'''simple docstring'''

from __future__ import annotations




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Optional[int]				=    value
									snake_case :       Node | None				=    None
									snake_case :       Node | None				=    None







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			__magic_name__ :				int



			def __init__(							self					):
									snake_case :       str				=    0



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_					):
									if node is None:
															return

									if path_sum == self.target:
															self.paths += 1

									if node.left:
															self.depth_first_search(node.left				,			path_sum + node.left.value					)
									if node.right:
															self.depth_first_search(node.right				,			path_sum + node.right.value					)






			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = None					):
									if node is None:
															return 0
									if target is not None:
															snake_case :       Dict				=    target

									self.depth_first_search(snake_case_				,			node.value					)
									self.path_sum(node.left					)
									self.path_sum(node.right					)

									return self.paths


if __name__ == "__main__":
						import doctest

						doctest.testmod()






'''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)





