


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:
			lowerCAmelCase			=				False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor


class 			_a				(     unittest.TestCase     ):

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





									"""simple docstring"""







									lowercase__				= size if size is not None else {'''height''': 20, '''width''': 20}
									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__				= [512, 1_024, 2_048, 4_096]
									lowercase__				= patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}

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





									"""simple docstring"""







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


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





									"""simple docstring"""







									lowercase__				= '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
									lowercase__				= Image.open(requests.get(UpperCamelCase_       ,		stream=UpperCamelCase_				).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 			_a				(     UpperCamelCase__	,      unittest.TestCase     ):
							_lowercase      :     Optional[Any]     =					PixaStructImageProcessor if is_vision_available() else None

							def  lowerCamelCase_					(				self:      Any				)     ->  int:





									"""simple docstring"""







									lowercase__				= PixaStructImageProcessingTester(self				)

							@property
							def  lowerCamelCase_					(				self:      Dict				)     ->  Tuple:





									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()

							def  lowerCamelCase_					(				self:      Optional[int]				)     ->  Union[str, Any]:





									"""simple docstring"""







									lowercase__				= self.image_processing_class(**self.image_processor_dict				)
									self.assertTrue(hasattr(UpperCamelCase_       ,		'''do_normalize'''				)				)
									self.assertTrue(hasattr(UpperCamelCase_       ,		'''do_convert_rgb'''				)				)

							def  lowerCamelCase_					(				self:      Optional[Any]				)     ->  Dict:





									"""simple docstring"""







									lowercase__				= self.image_processor_tester.prepare_dummy_image()

									lowercase__				= self.image_processing_class(**self.image_processor_dict				)
									lowercase__				= 2_048

									lowercase__				= image_processor(UpperCamelCase_       ,		return_tensors='''pt'''       ,		max_patches=UpperCamelCase_				)
									self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,		torch.tensor(0.0606				)       ,		atol=1E-3       ,		rtol=1E-3				)				)

							def  lowerCamelCase_					(				self:      Optional[Any]				)     ->  Dict:





									"""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=UpperCamelCase_				)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_       ,		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=UpperCamelCase_				).flattened_patches
											self.assertEqual(
											    encoded_images.shape       ,		(1, max_patch, expected_hidden_dim)       ,		)

											# Test batched
											lowercase__				= image_processor(
											    UpperCamelCase_       ,		return_tensors='''pt'''       ,		max_patches=UpperCamelCase_				).flattened_patches
											self.assertEqual(
											    encoded_images.shape       ,		(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,		)

							def  lowerCamelCase_					(				self:      List[Any]				)     ->  Optional[int]:





									"""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=UpperCamelCase_				)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_       ,		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(UpperCamelCase_				):
													lowercase__				= image_processor(
													    image_inputs[0]       ,		return_tensors='''pt'''       ,		max_patches=UpperCamelCase_				).flattened_patches

											lowercase__				= '''Hello'''

											lowercase__				= image_processor(
											    image_inputs[0]       ,		return_tensors='''pt'''       ,		max_patches=UpperCamelCase_       ,		header_text=UpperCamelCase_				).flattened_patches
											self.assertEqual(
											    encoded_images.shape       ,		(1, max_patch, expected_hidden_dim)       ,		)

											# Test batched
											lowercase__				= image_processor(
											    UpperCamelCase_       ,		return_tensors='''pt'''       ,		max_patches=UpperCamelCase_       ,		header_text=UpperCamelCase_				).flattened_patches
											self.assertEqual(
											    encoded_images.shape       ,		(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,		)

							def  lowerCamelCase_					(				self:      List[Any]				)     ->  int:





									"""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=UpperCamelCase_       ,		numpify=UpperCamelCase_				)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_       ,		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=UpperCamelCase_				).flattened_patches
											self.assertEqual(
											    encoded_images.shape       ,		(1, max_patch, expected_hidden_dim)       ,		)

											# Test batched
											lowercase__				= image_processor(
											    UpperCamelCase_       ,		return_tensors='''pt'''       ,		max_patches=UpperCamelCase_				).flattened_patches
											self.assertEqual(
											    encoded_images.shape       ,		(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,		)


							def  lowerCamelCase_					(				self:      Optional[Any]				)     ->  str:





									"""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=UpperCamelCase_       ,		torchify=UpperCamelCase_				)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_       ,		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=UpperCamelCase_				).flattened_patches
											self.assertEqual(
											    encoded_images.shape       ,		(1, max_patch, expected_hidden_dim)       ,		)

											# Test batched
											lowercase__				= image_processor(
											    UpperCamelCase_       ,		return_tensors='''pt'''       ,		max_patches=UpperCamelCase_				).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 			_a				(     UpperCamelCase__	,      unittest.TestCase     ):
							_lowercase      :     Tuple     =					PixaStructImageProcessor if is_vision_available() else None

							def  lowerCamelCase_					(				self:      str				)     ->  Any:





									"""simple docstring"""







									lowercase__				= PixaStructImageProcessingTester(self       ,		num_channels=4				)
									lowercase__				= 3

							@property
							def  lowerCamelCase_					(				self:      List[Any]				)     ->  Tuple:





									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()

							def  lowerCamelCase_					(				self:      Any				)     ->  Optional[Any]:





									"""simple docstring"""







									lowercase__				= self.image_processing_class(**self.image_processor_dict				)
									self.assertTrue(hasattr(UpperCamelCase_       ,		'''do_normalize'''				)				)
									self.assertTrue(hasattr(UpperCamelCase_       ,		'''do_convert_rgb'''				)				)


							def  lowerCamelCase_					(				self:      int				)     ->  Union[str, 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=UpperCamelCase_				)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_       ,		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=UpperCamelCase_				).flattened_patches
											self.assertEqual(
											    encoded_images.shape       ,		(1, max_patch, expected_hidden_dim)       ,		)

											# Test batched
											lowercase__				= image_processor(
											    UpperCamelCase_       ,		return_tensors='''pt'''       ,		max_patches=UpperCamelCase_				).flattened_patches
											self.assertEqual(
											    encoded_images.shape       ,		(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,		)




def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):

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

            return "".join(UpperCamelCase__		)

      # matching characters
      UpperCamelCase__     : Any											=					get_matched_characters(UpperCamelCase__			,		UpperCamelCase__		)
      UpperCamelCase__     : Union[str, Any]											=					get_matched_characters(UpperCamelCase__			,		UpperCamelCase__		)
      UpperCamelCase__     : List[Any]											=					len(UpperCamelCase__		)

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

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

      # common prefix up to 4 characters
      UpperCamelCase__     : Dict											=					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"))


