




'''simple docstring'''







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def        lowerCamelCase__				(      __lowercase				):
     if "model" in orig_key:
          snake_case			:      str        =			orig_key.replace("""model."""     ,   """"""				)
     if "norm1" in orig_key:
          snake_case			:      Any        =			orig_key.replace("""norm1"""     ,   """attention.output.LayerNorm"""				)
     if "norm2" in orig_key:
          snake_case			:      Optional[Any]        =			orig_key.replace("""norm2"""     ,   """output.LayerNorm"""				)
     if "norm" in orig_key:
          snake_case			:      List[str]        =			orig_key.replace("""norm"""     ,   """LayerNorm"""				)
     if "transformer" in orig_key:
          snake_case			:      List[str]        =			orig_key.split("""."""				)[0].split("""_"""				)[-1]
          snake_case			:      Tuple        =			orig_key.replace(F'''transformer_{layer_num}'''     ,   F'''encoder.layer.{layer_num}'''				)
     if "mha.attn" in orig_key:
          snake_case			:      Optional[int]        =			orig_key.replace("""mha.attn"""     ,   """attention.self"""				)
     if "mha" in orig_key:
          snake_case			:      List[str]        =			orig_key.replace("""mha"""     ,   """attention"""				)
     if "W_q" in orig_key:
          snake_case			:      List[str]        =			orig_key.replace("""W_q"""     ,   """self.query"""				)
     if "W_k" in orig_key:
          snake_case			:      int        =			orig_key.replace("""W_k"""     ,   """self.key"""				)
     if "W_v" in orig_key:
          snake_case			:      Optional[Any]        =			orig_key.replace("""W_v"""     ,   """self.value"""				)
     if "ff1" in orig_key:
          snake_case			:      int        =			orig_key.replace("""ff1"""     ,   """intermediate.dense"""				)
     if "ff2" in orig_key:
          snake_case			:      Optional[Any]        =			orig_key.replace("""ff2"""     ,   """output.dense"""				)
     if "ff" in orig_key:
          snake_case			:      List[str]        =			orig_key.replace("""ff"""     ,   """output.dense"""				)
     if "mlm_class" in orig_key:
          snake_case			:      Tuple        =			orig_key.replace("""mlm.mlm_class"""     ,   """cls.predictions.decoder"""				)
     if "mlm" in orig_key:
          snake_case			:      str        =			orig_key.replace("""mlm"""     ,   """cls.predictions.transform"""				)
     if "cls" not in orig_key:
          snake_case			:      Any        =			"""yoso.""" + orig_key

     return orig_key



def        lowerCamelCase__				(      __lowercase     ,   __lowercase				):
     for key in orig_state_dict.copy().keys():
          snake_case			:      Tuple        =			orig_state_dict.pop(__lowercase				)

          if ("pooler" in key) or ("sen_class" in key):
               continue
          else:
               snake_case			:      Union[str, Any]        =			val

     snake_case			:      Union[str, Any]        =			orig_state_dict["""cls.predictions.decoder.bias"""]
     snake_case			:      Optional[Any]        =			torch.arange(__lowercase				).expand((1, -1)				) + 2

     return orig_state_dict



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      Any        =			torch.load(__lowercase     ,   map_location="""cpu"""				)["""model_state_dict"""]
     snake_case			:      Tuple        =			YosoConfig.from_json_file(__lowercase				)
     snake_case			:      Union[str, Any]        =			YosoForMaskedLM(__lowercase				)

     snake_case			:      List[Any]        =			convert_checkpoint_helper(config.max_position_embeddings     ,   __lowercase				)

     print(model.load_state_dict(__lowercase				)				)
     model.eval()
     model.save_pretrained(__lowercase				)

     print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''				)


if __name__ == "__main__":
      lowercase      :   int      =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to YOSO pytorch checkpoint."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help="""The json file for YOSO model config.""",
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      lowercase      :   List[str]      =       parser.parse_args()
      convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase      :   Any      =       imread(R"""digital_image_processing/image_data/lena_small.jpg""")
lowercase      :   List[str]      =       cvtColor(img, COLOR_BGR2GRAY)



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[int]        =			cn.convert_to_negative(__lowercase				)
     # assert negative_img array for at least one True
     assert negative_img.any()



def        lowerCamelCase__				(      ):
     with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
          # Work around assertion for response
          assert str(cc.change_contrast(__lowercase     ,   110				)				).startswith(
              """<PIL.Image.Image image mode=RGB size=100x100 at"""				)



def        lowerCamelCase__				(      ):
     snake_case			:      Union[str, Any]        =			canny.gen_gaussian_kernel(9     ,   sigma=1.4				)
     # Assert ambiguous array
     assert resp.all()



def        lowerCamelCase__				(      ):
     snake_case			:      Optional[Any]        =			imread("""digital_image_processing/image_data/lena_small.jpg"""     ,   0				)
     # assert ambiguous array for all == True
     assert canny_img.all()
     snake_case			:      Any        =			canny.canny(__lowercase				)
     # assert canny array for at least one True
     assert canny_array.any()



def        lowerCamelCase__				(      ):
     assert gg.gaussian_filter(__lowercase     ,   5     ,   sigma=0.9				).all()



def        lowerCamelCase__				(      ):
     # laplace diagonals
     snake_case			:      int        =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
     snake_case			:      Tuple        =			conv.img_convolve(__lowercase     ,   __lowercase				).astype(__lowercase				)
     assert res.any()



def        lowerCamelCase__				(      ):
     assert med.median_filter(__lowercase     ,   3				).any()



def        lowerCamelCase__				(      ):
     snake_case ,		snake_case			:      Dict        =			sob.sobel_filter(__lowercase				)
     assert grad.any() and theta.any()



def        lowerCamelCase__				(      ):
     snake_case			:      int        =			sp.make_sepia(__lowercase     ,   20				)
     assert sepia.all()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"				):
     snake_case			:      int        =			bs.Burkes(imread(__lowercase     ,   1				)     ,   120				)
     burkes.process()
     assert burkes.output_img.any()



def        lowerCamelCase__				(      __lowercase = "digital_image_processing/image_data/lena_small.jpg"     ,   ):
     snake_case			:      Dict        =			rs.NearestNeighbour(imread(__lowercase     ,   1				)     ,   400     ,   200				)
     nn.process()
     assert nn.output.any()







def        lowerCamelCase__				(      ):
     snake_case			:      int        =			"""digital_image_processing/image_data/lena.jpg"""

     # Reading the image and converting it to grayscale.
     snake_case			:      Tuple        =			imread(__lowercase     ,   0				)

     # Test for get_neighbors_pixel function() return not None
     snake_case			:      Optional[int]        =			0
     snake_case			:      Tuple        =			0
     snake_case			:      Any        =			image[x_coordinate][y_coordinate]

     snake_case			:      Any        =			lbp.get_neighbors_pixel(
         __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				)

     assert neighbors_pixels is not None

     # Test for local_binary_pattern function()
     # Create a numpy array as the same height and width of read image
     snake_case			:      List[Any]        =			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]				):
               snake_case			:      Optional[Any]        =			lbp.local_binary_value(__lowercase     ,   __lowercase     ,   __lowercase				)

     assert lbp_image.any()



