert("""RGB"""							)
       return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,   reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,   )
@require_torch
@require_vision
class    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     int											=		PixaStructImageProcessor if is_vision_available() else None
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	PixaStructImageProcessingTester(self							)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       return self.image_processor_tester.prepare_image_processor_dict()
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(_A      ,       """do_normalize"""							)							)
       self.assertTrue(hasattr(_A      ,       """do_convert_rgb"""							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.image_processor_tester.prepare_dummy_image()

       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       __lowercase									=	2_0_4_8

       __lowercase									=	image_processor(_A      ,       return_tensors="""pt"""      ,       max_patches=_A							)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()      ,       torch.tensor(0.06_06							)      ,       atol=1e-3      ,       rtol=1e-3							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


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

       # Test not batched input
       __lowercase									=	(
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        __lowercase									=	image_processor(
            image_inputs[0]      ,       return_tensors="""pt"""      ,       max_patches=_A							).flattened_patches
        self.assertEqual(
            encoded_images.shape      ,       (1, max_patch, expected_hidden_dim)      ,       )

        # Test batched
        __lowercase									=	image_processor(
            _A      ,       return_tensors="""pt"""      ,       max_patches=_A							).flattened_patches
        self.assertEqual(
            encoded_images.shape      ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


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

       # Test not batched input
       __lowercase									=	(
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       __lowercase									=	True

       for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        with self.assertRaises(_A							):
         __lowercase									=	image_processor(
             image_inputs[0]      ,       return_tensors="""pt"""      ,       max_patches=_A							).flattened_patches

        __lowercase									=	"""Hello"""

        __lowercase									=	image_processor(
            image_inputs[0]      ,       return_tensors="""pt"""      ,       max_patches=_A      ,       header_text=_A							).flattened_patches
        self.assertEqual(
            encoded_images.shape      ,       (1, max_patch, expected_hidden_dim)      ,       )

        # Test batched
        __lowercase									=	image_processor(
            _A      ,       return_tensors="""pt"""      ,       max_patches=_A      ,       header_text=_A							).flattened_patches
        self.assertEqual(
            encoded_images.shape      ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


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

       __lowercase									=	(
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        __lowercase									=	image_processor(
            image_inputs[0]      ,       return_tensors="""pt"""      ,       max_patches=_A							).flattened_patches
        self.assertEqual(
            encoded_images.shape      ,       (1, max_patch, expected_hidden_dim)      ,       )

        # Test batched
        __lowercase									=	image_processor(
            _A      ,       return_tensors="""pt"""      ,       max_patches=_A							).flattened_patches
        self.assertEqual(
            encoded_images.shape      ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,       )






      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       __lowercase									=	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
       __lowercase									=	(
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        __lowercase									=	image_processor(
            image_inputs[0]      ,       return_tensors="""pt"""      ,       max_patches=_A							).flattened_patches
        self.assertEqual(
            encoded_images.shape      ,       (1, max_patch, expected_hidden_dim)      ,       )

        # Test batched
        __lowercase									=	image_processor(
            _A      ,       return_tensors="""pt"""      ,       max_patches=_A							).flattened_patches
        self.assertEqual(
            encoded_images.shape      ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,       )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,   reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""			,   )
@require_torch
@require_vision
class    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     Optional[Any]											=		PixaStructImageProcessor if is_vision_available() else None
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	PixaStructImageProcessingTester(self      ,       num_channels=4							)
       __lowercase									=	3
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       return self.image_processor_tester.prepare_image_processor_dict()
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(_A      ,       """do_normalize"""							)							)
       self.assertTrue(hasattr(_A      ,       """do_convert_rgb"""							)							)






      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


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

       # Test not batched input
       __lowercase									=	(
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
        # Test not batched input
        __lowercase									=	image_processor(
            image_inputs[0]      ,       return_tensors="""pt"""      ,       max_patches=_A							).flattened_patches
        self.assertEqual(
            encoded_images.shape      ,       (1, max_patch, expected_hidden_dim)      ,       )

        # Test batched
        __lowercase									=	image_processor(
            _A      ,       return_tensors="""pt"""      ,       max_patches=_A							).flattened_patches
        self.assertEqual(
            encoded_images.shape      ,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,       )




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str ):






 """simple docstring"""







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

  return "".join(UpperCamelCase__ )

 # matching characters
 __lowercase									=	get_matched_characters(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	get_matched_characters(UpperCamelCase__				,   UpperCamelCase__ )
 __lowercase									=	len(UpperCamelCase__ )

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

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

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