






"""simple docstring"""



def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int = 99_80_01       ):





  '''simple docstring'''





  for number in range(n - 1						,   99_99						,   -1       ):
    lowercase				       =			str(__snake_case       )

    # checks whether 'str_number' is a palindrome.
    if str_number == str_number[::-1]:
      lowercase				       =			9_99

      # if 'number' is a product of two 3-digit numbers
      # then number is the answer otherwise fetch next number.
      while divisor != 99:
        if (number % divisor == 0) and (len(str(number // divisor       )       ) == 3.0):
          return number
        divisor -= 1
  raise ValueError('That number is larger than our acceptable range.'       )


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










"""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


_UpperCamelCase						:    Optional[Any]		=						collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str       ):





  '''simple docstring'''





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

class 			a	:







    def __init__(     self				,						_lowerCamelCase			):
      lowercase				       =			src_file







    def      UpperCamelCase_ (     self			):
      raise NotImplementedError







    @staticmethod
    def      UpperCamelCase_ (     _lowerCamelCase				,						*_lowerCamelCase				,						**_lowerCamelCase			):
      if comp_type.startswith('c'			):
        return DPRContextEncoderState(*_lowerCamelCase				,						**_lowerCamelCase			)
      if comp_type.startswith('q'			):
        return DPRQuestionEncoderState(*_lowerCamelCase				,						**_lowerCamelCase			)
      if comp_type.startswith('r'			):
        return DPRReaderState(*_lowerCamelCase				,						**_lowerCamelCase			)
      else:
        raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'			)


class 			a	(  a_      ):







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


class 			a	(  a_      ):







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


class 			a	(  a_      ):







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


def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    Path						,   __snake_case     :    Path       ):





  '''simple docstring'''





  lowercase				       =			Path(__snake_case       )
  dest_dir.mkdir(exist_ok=__snake_case       )

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


if __name__ == "__main__":
    _UpperCamelCase						:    Optional[int]		=						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						:    List[Any]		=						parser.parse_args()

    _UpperCamelCase						:    int		=						Path(args.src)
    _UpperCamelCase						:    List[Any]		=						F'''converted-{src_file.name}''' if args.dest is None else args.dest
    _UpperCamelCase						:    Optional[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)


