
"""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 				A		(							snake_case :Union[str, Any]						) ->      Optional[int]:
 print(f'Reading saved model from {model_file}'						)
 __UpperCamelCase							    =			torch.load(lowerCAmelCase__							,   map_location=lambda snake_case							,   snake_case						: default_restore_location(lowerCAmelCase__							,   'cpu'						)						)
 return CheckpointState(**lowerCAmelCase__						)

class __lowerCAmelCase						:





     def __init__(      self      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			src_file





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      raise NotImplementedError







     @staticmethod
     def 		UpperCAmelCase   (      __UpperCAmelCase      ,	*__UpperCAmelCase      ,	**__UpperCAmelCase ):

      '''simple docstring'''




      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 __lowerCAmelCase						( SCREAMING_SNAKE_CASE__     ):





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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


class __lowerCAmelCase						( SCREAMING_SNAKE_CASE__     ):





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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


class __lowerCAmelCase						( SCREAMING_SNAKE_CASE__     ):





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




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






def 				A		(							snake_case :Tuple							,   snake_case :Optional[int]							,   snake_case :Optional[Any]						) ->      List[str]:
 __UpperCamelCase							    =			Path(lowerCAmelCase__						)
 dest_dir.mkdir(exist_ok=lowerCAmelCase__						)

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


if __name__ == "__main__":
      UpperCamelCase      :							List[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.")
      UpperCamelCase      :							Union[str, Any]          =					parser.parse_args()

      UpperCamelCase      :							Optional[Any]          =					Path(args.src)
      UpperCamelCase      :							Optional[Any]          =					f'''converted-{src_file.name}''' if args.dest is None else args.dest
      UpperCamelCase      :							Dict          =					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)





def 			UpperCamelCase       (				lowerCAmelCase__ = 30						):
					'''simple docstring'''
					lowercase		      =		exponent + 2
					lowercase		      =		(1 + 5**0.5) / 2
					lowercase		      =		(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

					return int(lowerCAmelCase__						)


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




