code: 
  import cva
  import numpy as np
  def 			__UpperCAmelCase		(		a_			:		str    ,  a_			:		str    ,  a_			:		List[Any]    ,  a_			:		Optional[int]				)     ->       List[Any]:
  
  
  
  
  
  
     try:
        return int(image[x_coordinate][y_coordinate] >= center				)
     except (IndexError, TypeError):
        return 0
  def 			__UpperCAmelCase		(		a_			:		Optional[int]    ,  a_			:		Union[str, Any]    ,  a_			:		Dict				)     ->       Optional[Any]:
     _UpperCAmelCase   	=					image[x_coordinate][y_coordinate]
     _UpperCAmelCase   	=					[1, 2, 4, 8, 16, 32, 64, 1_28]
  
     # skip get_neighbors_pixel if center is null
     if center is None:
        return 0
  
     # Starting from the top right, assigning value to pixels clockwise
     _UpperCAmelCase   	=					[
         get_neighbors_pixel(UpperCamelCase_    ,  x_coordinate - 1    ,  y_coordinate + 1    ,  UpperCamelCase_				),
         get_neighbors_pixel(UpperCamelCase_    ,  UpperCamelCase_    ,  y_coordinate + 1    ,  UpperCamelCase_				),
         get_neighbors_pixel(UpperCamelCase_    ,  x_coordinate - 1    ,  UpperCamelCase_    ,  UpperCamelCase_				),
         get_neighbors_pixel(UpperCamelCase_    ,  x_coordinate + 1    ,  y_coordinate + 1    ,  UpperCamelCase_				),
         get_neighbors_pixel(UpperCamelCase_    ,  x_coordinate + 1    ,  UpperCamelCase_    ,  UpperCamelCase_				),
         get_neighbors_pixel(UpperCamelCase_    ,  x_coordinate + 1    ,  y_coordinate - 1    ,  UpperCamelCase_				),
         get_neighbors_pixel(UpperCamelCase_    ,  UpperCamelCase_    ,  y_coordinate - 1    ,  UpperCamelCase_				),
         get_neighbors_pixel(UpperCamelCase_    ,  x_coordinate - 1    ,  y_coordinate - 1    ,  UpperCamelCase_				),
     ]
  
     # Converting the binary value to decimal.
     return sum(
         binary_value * power for binary_value, power in zip(UpperCamelCase_    ,  UpperCamelCase_				)				)
  
  
  if __name__ == "__main__":
    # Reading the image and converting it to grayscale.
    SCREAMING_SNAKE_CASE_     				=   cva.imread(
        'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
    )
  
    # Create a numpy array as the same height and width of read image
    SCREAMING_SNAKE_CASE_     				=   np.zeros((image.shape[0], image.shape[1]))
  
    # Iterating through the image and calculating the
    # local binary pattern value for each pixel.
    for i in range(0, image.shape[0]):
      for j in range(0, image.shape[1]):
        SCREAMING_SNAKE_CASE_     				=   local_binary_value(image, i, j)
  
    cva.imshow('local binary pattern', lbp_image)
    cva.waitKey(0)
    cva.destroyAllWindows()
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import MraConfig, MraForMaskedLM
  def 			__UpperCAmelCase		(		a_			:		Any				)     ->       List[str]:
     if "model" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("model."    ,  ""				)
     if "norm1" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm1"    ,  "attention.output.LayerNorm"				)
     if "norm2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm2"    ,  "output.LayerNorm"				)
     if "norm" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("norm"    ,  "LayerNorm"				)
     if "transformer" in orig_key:
        _UpperCAmelCase   	=					orig_key.split("."				)[0].split("_"				)[-1]
        _UpperCAmelCase   	=					orig_key.replace(F'''transformer_{layer_num}'''    ,  F'''encoder.layer.{layer_num}'''				)
     if "mha.attn" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mha.attn"    ,  "attention.self"				)
     if "mha" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mha"    ,  "attention"				)
     if "W_q" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_q"    ,  "self.query"				)
     if "W_k" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_k"    ,  "self.key"				)
     if "W_v" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("W_v"    ,  "self.value"				)
     if "ff.0" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff.0"    ,  "intermediate.dense"				)
     if "ff.2" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff.2"    ,  "output.dense"				)
     if "ff" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("ff"    ,  "output.dense"				)
     if "mlm_class" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mlm.mlm_class"    ,  "cls.predictions.decoder"				)
     if "mlm" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("mlm"    ,  "cls.predictions.transform"				)
     if "backbone.backbone.encoders" in orig_key:
        _UpperCAmelCase   	=					orig_key.replace("backbone.backbone.encoders"    ,  "encoder.layer"				)
     if "cls" not in orig_key:
        _UpperCAmelCase   	=					"mra." + orig_key
  
     return orig_key
  def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		Any				)     ->       List[str]:
     for key in orig_state_dict.copy().keys():
        _UpperCAmelCase   	=					orig_state_dict.pop(a_				)
  
        if ("pooler" in key) or ("sen_class" in key):
           continue
        else:
           _UpperCAmelCase   	=					val
  
     _UpperCAmelCase   	=					orig_state_dict["cls.predictions.decoder.bias"]
     _UpperCAmelCase   	=					torch.arange(a_				).expand((1, -1)				) + 2
  
     return orig_state_dict
  def 			__UpperCAmelCase		(		a_			:		int    ,  a_			:		Tuple    ,  a_			:		List[str]				)     ->       Any:
     _UpperCAmelCase   	=					torch.load(a_    ,  map_location="cpu"				)["model_state_dict"]
     _UpperCAmelCase   	=					MraConfig.from_json_file(a_				)
     _UpperCAmelCase   	=					MraForMaskedLM(a_				)
  
     _UpperCAmelCase   	=					convert_checkpoint_helper(config.max_position_embeddings    ,  a_				)
  
     print(model.load_state_dict(a_				)				)
     model.eval()
     model.save_pretrained(a_				)
  
     print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)
  
  
  if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_     				=   argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for Mra model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
    convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  