

"""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       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     Union[str, Any]:



       """simple docstring"""







       print(f"""Reading saved model from {model_file}"""   )
       __magic_name__			:		Optional[int]        =     torch.load(_lowercase     , map_location=lambda lowerCamelCase     , lowerCamelCase   : default_restore_location(_lowercase     , '''cpu'''   )   )
       return CheckpointState(**_lowercase   )

class        _UpperCamelCase	:


  """simple docstring"""





  def __init__( self					: List[str]       ,       snake_case					: Path      )	->			Union[str, Any]:


         '''simple docstring'''





         __magic_name__			:		Any        =     src_file



  def 							_UpperCAmelCase      ( self					: str      )	->			str:


         '''simple docstring'''





         raise NotImplementedError




  @staticmethod
  def 							_UpperCAmelCase      ( snake_case					: str       ,       *snake_case					: Union[str, Any]       ,       **snake_case					: List[str]      )	->			"DPRState":


         '''simple docstring'''





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

class        _UpperCamelCase	(						_a	):


  """simple docstring"""





  def 							_UpperCAmelCase      ( self					: List[str]      )	->			List[str]:


         '''simple docstring'''





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

class        _UpperCamelCase	(						_a	):


  """simple docstring"""





  def 							_UpperCAmelCase      ( self					: Dict      )	->			Optional[Any]:


         '''simple docstring'''





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

class        _UpperCamelCase	(						_a	):


  """simple docstring"""





  def 							_UpperCAmelCase      ( self					: Union[str, Any]      )	->			Any:


         '''simple docstring'''





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







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					Path     , lowerCamelCase							:					Path   )						->     Optional[int]:



       """simple docstring"""







       __magic_name__			:		Optional[Any]        =     Path(_lowercase   )
       dest_dir.mkdir(exist_ok=_lowercase   )

       __magic_name__			:		List[str]        =     DPRState.from_type(_lowercase     , src_file=_lowercase   )
       __magic_name__			:		Tuple        =     dpr_state.load_dpr_model()
       model.save_pretrained(_lowercase   )
       model.from_pretrained(_lowercase   )  # 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)




'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase     :Union[str, Any]												=  {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Optional[int]												=  ["MPNetTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Union[str, Any]												=  [
          "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MPNetForMaskedLM",
          "MPNetForMultipleChoice",
          "MPNetForQuestionAnswering",
          "MPNetForSequenceClassification",
          "MPNetForTokenClassification",
          "MPNetLayer",
          "MPNetModel",
          "MPNetPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase     :Any												=  [
          "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMPNetEmbeddings",
          "TFMPNetForMaskedLM",
          "TFMPNetForMultipleChoice",
          "TFMPNetForQuestionAnswering",
          "TFMPNetForSequenceClassification",
          "TFMPNetForTokenClassification",
          "TFMPNetMainLayer",
          "TFMPNetModel",
          "TFMPNetPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
      from .tokenization_mpnet import MPNetTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mpnet_fast import MPNetTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mpnet import (
                MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                MPNetForMaskedLM,
                MPNetForMultipleChoice,
                MPNetForQuestionAnswering,
                MPNetForSequenceClassification,
                MPNetForTokenClassification,
                MPNetLayer,
                MPNetModel,
                MPNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mpnet import (
                TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMPNetEmbeddings,
                TFMPNetForMaskedLM,
                TFMPNetForMultipleChoice,
                TFMPNetForQuestionAnswering,
                TFMPNetForSequenceClassification,
                TFMPNetForTokenClassification,
                TFMPNetMainLayer,
                TFMPNetModel,
                TFMPNetPreTrainedModel,
            )

else:
      import sys

      __UpperCAmelCase     :Optional[Any]												=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)