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


snake_case__						      =			collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)
def    __magic_name__(	__UpperCAmelCase		)    ->     CheckpointState:



      '''simple docstring'''

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



class 			UpperCamelCase					:

     '''simple docstring'''

     def __init__(       self					,					A_    )			->					Optional[Any]:






           """simple docstring"""
           _lowerCamelCase           =					src_file



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






           """simple docstring"""
           raise NotImplementedError





     @staticmethod
     def 		UpperCamelCase_    (       A_					,					*A_					,					**A_    )			->					"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					(			__lowercase			):

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					Optional[Any]:






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





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					str:






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





class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

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






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





def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase           =					Path(__UpperCAmelCase		)
      dest_dir.mkdir(exist_ok=__UpperCAmelCase		)

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


if __name__ == "__main__":
     snake_case__						      =			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.')
     snake_case__						      =			parser.parse_args()

     snake_case__						      =			Path(args.src)
     snake_case__						      =			f'''converted-{src_file.name}''' if args.dest is None else args.dest
     snake_case__						      =			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)
import torch

from accelerate import Accelerator, DistributedType



class 			UpperCamelCase					:

     '''simple docstring'''

     def __enter__(       self    )			->					int:






           """simple docstring"""
           if self.enabled:
                 _lowerCamelCase           =					self.model.no_sync()
                 self.model_sync_obj.__enter__()

           return self



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






           """simple docstring"""
           if self.enabled:
                 # Average all models on exit
                 self._sync_and_avg_model_params()
                 self.model_sync_obj.__exit__(A_					,					A_					,					A_    )



     def __init__(       self					,					A_					,					A_					,					A_					,					A_ = True    )			->					List[str]:






           """simple docstring"""
           if accelerator.distributed_type not in [
               DistributedType.NO,
               DistributedType.MULTI_CPU,
               DistributedType.MULTI_GPU,
           ]:
                 raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''    )
           _lowerCamelCase           =					enabled and accelerator.distributed_type != DistributedType.NO
           _lowerCamelCase           =					0
           if self.enabled:
                 _lowerCamelCase           =					accelerator
                 _lowerCamelCase           =					model
                 _lowerCamelCase           =					local_sgd_steps



     def 		UpperCamelCase_    (       self    )			->					Optional[Any]:






           """simple docstring"""
           self.num_steps += 1
           if not self.enabled:
                 return

           if self.num_steps % self.local_sgd_steps == 0:
                 self._sync_and_avg_model_params()





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






           """simple docstring"""
           self.accelerator.wait_for_everyone()
           with self.accelerator.autocast():
                 for param in self.model.parameters():
                       _lowerCamelCase           =					self.accelerator.reduce(param.data					,					reduction='''mean'''    )