

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     if "model" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('model.',   ''      )
     if "norm1" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('norm1',   'attention.output.LayerNorm'      )
     if "norm2" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('norm2',   'output.LayerNorm'      )
     if "norm" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('norm',   'LayerNorm'      )
     if "transformer" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.split('.'      )[0].split('_'      )[-1]
          SCREAMING_SNAKE_CASE										=orig_key.replace(F'transformer_{layer_num}',   F'encoder.layer.{layer_num}'      )
     if "mha.attn" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('mha.attn',   'attention.self'      )
     if "mha" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('mha',   'attention'      )
     if "W_q" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('W_q',   'self.query'      )
     if "W_k" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('W_k',   'self.key'      )
     if "W_v" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('W_v',   'self.value'      )
     if "ff.0" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('ff.0',   'intermediate.dense'      )
     if "ff.2" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('ff.2',   'output.dense'      )
     if "ff" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('ff',   'output.dense'      )
     if "mlm_class" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('mlm.mlm_class',   'cls.predictions.decoder'      )
     if "mlm" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('mlm',   'cls.predictions.transform'      )
     if "backbone.backbone.encoders" in orig_key:
          SCREAMING_SNAKE_CASE										=orig_key.replace('backbone.backbone.encoders',   'encoder.layer'      )
     if "cls" not in orig_key:
          SCREAMING_SNAKE_CASE										='mra.' + orig_key

     return orig_key







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     for key in orig_state_dict.copy().keys():
          SCREAMING_SNAKE_CASE										=orig_state_dict.pop(lowerCAmelCase_      )

          if ("pooler" in key) or ("sen_class" in key):
               continue
          else:
               SCREAMING_SNAKE_CASE										=val

     SCREAMING_SNAKE_CASE										=orig_state_dict['cls.predictions.decoder.bias']
     SCREAMING_SNAKE_CASE										=torch.arange(lowerCAmelCase_      ).expand((1, -1)      ) + 2

     return orig_state_dict







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=torch.load(lowerCAmelCase_,   map_location='cpu'      )['model_state_dict']
     SCREAMING_SNAKE_CASE										=MraConfig.from_json_file(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=MraForMaskedLM(lowerCAmelCase_      )

     SCREAMING_SNAKE_CASE										=convert_checkpoint_helper(config.max_position_embeddings,   lowerCAmelCase_      )

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

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


if __name__ == "__main__":
 _lowerCamelCase								=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."
 )
 _lowerCamelCase								=parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




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

from digital_image_processing.filters.convolve import img_convolve







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]      )
     SCREAMING_SNAKE_CASE										=np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]      )

     SCREAMING_SNAKE_CASE										=np.abs(img_convolve(lowerCAmelCase_,   lowerCAmelCase_      )      )
     SCREAMING_SNAKE_CASE										=np.abs(img_convolve(lowerCAmelCase_,   lowerCAmelCase_      )      )
     # modify the pix within [0, 255]
     SCREAMING_SNAKE_CASE										=dst_x * 255 / np.max(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=dst_y * 255 / np.max(lowerCAmelCase_      )

     SCREAMING_SNAKE_CASE										=np.sqrt((np.square(lowerCAmelCase_      )) + (np.square(lowerCAmelCase_      ))      )
     SCREAMING_SNAKE_CASE										=dst_xy * 255 / np.max(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=dst_xy.astype(np.uinta      )

     SCREAMING_SNAKE_CASE										=np.arctana(lowerCAmelCase_,   lowerCAmelCase_      )
     return dst, theta


if __name__ == "__main__":
 # read original image
 _lowerCamelCase								=imread("../image_data/lena.jpg")
 # turn image in gray scale value
 _lowerCamelCase								=cvtColor(img, COLOR_BGR2GRAY)

 _lowerCamelCase	,			_lowerCamelCase								=sobel_filter(gray)

 # show result images
 imshow("sobel filter", sobel_grad)
 imshow("sobel theta", sobel_theta)
 waitKey(0)

