




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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      Tuple      )     ->       np.ndarray:





				"""simple docstring"""


				if (ksize % 2) == 0:
								__lowerCamelCase		       =    ksize + 1
				__lowerCamelCase		       =    np.zeros((ksize, ksize) ,     dtype=np.floataa      )

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

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

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

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

												# fill kernel
												__lowerCamelCase		       =    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
					__A        =    imread("../image_data/lena.jpg")
					# turn image in gray scale value
					__A        =    cvtColor(img, COLOR_BGR2GRAY)

					# Apply multiple Kernel to detect edges
					__A        =    np.zeros(gray.shape[:2])
					for theta in [0, 30, 60, 90, 1_20, 1_50]:
										__A        =    gabor_filter_kernel(10, 8, theta, 10, 0, 0)
										out += filteraD(gray, CV_8UC3, kernel_aa)
					__A        =    out / out.max() * 2_55
					__A        =    out.astype(np.uinta)

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

					waitKey(0)






'''simple docstring'''

import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 lowerCamelCase							:   List[str]  						=	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')
 lowerCamelCase							:   str  						=	parser.parse_args()

 if args.model_type == "bert":
  lowerCamelCase							:   List[Any]  						=	BertForMaskedLM.from_pretrained(args.model_name)
  lowerCamelCase							:   Any  						=	'bert'
 else:
  raise ValueError('args.model_type should be "bert".')

 lowerCamelCase							:   int  						=	model.state_dict()
 lowerCamelCase							:   int  						=	{}

 for w in ["word_embeddings", "position_embeddings"]:
  lowerCamelCase							:   Any  						=	state_dict[f"""{prefix}.embeddings.{w}.weight"""]
 for w in ["weight", "bias"]:
  lowerCamelCase							:   Any  						=	state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

 lowerCamelCase							:   Tuple  						=	0
 for teacher_idx in [0, 2, 4, 7, 9, 11]:
  for w in ["weight", "bias"]:
   lowerCamelCase							:   Dict  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
   ]
   lowerCamelCase							:   Dict  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
   ]
   lowerCamelCase							:   List[Any]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
   ]

   lowerCamelCase							:   Tuple  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
   ]
   lowerCamelCase							:   Optional[int]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
   ]

   lowerCamelCase							:   Optional[Any]  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
   ]
   lowerCamelCase							:   Dict  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
   ]
   lowerCamelCase							:   Any  						=	state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
   ]
  std_idx += 1

 lowerCamelCase							:   Optional[int]  						=	state_dict['cls.predictions.decoder.weight']
 lowerCamelCase							:   str  						=	state_dict['cls.predictions.bias']
 if args.vocab_transform:
  for w in ["weight", "bias"]:
   lowerCamelCase							:   str  						=	state_dict[f"""cls.predictions.transform.dense.{w}"""]
   lowerCamelCase							:   Any  						=	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)




