code: 
  
  
  """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_     :     Any    		=						collections.namedtuple(
      """CheckpointState""", ["""model_dict""", """optimizer_dict""", """scheduler_dict""", """offset""", """epoch""", """encoder_params"""]
  )
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      print(f'''Reading saved model from {model_file}'''	)
      a					=torch.load(lowercase ,					map_location=lambda lowercase ,					lowercase	: default_restore_location(lowercase ,					'''cpu'''	)	)
      return CheckpointState(**lowercase	)
  
  
  
  
  
  
  class    __A       :
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def __init__(	self				,					__A		)      ->  Tuple:
       a					=src_file
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
       raise NotImplementedError
  
  
  
  
  
   @staticmethod
   def    SCREAMING_SNAKE_CASE							(	__A				,					*__A				,					**__A		)      ->  "DPRState":
       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    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       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    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
       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    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
       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 _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=Path(lowercase	)
      dest_dir.mkdir(exist_ok=lowercase	)
  
      a					=DPRState.from_type(lowercase ,					src_file=lowercase	)
      a					=dpr_state.load_dpr_model()
      model.save_pretrained(lowercase	)
      model.from_pretrained(lowercase	)  # sanity check
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Optional[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_     :     List[Any]    		=						parser.parse_args()
  
         lowerCamelCase_     :     List[Any]    		=						Path(args.src)
         lowerCamelCase_     :     Union[str, Any]    		=						F'converted-{src_file.name}' if args.dest is None else args.dest
         lowerCamelCase_     :     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)
  
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     Any    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Tuple    		=						{
      """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"mobilevitv2"
  
  
   def __init__(	self				,					__A=3				,					__A=256				,					__A=2				,					__A=2.0				,					__A="swish"				,					__A=3				,					__A=32				,					__A=0.1				,					__A=0.02				,					__A=1E-5				,					__A=512				,					__A=[6, 12, 18]				,					__A=0.1				,					__A=255				,					__A=[2, 4, 3]				,					__A=[128, 192, 256]				,					__A=1.0				,					__A=2				,					__A=0.0				,					__A=0.0				,					**__A				,					)      ->  int:
       super().__init__(**__A		)
  
       a					=num_channels
       a					=image_size
       a					=patch_size
       a					=expand_ratio
       a					=hidden_act
       a					=conv_kernel_size
       a					=output_stride
       a					=initializer_range
       a					=layer_norm_eps
       a					=n_attn_blocks
       a					=base_attn_unit_dims
       a					=width_multiplier
       a					=ffn_multiplier
       a					=ffn_dropout
       a					=attn_dropout
       a					=classifier_dropout_prob
  
       # decode head attributes for semantic segmentation
       a					=aspp_out_channels
       a					=atrous_rates
       a					=aspp_dropout_prob
       a					=semantic_loss_ignore_index
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						version.parse("1.11"    )
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]		)
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]		)
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]		)
  
  
  
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  float:
       return 1E-4