'''simple docstring'''

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


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Tuple,      _lowerCAmelCase    :			Any=3)    ->		Dict:



						"""simple docstring"""

						snake_case :       List[str]				=    int(mask / 2)
						# copy image size
						snake_case :       List[Any]				=    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
																		snake_case :       Any				=    ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1])
																		# calculate mask median
																		snake_case :       Optional[int]				=    sort(_lowerCAmelCase)[inta(divide((multiply(_lowerCAmelCase,      _lowerCAmelCase)),      2) + 1)]
																		snake_case :       int				=    median
						return median_img


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

						# get values with two different mask size
						A_	   = median_filter(gray, 3)
						A_	   = median_filter(gray, 5)

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


A_	   = collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)


def        UpperCAmelCase__   (				_lowerCAmelCase    :			str)    ->		CheckpointState:



						"""simple docstring"""

						print(f"""Reading saved model from {model_file}""")
						snake_case :       Optional[Any]				=    torch.load(_lowerCAmelCase,      map_location=lambda _lowerCAmelCase,      _lowerCAmelCase: default_restore_location(_lowerCAmelCase,      "cpu"))
						return CheckpointState(**_lowerCAmelCase)




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    src_file



			def   lowerCamelCase (							self					):
									raise NotImplementedError






			@staticmethod
			def   lowerCamelCase (							snake_case_				,			*snake_case_				,			**snake_case_					):
									if comp_type.startswith("c"					):
															return DPRContextEncoderState(*snake_case_				,			**snake_case_					)
									if comp_type.startswith("q"					):
															return DPRQuestionEncoderState(*snake_case_				,			**snake_case_					)
									if comp_type.startswith("r"					):
															return DPRReaderState(*snake_case_				,			**snake_case_					)
									else:
															raise ValueError("Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'."					)







class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






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







class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       Any				=    DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"					)[0]					)					)
									print(F"""Loading DPR biencoder from {self.src_file}"""					)
									snake_case :       Dict				=    load_states_from_checkpoint(self.src_file					)
									snake_case				,					snake_case :       Tuple				=    model.question_encoder, "question_model."
									# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
									snake_case :       Dict				=    {"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(snake_case_					):
																					snake_case :       Optional[int]				=    key[len(snake_case_					) :]
																					if not key.startswith("encode_proj."					):
																											snake_case :       List[str]				=    "bert_model." + key
																					snake_case :       Union[str, Any]				=    value
									encoder.load_state_dict(snake_case_					)
									return model







class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"					)[0]					)					)
									print(F"""Loading DPR reader from {self.src_file}"""					)
									snake_case :       List[str]				=    load_states_from_checkpoint(self.src_file					)
									# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
									snake_case :       List[str]				=    {
									    "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"					):
																					snake_case :       List[Any]				=    "encoder.bert_model." + key[len("encoder."					) :]
															snake_case :       Union[str, Any]				=    value
									model.span_predictor.load_state_dict(snake_case_					)
									return model







def        UpperCAmelCase__   (				_lowerCAmelCase    :			str,      _lowerCAmelCase    :			Path,      _lowerCAmelCase    :			Path)    ->		List[Any]:



						"""simple docstring"""

						snake_case :       Any				=    Path(_lowerCAmelCase)
						dest_dir.mkdir(exist_ok=_lowerCAmelCase)

						snake_case :       Tuple				=    DPRState.from_type(_lowerCAmelCase,      src_file=_lowerCAmelCase)
						snake_case :       Optional[int]				=    dpr_state.load_dpr_model()
						model.save_pretrained(_lowerCAmelCase)
						model.from_pretrained(_lowerCAmelCase)  # sanity check


if __name__ == "__main__":
						A_	   = 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.")
						A_	   = parser.parse_args()

						A_	   = Path(args.src)
						A_	   = f"converted-{src_file.name}" if args.dest is None else args.dest
						A_	   = 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)





