
'''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__ :		Optional[Any]					=collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str			)					->							CheckpointState:





  """simple docstring"""

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







class 			__A					:
 def __init__( self      :     Union[str, Any]					, lowerCamelCase      :     Path							):



   """simple docstring"""





   __A		:  str       				=		src_file
 def 		lowercase_( self      :     Dict							):



   """simple docstring"""





   raise NotImplementedError






 @staticmethod
 def 		lowercase_( lowerCamelCase      :     str					, *lowerCamelCase      :     Dict					, **lowerCamelCase      :     Tuple							):



   """simple docstring"""





   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					(			_SCREAMING_SNAKE_CASE      ):
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





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







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def 		lowercase_( self      :     int							):



   """simple docstring"""





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







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





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







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	Path      ,  __SCREAMING_SNAKE_CASE    :	Path			)					->							Union[str, Any]:





  """simple docstring"""

  __A		:  str       				=		Path(__SCREAMING_SNAKE_CASE			)
  dest_dir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE			)

  __A		:  List[str]       				=		DPRState.from_type(__SCREAMING_SNAKE_CASE      ,  src_file=__SCREAMING_SNAKE_CASE			)
  __A		:  Any       				=		dpr_state.load_dpr_model()
  model.save_pretrained(__SCREAMING_SNAKE_CASE			)
  model.from_pretrained(__SCREAMING_SNAKE_CASE			)  # sanity check


if __name__ == "__main__":
     A__ :		Tuple					=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__ :		Dict					=parser.parse_args()

     A__ :		Tuple					=Path(args.src)
     A__ :		Dict					=F"converted-{src_file.name}" if args.dest is None else args.dest
     A__ :		Union[str, 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)





'''simple docstring'''
A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_000,
    "atomic-mass-unit": 6.0_2_2_1_3_6_6_5_2E2_6,
}

A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_6_0_5_4_0_1_9_9E-2_7,
}







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	float			)					->							float:





  """simple docstring"""

  if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
    __A		:  Tuple       				=		(
        F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
        F"Supported values are: {', '.join(__SCREAMING_SNAKE_CASE			)}"
    )
    raise ValueError(__SCREAMING_SNAKE_CASE			)
  return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
     import doctest

     doctest.testmod()



