from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class 							a_					(		_a		):






 '''simple docstring'''






 UpperCAmelCase_      					=   """MarkupLMFeatureExtractor"""
 UpperCAmelCase_      					=   ("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
 UpperCAmelCase_      					=   True




 def __call__(     self   :    List[Any]     ,       lowercase__   :    List[str]=None     ,       lowercase__   :    Dict=None     ,       lowercase__   :    List[Any]=None     ,       lowercase__   :    Tuple=None     ,       lowercase__   :    Dict=None     ,       lowercase__   :    Optional[Any] = True     ,       lowercase__   :    Dict = False     ,       lowercase__   :    str = None     ,       lowercase__   :    List[Any] = None     ,       lowercase__   :    List[str] = 0     ,       lowercase__   :    Any = None     ,       lowercase__   :    Optional[Any] = None     ,       lowercase__   :    str = None     ,       lowercase__   :    str = False     ,       lowercase__   :    str = False     ,       lowercase__   :    Optional[int] = False     ,       lowercase__   :    str = False     ,       lowercase__   :    Union[str, Any] = True     ,       lowercase__   :    Optional[Any] = None     ,       **lowercase__   :    int     ,       ):







        '''simple docstring'''



        if self.parse_html:
               if html_strings is None:
                      raise ValueError('Make sure to pass HTML strings in case `parse_html` is set to `True`')

               if nodes is not None or xpaths is not None or node_labels is not None:
                      raise ValueError(
                          'Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`')

               lowerCAmelCase__	      =   self.feature_extractor(lowerCAmelCase__)
               lowerCAmelCase__	      =   features['nodes']
               lowerCAmelCase__	      =   features['xpaths']
        else:
               if html_strings is not None:
                      raise ValueError('You have passed HTML strings but `parse_html` is set to `False`.')
               if nodes is None or xpaths is None:
                      raise ValueError('Make sure to pass nodes and xpaths in case `parse_html` is set to `False`')

        # # second, apply the tokenizer
        if questions is not None and self.parse_html:
               if isinstance(lowerCAmelCase__     ,       lowerCAmelCase__):
                      lowerCAmelCase__	      =   [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

        lowerCAmelCase__	      =   self.tokenizer(
            text=questions if questions is not None else nodes     ,       text_pair=nodes if questions is not None else None     ,       xpaths=lowerCAmelCase__     ,       node_labels=lowerCAmelCase__     ,       add_special_tokens=lowerCAmelCase__     ,       padding=lowerCAmelCase__     ,       truncation=lowerCAmelCase__     ,       max_length=lowerCAmelCase__     ,       stride=lowerCAmelCase__     ,       pad_to_multiple_of=lowerCAmelCase__     ,       return_token_type_ids=lowerCAmelCase__     ,       return_attention_mask=lowerCAmelCase__     ,       return_overflowing_tokens=lowerCAmelCase__     ,       return_special_tokens_mask=lowerCAmelCase__     ,       return_offsets_mapping=lowerCAmelCase__     ,       return_length=lowerCAmelCase__     ,       verbose=lowerCAmelCase__     ,       return_tensors=lowerCAmelCase__     ,       **lowerCAmelCase__     ,       )

        return encoded_inputs




 def 						__snake_case		(     self   :    List[str]     ,       *lowercase__   :    Tuple     ,       **lowercase__   :    List[str]):







        '''simple docstring'''



        return self.tokenizer.batch_decode(*lowerCAmelCase__     ,       **lowerCAmelCase__)




 def 						__snake_case		(     self   :    Any     ,       *lowercase__   :    List[Any]     ,       **lowercase__   :    Optional[Any]):







        '''simple docstring'''



        return self.tokenizer.decode(*lowerCAmelCase__     ,       **lowerCAmelCase__)




 @property
 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        lowerCAmelCase__	      =   self.tokenizer.model_input_names
        return tokenizer_input_names

import numpy as np
from PIL import Image

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A,      _A,      _A=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       return (gray > 1_27) & (gray <= 2_55)

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ )		->    np.array:




       '''simple docstring'''






       _A=						np.zeros_like(lowerCAmelCase_ )
       _A=						np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )

       # Copy image to padded image
       _A=						image

       # Iterate over image & apply kernel
       for x in range(image.shape[1] ):
              for y in range(image.shape[0] ):
                     _A=						(
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     _A=						int(summation == 5 )
       return output


# kernel to be applied
UpperCAmelCase_    		=				np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      UpperCAmelCase_    		=				np.array(Image.open(R'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      UpperCAmelCase_    		=				erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      UpperCAmelCase_    		=				Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')