




'''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    snake_case__     (  a	)			->       CheckpointState:





			'''simple docstring'''






			print(F"""Reading saved model from {model_file}"""	)
			snake_case__          =						torch.load(a       ,      map_location=lambda a       ,      a	: default_restore_location(a       ,      """cpu"""	)	)
			return CheckpointState(**a	)





class __magic_name__:






						def __init__(   self						:						Tuple					,   __UpperCamelCase						:						Path       ):



									'''simple docstring'''






									snake_case__          =						src_file






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									raise NotImplementedError







						@staticmethod
						def       __lowerCAmelCase(   __UpperCamelCase						:						str					,   *__UpperCamelCase						:						Optional[int]					,   **__UpperCamelCase						:						int       ):



									'''simple docstring'''






									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 __magic_name__(						__lowerCAmelCase      ):






						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									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 __magic_name__(						__lowerCAmelCase      ):






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									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 __magic_name__(						__lowerCAmelCase      ):






						def       __lowerCAmelCase(   self						:						str       ):



									'''simple docstring'''






									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    snake_case__     (  a       ,      a       ,      a	)			->       int:





			'''simple docstring'''






			snake_case__          =						Path(a	)
			dest_dir.mkdir(exist_ok=a	)

			snake_case__          =						DPRState.from_type(a       ,      src_file=a	)
			snake_case__          =						dpr_state.load_dpr_model()
			model.save_pretrained(a	)
			model.from_pretrained(a	)  # 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)




"""simple docstring"""
import os
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:Optional[Any]		=     os.path.dirname(os.path.realpath(_lowercase      )      )
						snake_case_					:int		=     os.path.join(_lowercase,   """triangle.txt"""      )

						with open(_lowercase      ) as in_file:
												snake_case_					:List[Any]		=     [[int(_lowercase      ) for i in line.split()] for line in in_file]

						while len(_lowercase      ) != 1:
												snake_case_					:str		=     triangle.pop()
												snake_case_					:int		=     triangle[-1]
												for j in range(len(_lowercase      ) - 1      ):
																		curr_row[j] += max(last_row[j],   last_row[j + 1]      )
						return triangle[0][0]


if __name__ == "__main__":
						print(solution())
