code: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  lowerCamelCase							:     Tuple								=			{
      """configuration_mobilenet_v1""": [
          """MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """MobileNetV1Config""",
          """MobileNetV1OnnxConfig""",
      ],
  }
  
  try:
   if not is_vision_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase							:     Tuple								=			["""MobileNetV1FeatureExtractor"""]
   lowerCamelCase							:     int								=			["""MobileNetV1ImageProcessor"""]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase							:     List[Any]								=			[
       """MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MobileNetV1ForImageClassification""",
       """MobileNetV1Model""",
       """MobileNetV1PreTrainedModel""",
       """load_tf_weights_in_mobilenet_v1""",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_mobilenet_va import (
       MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileNetVaConfig,
       MobileNetVaOnnxConfig,
   )
  
   try:
    if not is_vision_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
    from .image_processing_mobilenet_va import MobileNetVaImageProcessor
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_mobilenet_va import (
        MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileNetVaForImageClassification,
        MobileNetVaModel,
        MobileNetVaPreTrainedModel,
        load_tf_weights_in_mobilenet_va,
    )
  
  
  else:
   import sys
  
   lowerCamelCase							:     Tuple								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
style_context: 
  
  
  '''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
  
  
  lowerCamelCase							:     List[str]								=			collections.namedtuple(
      'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
  )
  
  def _SCREAMING_SNAKE_CASE   (A ) -> CheckpointState:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         print(f"Reading saved model from {model_file}" )
         lowercase__       =				torch.load(A						,				map_location=lambda A						,				A : default_restore_location(A						,				'''cpu''' ) )
         return CheckpointState(**A )
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __init__(self					:					Any     ,		UpperCamelCase					:					Path				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				src_file
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               raise NotImplementedError
  
  
  
  
  
  
        @staticmethod
        def    UpperCamelCase__	(UpperCamelCase					:					str     ,		*UpperCamelCase					:					Dict     ,		**UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               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 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''				)[0]				)				)
               print(f"Loading DPR biencoder from {self.src_file}"				)
               lowercase__       =				load_states_from_checkpoint(self.src_file				)
               lowercase__    ,lowercase__       =				model.ctx_encoder, '''ctx_model.'''
               # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
               lowercase__       =				{'''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__       =				key[len(UpperCamelCase				) :]
                             if not key.startswith('''encode_proj.'''				):
                                    lowercase__       =				'''bert_model.''' + key
                             lowercase__       =				value
               encoder.load_state_dict(UpperCamelCase				)
               return model
  
  
  
  
  
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''				)[0]				)				)
               print(f"Loading DPR biencoder from {self.src_file}"				)
               lowercase__       =				load_states_from_checkpoint(self.src_file				)
               lowercase__    ,lowercase__       =				model.question_encoder, '''question_model.'''
               # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
               lowercase__       =				{'''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__       =				key[len(UpperCamelCase				) :]
                             if not key.startswith('''encode_proj.'''				):
                                    lowercase__       =				'''bert_model.''' + key
                             lowercase__       =				value
               encoder.load_state_dict(UpperCamelCase				)
               return model
  
  
  
  
  
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''				)[0]				)				)
               print(f"Loading DPR reader from {self.src_file}"				)
               lowercase__       =				load_states_from_checkpoint(self.src_file				)
               # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
               lowercase__       =				{
                   '''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__       =				'''encoder.bert_model.''' + key[len('''encoder.'''				) :]
                      lowercase__       =				value
               model.span_predictor.load_state_dict(UpperCamelCase				)
               return model
  
  
  
  
  
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Optional[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				Path(A )
         dest_dir.mkdir(exist_ok=A )
  
         lowercase__       =				DPRState.from_type(A						,				src_file=A )
         lowercase__       =				dpr_state.load_dpr_model()
         model.save_pretrained(A )
         model.from_pretrained(A )  # sanity check
  
  
  if __name__ == "__main__":
   lowerCamelCase							:     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.')
   lowerCamelCase							:     Optional[int]								=			parser.parse_args()
  
   lowerCamelCase							:     List[str]								=			Path(args.src)
   lowerCamelCase							:     List[Any]								=			f"""converted-{src_file.name}""" if args.dest is None else args.dest
   lowerCamelCase							:     Optional[int]								=			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)
  
  
  
  
  