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





def 							_lowerCamelCase			(					lowerCamelCase_:  str		):



				'''simple docstring'''







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

class     _SCREAMING_SNAKE_CASE							:





				def __init__(		self :		List[Any]					,      snake_case_ :		Path							):


								"""simple docstring"""

								A     :  List[str]   =				src_file





				def        _UpperCAmelCase						(		self :		List[str]							):


								"""simple docstring"""

								raise NotImplementedError






				@staticmethod
				def        _UpperCAmelCase						(		snake_case_ :		str					,      *snake_case_ :		Union[str, Any]					,      **snake_case_ :		List[Any]							):


								"""simple docstring"""

								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							(		snake_case							):





				def        _UpperCAmelCase						(		self :		Any							):


								"""simple docstring"""

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



class     _SCREAMING_SNAKE_CASE							(		snake_case							):





				def        _UpperCAmelCase						(		self :		Dict							):


								"""simple docstring"""

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



class     _SCREAMING_SNAKE_CASE							(		snake_case							):





				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

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





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  Path				,	lowerCamelCase_:  Path		):



				'''simple docstring'''







				A     :  List[str]   =				Path(lowerCamelCase_		)
				dest_dir.mkdir(exist_ok=lowerCamelCase_		)

				A     :  str   =				DPRState.from_type(lowerCamelCase_				,	src_file=lowerCamelCase_		)
				A     :  Any   =				dpr_state.load_dpr_model()
				model.save_pretrained(lowerCamelCase_		)
				model.from_pretrained(lowerCamelCase_		)  # sanity check


if __name__ == "__main__":
					UpperCamelCase_  =	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_  =	parser.parse_args()

					UpperCamelCase_  =	Path(args.src)
					UpperCamelCase_  =	F'''converted-{src_file.name}''' if args.dest is None else args.dest
					UpperCamelCase_  =	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)

def 							_lowerCamelCase			(					lowerCamelCase_:  int		):



				'''simple docstring'''







				if not isinstance(lowerCamelCase_				,	lowerCamelCase_		):
								raise ValueError('''double_factorial() only accepts integral values'''		)
				if num < 0:
								raise ValueError('''double_factorial() not defined for negative values'''		)
				A     :  int   =				1
				for i in range(lowerCamelCase_				,	0				,	-2		):
								value *= i
				return value


if __name__ == "__main__":
					import doctest

					doctest.testmod()