
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


__SCREAMING_SNAKE_CASE	:      List[str]			   =   collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)
def 			UpperCAmelCase__							(				__magic_name__  :   str     ):


					'''simple docstring'''

					print(f'''Reading saved model from {model_file}'''     )
					lowerCAmelCase						:    Dict     		=   torch.load(_SCREAMING_SNAKE_CASE							,     map_location=lambda __magic_name__							,     __magic_name__     : default_restore_location(_SCREAMING_SNAKE_CASE							,     '''cpu'''     )     )
					return CheckpointState(**_SCREAMING_SNAKE_CASE     )


class 						__magic_name__      :





		def __init__(							self			:		int  ,     lowerCamelCase__			:		Union[str, Any] ):
							lowerCAmelCase						:    List[Any]     		=   src_file





		def 					_A							(							self			:		Dict ):
							raise NotImplementedError





		@staticmethod
		def 					_A							(							lowerCamelCase__			:		str  ,     *lowerCamelCase__			:		Optional[Any]  ,     **lowerCamelCase__			:		Union[str, Any] ):
							if comp_type.startswith('''c''' ):
												return DPRContextEncoderState(*A_  ,     **A_ )
							if comp_type.startswith('''q''' ):
												return DPRQuestionEncoderState(*A_  ,     **A_ )
							if comp_type.startswith('''r''' ):
												return DPRReaderState(*A_  ,     **A_ )
							else:
												raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.''' )



class 						__magic_name__      (  _lowerCAmelCase			):





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



class 						__magic_name__      (  _lowerCAmelCase			):





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



class 						__magic_name__      (  _lowerCAmelCase			):





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



def 			UpperCAmelCase__							(				__magic_name__  :   str							,     __magic_name__  :   Path							,     __magic_name__  :   Path     ):


					'''simple docstring'''

					lowerCAmelCase						:    Optional[int]     		=   Path(_SCREAMING_SNAKE_CASE     )
					dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE     )

					lowerCAmelCase						:    Any     		=   DPRState.from_type(_SCREAMING_SNAKE_CASE							,     src_file=_SCREAMING_SNAKE_CASE     )
					lowerCAmelCase						:    Dict     		=   dpr_state.load_dpr_model()
					model.save_pretrained(_SCREAMING_SNAKE_CASE     )
					model.from_pretrained(_SCREAMING_SNAKE_CASE     )  # sanity check


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE	:      Optional[Any]			   =   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.')
		__SCREAMING_SNAKE_CASE	:      Optional[Any]			   =   parser.parse_args()

		__SCREAMING_SNAKE_CASE	:      str			   =   Path(args.src)
		__SCREAMING_SNAKE_CASE	:      List[Any]			   =   f"""converted-{src_file.name}""" if args.dest is None else args.dest
		__SCREAMING_SNAKE_CASE	:      List[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)





'''simple docstring'''

def 		_snake_case		(					_SCREAMING_SNAKE_CASE     :						int = 4_000_000							)			->						int:







	"""simple docstring"""
	lowerCAmelCase    =	1
	lowerCAmelCase    =	2
	lowerCAmelCase    =	0
	while j <= n:
		if j % 2 == 0:
			total += j
		lowerCAmelCase,   lowerCAmelCase    =	j, i + j

	return total


if __name__ == "__main__":
				print(F'''{solution() = }''')