code: 
  
  
  
  
  
  
  from typing import Optional, Union
  
  from ...file_utils import TensorType
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
       __lowerCamelCase										=     'MarkupLMFeatureExtractor'
       __lowerCamelCase										=     ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
       __lowerCamelCase										=     True
  
  
  
  
       def __call__(							self  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase = True  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = 0  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = False  ,	_lowerCAmelCase = True  ,	_lowerCAmelCase = None  ,	**_lowerCAmelCase  ,	):
            # first, create nodes and xpaths
            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`"""		)
  
                 UpperCAmelCase__			:     Dict										= self.feature_extractor(_lowerCAmelCase		)
                 UpperCAmelCase__			:     Tuple										= features["""nodes"""]
                 UpperCAmelCase__			:     List[Any]										= 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		):
                      UpperCAmelCase__			:     Tuple										= [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)
  
            UpperCAmelCase__			:     Union[str, Any]										= 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      __UpperCAmelCase     (							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            return self.tokenizer.batch_decode(*_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            return self.tokenizer.decode(*_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
  
  
  
       @property
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Union[str, Any]										= self.tokenizer.model_input_names
            return tokenizer_input_names
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import math
  import os
  
  import cva
  import numpy as np
  
  SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							255.0
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> float:
  
       '''simple docstring'''
       UpperCAmelCase__			:     List[Any]										= np.mean((original - contrast) ** 2					)
       if mse == 0:
            return 100
  
       return 20 * math.logaa(PIXEL_MAX / math.sqrt(__lowerCamelCase					)					)
  
  
  
  
  
  def    _lowerCamelCase	(	)   -> None:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Union[str, Any]										= os.path.dirname(os.path.realpath(__lowerCamelCase					)					)
       # Loading images (original image and compressed image)
       UpperCAmelCase__			:     List[Any]										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/original_image.png"""					)					)
       UpperCAmelCase__			:     Dict										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/compressed_image.png"""					)   ,       1					)
  
       UpperCAmelCase__			:     Tuple										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-base.png"""					)					)
       UpperCAmelCase__			:     int										= cva.imread(
           os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-comp-10.jpg"""					)   ,       1					)
  
       # Value expected: 29.73dB
       print("""-- First Test --"""					)
       print(F"PSNR value is {peak_signal_to_noise_ratio(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)
  
       # # Value expected: 31.53dB (Wikipedia Example)
       print("""\n-- Second Test --"""					)
       print(F"PSNR value is {peak_signal_to_noise_ratio(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
  
  
  