

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


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


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						print(F'''Reading saved model from {model_file}'''	)
						snake_case_       =     torch.load(SCREAMING_SNAKE_CASE__  ,		map_location=lambda SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	: default_restore_location(SCREAMING_SNAKE_CASE__  ,		'''cpu'''	)	)
						return CheckpointState(**SCREAMING_SNAKE_CASE__	)





class      snake_case_       :







			'''simple docstring'''

			def __init__(					self			:    Dict							,       _UpperCamelCase			:    Path							)				->int:
									snake_case_       =     src_file







			def     snake_case__(					self			:    Optional[Any]							)				->Optional[Any]:
									raise NotImplementedError







			@staticmethod
			def     snake_case__(					_UpperCamelCase			:    str							,       *_UpperCamelCase			:    Optional[int]							,       **_UpperCamelCase			:    List[str]							)				->"DPRState":
									if comp_type.startswith('''c'''							):
															return DPRContextEncoderState(*_UpperCamelCase							,       **_UpperCamelCase							)
									if comp_type.startswith('''q'''							):
															return DPRQuestionEncoderState(*_UpperCamelCase							,       **_UpperCamelCase							)
									if comp_type.startswith('''r'''							):
															return DPRReaderState(*_UpperCamelCase							,       **_UpperCamelCase							)
									else:
															raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''							)






class      snake_case_       (		__A ):







			'''simple docstring'''

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






class      snake_case_       (		__A ):







			'''simple docstring'''

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






class      snake_case_       (		__A ):







			'''simple docstring'''

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






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     Path(SCREAMING_SNAKE_CASE__	)
						dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__	)

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


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

					lowerCAmelCase_	     =     Path(args.src)
					lowerCAmelCase_	     =     f"""converted-{src_file.name}""" if args.dest is None else args.dest
					lowerCAmelCase_	     =     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   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     5381
						for x in s:
												snake_case_       =     ((hash_value << 5) + hash_value) + ord(SCREAMING_SNAKE_CASE__	)
						return hash_value & 0xFF_FF_FF_FF