



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							=       180

def       A_   (						__a  :					Optional[Any]			,		__a  :					Union[str, Any]		):

				"""simple docstring"""
				a__					   =		k_size // 2
				a__   ,      a__					   =		np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				a__					   =		(
				    1
				    / (2 * np.pi * sigma)
				    * np.exp(-(np.square(__a		) + np.square(__a		)) / (2 * np.square(__a		))		)
				)
				return g

def       A_   (						__a  :					Optional[int]			,		__a  :					Tuple			,		__a  :					List[str]		):

				"""simple docstring"""
				a__					   =		np.zeros(__a		)

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

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

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

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

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

				return destination

def       A_   (						__a  :					Tuple			,		__a  :					Optional[Any]			,		__a  :					Union[str, Any]			,		__a  :					int			,		__a  :					Optional[Any]			,		__a  :					Union[str, 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:
																a__					   =		strong
												elif destination[row, col] <= threshold_low:
																a__					   =		0
												else:
																a__					   =		weak

def       A_   (						__a  :					Tuple			,		__a  :					int			,		__a  :					List[str]			,		__a  :					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],
																):
																				a__					   =		strong
																else:
																				a__					   =		0

def       A_   (						__a  :					str			,		__a  :					str=15			,		__a  :					Optional[int]=30			,		__a  :					Dict=128			,		__a  :					List[str]=255		):

				"""simple docstring"""
				# gaussian_filter
				a__					   =		img_convolve(__a			,		gen_gaussian_kernel(9			,		sigma=1.4		)		)
				# get the gradient and degree by sobel_filter
				a__   ,      a__					   =		sobel_filter(__a		)
				a__					   =		PI + np.radadeg(__a		)

				a__					   =		suppress_non_maximum(image.shape			,		__a			,		__a		)

				detect_high_low_threshold(
				    image.shape			,		__a			,		__a			,		__a			,		__a			,		__a		)

				track_edge(image.shape			,		__a			,		__a			,		__a		)

				return destination


if __name__ == "__main__":
	# read original image in gray mode
	UpperCAmelCase							=       cva.imread(R"""../image_data/lena.jpg""", 0)
	# canny edge detection
	UpperCAmelCase							=       canny(lena)
	cva.imshow("""canny""", canny_destination)
	cva.waitKey(0)









"""simple docstring"""



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						:    Optional[Any]		=						collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str       ):





  '''simple docstring'''





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

class 			a	:







    def __init__(     self				,						_lowerCamelCase			):
      lowercase				       =			src_file







    def      UpperCamelCase_ (     self			):
      raise NotImplementedError







    @staticmethod
    def      UpperCamelCase_ (     _lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			):
      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 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'			)[0]			)			)
      print(F'Loading DPR biencoder from {self.src_file}'			)
      lowercase				       =			load_states_from_checkpoint(self.src_file			)
      lowercase       ,	lowercase				       =			model.ctx_encoder, 'ctx_model.'
      # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
      lowercase				       =			{'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			):
          lowercase				       =			key[len(_lowerCamelCase			) :]
          if not key.startswith('encode_proj.'			):
            lowercase				       =			'bert_model.' + key
          lowercase				       =			value
      encoder.load_state_dict(_lowerCamelCase			)
      return model


class 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'			)[0]			)			)
      print(F'Loading DPR biencoder from {self.src_file}'			)
      lowercase				       =			load_states_from_checkpoint(self.src_file			)
      lowercase       ,	lowercase				       =			model.question_encoder, 'question_model.'
      # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
      lowercase				       =			{'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			):
          lowercase				       =			key[len(_lowerCamelCase			) :]
          if not key.startswith('encode_proj.'			):
            lowercase				       =			'bert_model.' + key
          lowercase				       =			value
      encoder.load_state_dict(_lowerCamelCase			)
      return model


class 			a	(  a_      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'			)[0]			)			)
      print(F'Loading DPR reader from {self.src_file}'			)
      lowercase				       =			load_states_from_checkpoint(self.src_file			)
      # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
      lowercase				       =			{
          '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'			):
          lowercase				       =			'encoder.bert_model.' + key[len('encoder.'			) :]
        lowercase				       =			value
      model.span_predictor.load_state_dict(_lowerCamelCase			)
      return model


def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    Path						,   __snake_case     :    Path       ):





  '''simple docstring'''





  lowercase				       =			Path(__snake_case       )
  dest_dir.mkdir(exist_ok=__snake_case       )

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


if __name__ == "__main__":
    _UpperCamelCase						:    Optional[int]		=						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						:    List[Any]		=						parser.parse_args()

    _UpperCamelCase						:    int		=						Path(args.src)
    _UpperCamelCase						:    List[Any]		=						F'''converted-{src_file.name}''' if args.dest is None else args.dest
    _UpperCamelCase						:    Optional[Any]		=						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)


