


'''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 			__lowercase	(						_UpperCamelCase     )    ->CheckpointState:







      """simple docstring"""

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







class 				__SCREAMING_SNAKE_CASE   :


      def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
            lowercase			:   str					   =		src_file


      def        __lowerCamelCase			(							self		):
            raise NotImplementedError






      @staticmethod
      def        __lowerCamelCase			(							SCREAMING_SNAKE_CASE__							,						*SCREAMING_SNAKE_CASE__							,						**SCREAMING_SNAKE_CASE__		):
            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 				__SCREAMING_SNAKE_CASE   (			A__   ):


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







class 				__SCREAMING_SNAKE_CASE   (			A__   ):


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







class 				__SCREAMING_SNAKE_CASE   (			A__   ):


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





def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->str:







      """simple docstring"""

      lowercase			:   str					   =		Path(_UpperCAmelCase     )
      dest_dir.mkdir(exist_ok=_UpperCAmelCase     )

      lowercase			:   Any					   =		DPRState.from_type(_UpperCAmelCase,    src_file=_UpperCAmelCase     )
      lowercase			:   List[Any]					   =		dpr_state.load_dpr_model()
      model.save_pretrained(_UpperCAmelCase     )
      model.from_pretrained(_UpperCAmelCase     )  # 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)





import math




def 			__lowercase	(						_UpperCamelCase     )    ->Union[str, Any]:







      """simple docstring"""

      lowercase			:   List[Any]					   =		0
      if n % 2 == 0:
            count += 1
            while n % 2 == 0:
                  lowercase			:   Any					   =		int(n / 2     )
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

      lowercase			:   Optional[int]					   =		3

      while i <= int(math.sqrt(_UpperCamelCase     )     ):
            if n % i == 0:
                  count += 1
                  while n % i == 0:
                        lowercase			:   Optional[Any]					   =		int(n / i     )
            lowercase			:   str					   =		i + 2

      # this condition checks the prime
      # number n is greater than 2

      if n > 2:
            count += 1
      return count


if __name__ == "__main__":
  __a   				=      51_24_21_83
  print(F'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
  print(F'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')

