




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


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


def 						lowercase		(     _lowerCAmelCase      ):
  print(F'''Reading saved model from {model_file}'''      )
  UpperCAmelCase__    						=   torch.load(_lowerCAmelCase   ,     map_location=lambda _lowerCAmelCase   ,     _lowerCAmelCase      : default_restore_location(_lowerCAmelCase   ,     """cpu"""      )      )
  return CheckpointState(**_lowerCAmelCase      )





class     snake_case  :


   '''simple docstring'''







   def __init__(					self     :	List[str]						,    lowerCamelCase_     :	Path			)					->List[Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   src_file




   def 	UpperCAmelCase       (					self     :	Optional[int]			)					->Any:
     '''simple docstring'''







     raise NotImplementedError







   @staticmethod
   def 	UpperCAmelCase       (					lowerCamelCase_     :	str						,    *lowerCamelCase_     :	Union[str, Any]						,    **lowerCamelCase_     :	List[str]			)					->"DPRState":
     '''simple docstring'''







     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     snake_case  (	_snake_case							):


   '''simple docstring'''







   def 	UpperCAmelCase       (					self     :	Any			)					->Tuple:
     '''simple docstring'''







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





class     snake_case  (	_snake_case							):


   '''simple docstring'''







   def 	UpperCAmelCase       (					self     :	Dict			)					->List[str]:
     '''simple docstring'''







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





class     snake_case  (	_snake_case							):


   '''simple docstring'''







   def 	UpperCAmelCase       (					self     :	int			)					->Tuple:
     '''simple docstring'''







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


def 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   Path(_lowerCAmelCase      )
  dest_dir.mkdir(exist_ok=_lowerCAmelCase      )

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


if __name__ == "__main__":
   snake_case__ :						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.''')
   snake_case__ :						int     =			parser.parse_args()

   snake_case__ :						Union[str, Any]     =			Path(args.src)
   snake_case__ :						Dict     =			F"""converted-{src_file.name}""" if args.dest is None else args.dest
   snake_case__ :						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)










from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like


def 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase=3      ):
  UpperCAmelCase__    						=   int(mask / 2      )
  # copy image size
  UpperCAmelCase__    						=   zeros_like(_lowerCAmelCase      )
  for i in range(_lowerCAmelCase   ,     gray_img.shape[0] - bd      ):
    for j in range(_lowerCAmelCase   ,     gray_img.shape[1] - bd      ):
      # get mask according with mask
      UpperCAmelCase__    						=   ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
      # calculate mask median
      UpperCAmelCase__    						=   sort(_lowerCAmelCase      )[inta(divide((multiply(_lowerCAmelCase   ,     _lowerCAmelCase      ))   ,     2      ) + 1      )]
      UpperCAmelCase__    						=   median
  return median_img


if __name__ == "__main__":
   # read original image
   snake_case__ :						Tuple     =			imread('''../image_data/lena.jpg''')
   # turn image in gray scale value
   snake_case__ :						List[Any]     =			cvtColor(img, COLOR_BGR2GRAY)

   # get values with two different mask size
   snake_case__ :						str     =			median_filter(gray, 3)
   snake_case__ :						List[str]     =			median_filter(gray, 5)

   # show result images
   imshow('''median filter with 3x3 mask''', medianaxa)
   imshow('''median filter with 5x5 mask''', medianaxa)
   waitKey(0)




