





'''simple docstring'''






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				BertConfig.from_json_file(lowerCAmelCase    )
      print(f"Building PyTorch model from configuration: {config}"    )
      _lowerCAmelCase					=				BertForPreTraining(lowerCAmelCase    )

      # Load weights from tf checkpoint
      load_tf_weights_in_bert(lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}"    )
      torch.save(model.state_dict()				,      lowerCAmelCase    )


if __name__ == "__main__":
      A__							:  Union[str, Any]    =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--bert_config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained BERT model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      A__							:  Dict    =parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_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

A__							:  List[str]    =imread(r'''digital_image_processing/image_data/lena_small.jpg''')
A__							:  Union[str, Any]    =cvtColor(img, COLOR_BGR2GRAY)
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				cn.convert_to_negative(lowerCAmelCase    )
      # assert negative_img array for at least one True
      assert negative_img.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      with Image.open("""digital_image_processing/image_data/lena_small.jpg"""    ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowerCAmelCase				,      1_10    )    ).startswith(
                """<PIL.Image.Image image mode=RGB size=100x100 at"""    )
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				canny.gen_gaussian_kernel(9				,      sigma=1.4    )
      # Assert ambiguous array
      assert resp.all()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				imread("""digital_image_processing/image_data/lena_small.jpg"""				,      0    )
      # assert ambiguous array for all == True
      assert canny_img.all()
      _lowerCAmelCase					=				canny.canny(lowerCAmelCase    )
      # assert canny array for at least one True
      assert canny_array.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      assert gg.gaussian_filter(lowerCAmelCase				,      5				,      sigma=0.9    ).all()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]    )
      _lowerCAmelCase					=				conv.img_convolve(lowerCAmelCase				,      lowerCAmelCase    ).astype(lowerCAmelCase    )
      assert res.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      assert med.median_filter(lowerCAmelCase				,      3    ).any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				sob.sobel_filter(lowerCAmelCase    )
      assert grad.any() and theta.any()
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				sp.make_sepia(lowerCAmelCase				,      20    )
      assert sepia.all()
def   UpperCamelCase__ (						lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"    ):





      """simple docstring"""


      _lowerCAmelCase					=				bs.Burkes(imread(lowerCAmelCase				,      1    )				,      1_20    )
      burkes.process()
      assert burkes.output_img.any()
def   UpperCamelCase__ (						lowerCAmelCase = "digital_image_processing/image_data/lena_small.jpg"				,      ):





      """simple docstring"""


      _lowerCAmelCase					=				rs.NearestNeighbour(imread(lowerCAmelCase				,      1    )				,      4_00				,      2_00    )
      nn.process()
      assert nn.output.any()





def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				"""digital_image_processing/image_data/lena.jpg"""

      # Reading the image and converting it to grayscale.
      _lowerCAmelCase					=				imread(lowerCAmelCase				,      0    )

      # Test for get_neighbors_pixel function() return not None
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				image[x_coordinate][y_coordinate]

      _lowerCAmelCase					=				lbp.get_neighbors_pixel(
          lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      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
      _lowerCAmelCase					=				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]    ):
                  _lowerCAmelCase					=				lbp.local_binary_value(lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )

      assert lbp_image.any()






