



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




def        snake_case_   (	lowerCAmelCase_						)->						CheckpointState:





       '''simple docstring'''


       print(F'''Reading saved model from {model_file}'''						)
       _UpperCAmelCase    :		Tuple     	=					torch.load(lowerCAmelCase_			,  map_location=lambda lowerCAmelCase_			,  lowerCAmelCase_						: default_restore_location(lowerCAmelCase_			,  """cpu"""						)						)
       return CheckpointState(**lowerCAmelCase_						)



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_				)						->  str:
        _UpperCAmelCase    :		Dict     	=					src_file







 def _snake_case					(				self				)						->  str:
        raise NotImplementedError







 @staticmethod
 def _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 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


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







class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


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







class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


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







def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						str:





       '''simple docstring'''


       _UpperCAmelCase    :		List[Any]     	=					Path(lowerCAmelCase_						)
       dest_dir.mkdir(exist_ok=lowerCAmelCase_						)

       _UpperCAmelCase    :		int     	=					DPRState.from_type(lowerCAmelCase_			,  src_file=lowerCAmelCase_						)
       _UpperCAmelCase    :		str     	=					dpr_state.load_dpr_model()
       model.save_pretrained(lowerCAmelCase_						)
       model.from_pretrained(lowerCAmelCase_						)  # 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_				:					Union[str, Any]							=      parser.parse_args()

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



import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


_snake_case							:   Tuple	  =       logging.get_logger(__name__)




class 							__lowercase(							_UpperCamelCase	):






						def __init__(       self     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)      ->	None:




											"""simple docstring"""





											warnings.warn(
											    'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
											    ' use ViTImageProcessor instead.'     ,    _SCREAMING_SNAKE_CASE     ,    )
											super().__init__(*_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE)