
import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


_UpperCamelCase:					int     =collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)



def 	_a  (					__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




  print(f'''Reading saved model from {model_file}'''    )
  _lowerCAmelCase						=		torch.load(__SCREAMING_SNAKE_CASE		,				map_location=lambda __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    : default_restore_location(__SCREAMING_SNAKE_CASE		,				'cpu'    )    )
  return CheckpointState(**__SCREAMING_SNAKE_CASE    )





class 						__lowercase:


       """simple docstring"""







       def __init__(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			Path       )					->   str:
         _lowerCAmelCase						=		src_file



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Optional[int]:
         raise NotImplementedError






       @staticmethod
       def SCREAMING_SNAKE_CASE_				(     _lowerCAmelCase					:			str  ,      *_lowerCAmelCase					:			Union[str, Any]  ,      **_lowerCAmelCase					:			str       )					->   "DPRState":
         if comp_type.startswith('c'       ):
           return DPRContextEncoderState(*_lowerCAmelCase  ,      **_lowerCAmelCase       )
         if comp_type.startswith('q'       ):
           return DPRQuestionEncoderState(*_lowerCAmelCase  ,      **_lowerCAmelCase       )
         if comp_type.startswith('r'       ):
           return DPRReaderState(*_lowerCAmelCase  ,      **_lowerCAmelCase       )
         else:
           raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'       )





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Tuple:
         _lowerCAmelCase						=		DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'       )[0]       )       )
         print(F'''Loading DPR biencoder from {self.src_file}'''       )
         _lowerCAmelCase						=		load_states_from_checkpoint(self.src_file       )
         _lowerCAmelCase	,       _lowerCAmelCase						=		model.ctx_encoder, 'ctx_model.'
         # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
         _lowerCAmelCase						=		{'bert_model.embeddings.position_ids': model.ctx_encoder.bert_model.embeddings.position_ids}
         for key, value in saved_state.model_dict.items():
           if key.startswith(_lowerCAmelCase       ):
             _lowerCAmelCase						=		key[len(_lowerCAmelCase       ) :]
             if not key.startswith('encode_proj.'       ):
               _lowerCAmelCase						=		'bert_model.' + key
             _lowerCAmelCase						=		value
         encoder.load_state_dict(_lowerCAmelCase       )
         return model





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   List[Any]:
         _lowerCAmelCase						=		DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'       )[0]       )       )
         print(F'''Loading DPR biencoder from {self.src_file}'''       )
         _lowerCAmelCase						=		load_states_from_checkpoint(self.src_file       )
         _lowerCAmelCase	,       _lowerCAmelCase						=		model.question_encoder, 'question_model.'
         # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
         _lowerCAmelCase						=		{'bert_model.embeddings.position_ids': model.question_encoder.bert_model.embeddings.position_ids}
         for key, value in saved_state.model_dict.items():
           if key.startswith(_lowerCAmelCase       ):
             _lowerCAmelCase						=		key[len(_lowerCAmelCase       ) :]
             if not key.startswith('encode_proj.'       ):
               _lowerCAmelCase						=		'bert_model.' + key
             _lowerCAmelCase						=		value
         encoder.load_state_dict(_lowerCAmelCase       )
         return model





class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Optional[int]:
         _lowerCAmelCase						=		DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'       )[0]       )       )
         print(F'''Loading DPR reader from {self.src_file}'''       )
         _lowerCAmelCase						=		load_states_from_checkpoint(self.src_file       )
         # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
         _lowerCAmelCase						=		{
             'encoder.bert_model.embeddings.position_ids': model.span_predictor.encoder.bert_model.embeddings.position_ids
         }
         for key, value in saved_state.model_dict.items():
           if key.startswith('encoder.'       ) and not key.startswith('encoder.encode_proj'       ):
             _lowerCAmelCase						=		'encoder.bert_model.' + key[len('encoder.'       ) :]
           _lowerCAmelCase						=		value
         model.span_predictor.load_state_dict(_lowerCAmelCase       )
         return model



def 	_a  (					__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  Path		,				__SCREAMING_SNAKE_CASE					:  Path    ):






  """simple docstring"""




  _lowerCAmelCase						=		Path(__SCREAMING_SNAKE_CASE    )
  dest_dir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase						=		DPRState.from_type(__SCREAMING_SNAKE_CASE		,				src_file=__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		dpr_state.load_dpr_model()
  model.save_pretrained(__SCREAMING_SNAKE_CASE    )
  model.from_pretrained(__SCREAMING_SNAKE_CASE    )  # sanity check


if __name__ == "__main__":
       _UpperCamelCase:					Dict     =argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--type', type=str, help='Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'
       )
       parser.add_argument(
           '--src',
           type=str,
           help=(
               'Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'
               ' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'
               ' \'retriever\' checkpoints.'
           ),
       )
       parser.add_argument('--dest', type=str, default=None, help='Path to the output PyTorch model directory.')
       _UpperCamelCase:					Union[str, Any]     =parser.parse_args()

