
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     __magic_name__            =  argparse.ArgumentParser(
         description=(
             '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
             ''' Distillation'''
         )
     )
     parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
     parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
     parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
     parser.add_argument('''--vocab_transform''', action='''store_true''')
     __magic_name__            =  parser.parse_args()

     if args.model_type == "bert":
          __magic_name__            =  BertForMaskedLM.from_pretrained(args.model_name)
          __magic_name__            =  '''bert'''
     else:
          raise ValueError('''args.model_type should be "bert".''')

     __magic_name__            =  model.state_dict()
     __magic_name__            =  {}

     for w in ["word_embeddings", "position_embeddings"]:
          __magic_name__            =  state_dict[F'''{prefix}.embeddings.{w}.weight''']
     for w in ["weight", "bias"]:
          __magic_name__            =  state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

     __magic_name__            =  0
     for teacher_idx in [0, 2, 4, 7, 9, 11]:
          for w in ["weight", "bias"]:
               __magic_name__            =  state_dict[
                   F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
               ]
               __magic_name__            =  state_dict[
                   F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
               ]
               __magic_name__            =  state_dict[
                   F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
               ]

               __magic_name__            =  state_dict[
                   F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
               ]
               __magic_name__            =  state_dict[
                   F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
               ]

               __magic_name__            =  state_dict[
                   F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
               ]
               __magic_name__            =  state_dict[
                   F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
               ]
               __magic_name__            =  state_dict[
                   F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
               ]
          std_idx += 1

     __magic_name__            =  state_dict['''cls.predictions.decoder.weight''']
     __magic_name__            =  state_dict['''cls.predictions.bias''']
     if args.vocab_transform:
          for w in ["weight", "bias"]:
               __magic_name__            =  state_dict[F'''cls.predictions.transform.dense.{w}''']
               __magic_name__            =  state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

     print(F'''N layers selected for distillation: {std_idx}''')
     print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

     print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
     torch.save(compressed_sd, args.dump_checkpoint)





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):

    # prepare kernel
    # the kernel size have to be odd
    if (ksize % 2) == 0:
        snake_case__						=				ksize + 1
    snake_case__						=				np.zeros((ksize, ksize)    ,		dtype=np.floataa							)

    # each value
    for y in range(__lowerCAmelCase							):
        for x in range(__lowerCAmelCase							):
            # distance from center
            snake_case__						=				x - ksize // 2
            snake_case__						=				y - ksize // 2

            # degree to radiant
            snake_case__						=				theta / 180 * np.pi
            snake_case__						=				np.cos(_theta							)
            snake_case__						=				np.sin(_theta							)

            # get kernel x
            snake_case__						=				cos_theta * px + sin_theta * py

            # get kernel y
            snake_case__						=				-sin_theta * px + cos_theta * py

            # fill kernel
            snake_case__						=				np.exp(
                -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)							) * np.cos(2 * np.pi * _x / lambd + psi							)

    return gabor


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     # read original image
     __magic_name__            =  imread('''../image_data/lena.jpg''')
     # turn image in gray scale value
     __magic_name__            =  cvtColor(img, COLOR_BGR2GRAY)

     # Apply multiple Kernel to detect edges
     __magic_name__            =  np.zeros(gray.shape[:2])
     for theta in [0, 30, 60, 90, 120, 150]:
          __magic_name__            =  gabor_filter_kernel(10, 8, theta, 10, 0, 0)
          out += filteraD(gray, CV_8UC3, kernel_aa)
     __magic_name__            =  out / out.max() * 255
     __magic_name__            =  out.astype(np.uinta)

     imshow('''Original''', gray)
     imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

     waitKey(0)



