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





def 							snake_case				(		UpperCAmelCase						)->      CheckpointState:







      """simple docstring"""
      print(f'Reading saved model from {model_file}'						)
      __A													=      torch.load(UpperCAmelCase   ,	map_location=lambda UpperCAmelCase   ,	UpperCAmelCase						: default_restore_location(UpperCAmelCase   ,	'cpu'						)						)
      return CheckpointState(**UpperCAmelCase						)


class 			UpperCamelCase__	:






 def __init__(      self	:Union[str, Any]						,					_A	:Path      )					->      Tuple:







       '''simple docstring'''



       __A													=      src_file






 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[int]:







       '''simple docstring'''



       raise NotImplementedError






 @staticmethod
 def 						lowercase_    (      _A	:str						,					*_A	:Union[str, Any]						,					**_A	:Optional[int]      )					->      "DPRState":







       '''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 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):






 def 						lowercase_    (      self	:List[Any]      )					->      Tuple:







       '''simple docstring'''



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


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):






 def 						lowercase_    (      self	:Dict      )					->      str:







       '''simple docstring'''



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


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE):






 def 						lowercase_    (      self	:Tuple      )					->      Dict:







       '''simple docstring'''



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





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      List[Any]:







      """simple docstring"""
      __A													=      Path(UpperCAmelCase						)
      dest_dir.mkdir(exist_ok=UpperCAmelCase						)

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


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

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A     						=      {'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''HubertForCTC''',
     '''HubertForSequenceClassification''',
     '''HubertModel''',
     '''HubertPreTrainedModel''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFHubertForCTC''',
     '''TFHubertModel''',
     '''TFHubertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

 __A     						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

