





"""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


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






def    A__							(     UpperCamelCase			):
							print(F"Reading saved model from {model_file}"			)
							A						=    torch.load(_lowerCAmelCase   ,    map_location=lambda UpperCamelCase   ,    UpperCamelCase			: default_restore_location(_lowerCAmelCase   ,    "cpu"			)			)
							return CheckpointState(**_lowerCAmelCase			)






class    _UpperCAmelCase  :


					def __init__(   self     :Dict	,			__UpperCamelCase     :Optional[int]						):
												A						=    src_file


					def       lowerCamelCase			(   self     :List[Any]						):
												raise NotImplementedError


					@staticmethod
					def       lowerCamelCase			(   __UpperCamelCase     :Dict	,			*__UpperCamelCase     :List[str]	,			**__UpperCamelCase     :Union[str, Any]						):
												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    _UpperCAmelCase  (      _UpperCAmelCase					):


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







class    _UpperCAmelCase  (      _UpperCAmelCase					):


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







class    _UpperCAmelCase  (      _UpperCAmelCase					):


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







def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							A						=    Path(_lowerCAmelCase			)
							dest_dir.mkdir(exist_ok=_lowerCAmelCase			)

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


if __name__ == "__main__":
		_snake_case	:						List[str]					=					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.')
		_snake_case	:						str					=					parser.parse_args()

		_snake_case	:						int					=					Path(args.src)
		_snake_case	:						Union[str, Any]					=					F"""converted-{src_file.name}""" if args.dest is None else args.dest
		_snake_case	:						List[str]					=					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        UpperCAmelCase__   (				_lowerCAmelCase    :			int = 400_0000)    ->		int:



						"""simple docstring"""

						snake_case :       List[Any]				=    1
						snake_case :       int				=    2
						snake_case :       List[str]				=    0
						while j <= n:
												if j % 2 == 0:
																		total += j
												snake_case				,					snake_case :       Any				=    j, i + j

						return total


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