       _UpperCamelCase:					str     =Path(args.src)
       _UpperCamelCase:					str     =F"converted-{src_file.name}" if args.dest is None else args.dest
       _UpperCamelCase:					int     =Path(dest_dir)
       assert src_file.exists()
       assert (
           args.type is not None
       ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
       convert(args.type, src_file, dest_dir)


import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

_UpperCamelCase:					Tuple     =180



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




  _lowerCAmelCase						=		k_size // 2
  _lowerCAmelCase	,       _lowerCAmelCase						=		np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _lowerCAmelCase						=		(
      1
      / (2 * np.pi * sigma)
      * np.exp(-(np.square(__SCREAMING_SNAKE_CASE    ) + np.square(__SCREAMING_SNAKE_CASE    )) / (2 * np.square(__SCREAMING_SNAKE_CASE    ))    )
  )
  return g



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Optional[Any]    ):






  """simple docstring"""




  _lowerCAmelCase						=		np.zeros(__SCREAMING_SNAKE_CASE    )

  for row in range(1		,				image_shape[0] - 1    ):
    for col in range(1		,				image_shape[1] - 1    ):
      _lowerCAmelCase						=		gradient_direction[row, col]

      if (
          0 <= direction < PI / 8
          or 15 * PI / 8 <= direction <= 2 * PI
          or 7 * PI / 8 <= direction <= 9 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row, col - 1]
        _lowerCAmelCase						=		sobel_grad[row, col + 1]
        if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
          _lowerCAmelCase						=		sobel_grad[row, col]

      elif (
          PI / 8 <= direction < 3 * PI / 8
          or 9 * PI / 8 <= direction < 11 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row + 1, col - 1]
        _lowerCAmelCase						=		sobel_grad[row - 1, col + 1]
        if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
          _lowerCAmelCase						=		sobel_grad[row, col]

      elif (
          3 * PI / 8 <= direction < 5 * PI / 8
          or 11 * PI / 8 <= direction < 13 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row - 1, col]
        _lowerCAmelCase						=		sobel_grad[row + 1, col]
        if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
          _lowerCAmelCase						=		sobel_grad[row, col]

      elif (
          5 * PI / 8 <= direction < 7 * PI / 8
          or 13 * PI / 8 <= direction < 15 * PI / 8
      ):
        _lowerCAmelCase						=		sobel_grad[row - 1, col - 1]
        _lowerCAmelCase						=		sobel_grad[row + 1, col + 1]
        if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
          _lowerCAmelCase						=		sobel_grad[row, col]

  return destination



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  List[Any]    ):






  """simple docstring"""




  for row in range(1		,				image_shape[0] - 1    ):
    for col in range(1		,				image_shape[1] - 1    ):
      if destination[row, col] >= threshold_high:
        _lowerCAmelCase						=		strong
      elif destination[row, col] <= threshold_low:
        _lowerCAmelCase						=		0
      else:
        _lowerCAmelCase						=		weak



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




  for row in range(1		,				image_shape[0]    ):
    for col in range(1		,				image_shape[1]    ):
      if destination[row, col] == weak:
        if 255 in (
            destination[row, col + 1],
            destination[row, col - 1],
            destination[row - 1, col],
            destination[row + 1, col],
            destination[row - 1, col - 1],
            destination[row + 1, col - 1],
            destination[row - 1, col + 1],
            destination[row + 1, col + 1],
        ):
          _lowerCAmelCase						=		strong
        else:
          _lowerCAmelCase						=		0



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  int=15		,				__SCREAMING_SNAKE_CASE					:  int=30		,				__SCREAMING_SNAKE_CASE					:  Tuple=128		,				__SCREAMING_SNAKE_CASE					:  List[Any]=255    ):






  """simple docstring"""




  _lowerCAmelCase						=		img_convolve(__SCREAMING_SNAKE_CASE		,				gen_gaussian_kernel(9		,				sigma=1.4    )    )
  # get the gradient and degree by sobel_filter
  _lowerCAmelCase	,       _lowerCAmelCase						=		sobel_filter(__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		PI + np.radadeg(__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase						=		suppress_non_maximum(image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  detect_high_low_threshold(
      image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  track_edge(image.shape		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )

  return destination


if __name__ == "__main__":
       # read original image in gray mode
       _UpperCamelCase:					Optional[Any]     =cva.imread(R'../image_data/lena.jpg', 0)
       # canny edge detection
       _UpperCamelCase:					Tuple     =canny(lena)
       cva.imshow('canny', canny_destination)
       cva.waitKey(0)
