




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


SCREAMING_SNAKE_CASE_    :       Any       =							collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    print(F"""Reading saved model from {model_file}"""		)
    a__  			=							torch.load(UpperCAmelCase_ ,     map_location=lambda UpperCAmelCase_ ,     UpperCAmelCase_		: default_restore_location(UpperCAmelCase_ ,     """cpu"""		)		)
    return CheckpointState(**UpperCAmelCase_		)







class 		__UpperCAmelCase:





     """simple docstring"""



     def __init__(   self			:							str    ,					A			:							Path     )      ->      Any:






         """simple docstring"""
         a__  			=							src_file

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      List[Any]:






         """simple docstring"""
         raise NotImplementedError






     @staticmethod
     def 					__SCREAMING_SNAKE_CASE		(   A			:							str    ,					*A			:							Optional[Any]    ,					**A			:							List[Any]     )      ->      "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 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]     )      ->      Union[str, Any]:






         """simple docstring"""
         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 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Optional[Any]:






         """simple docstring"""
         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 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      int:






         """simple docstring"""
         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 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_		):
    a__  			=							Path(UpperCAmelCase_		)
    dest_dir.mkdir(exist_ok=UpperCAmelCase_		)

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_    :       List[str]       =							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.''')
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							parser.parse_args()

 SCREAMING_SNAKE_CASE_    :       Any       =							Path(args.src)
 SCREAMING_SNAKE_CASE_    :       Union[str, Any]       =							F"converted-{src_file.name}" if args.dest is None else args.dest
 SCREAMING_SNAKE_CASE_    :       Any       =							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)












from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_    :       List[str]       =							TypeVar('''T''')







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     pass







class 		__UpperCAmelCase(		lowercase__			):





     """simple docstring"""



     pass







class 		__UpperCAmelCase(		Generic[T]			):





     """simple docstring"""



     def __init__(   self			:							str    ,					A			:							int = 10     )      ->      str:






         """simple docstring"""
         a__  			=							[]
         a__  			=							limit

     def __bool__(   self			:							Dict     )      ->      bool:






         """simple docstring"""
         return bool(self.stack     )

     def __str__(   self			:							str     )      ->      str:






         """simple docstring"""
         return str(self.stack     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[str]    ,					A			:							T     )      ->      None:






         """simple docstring"""
         if len(self.stack     ) >= self.limit:
             raise StackOverflowError
         self.stack.append(A     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      T:






         """simple docstring"""
         if not self.stack:
             raise StackUnderflowError
         return self.stack.pop()

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      T:






         """simple docstring"""
         if not self.stack:
             raise StackUnderflowError
         return self.stack[-1]

     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      bool:






         """simple docstring"""
         return not bool(self.stack     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      bool:






         """simple docstring"""
         return self.size() == self.limit

     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      int:






         """simple docstring"""
         return len(self.stack     )






     def __contains__(   self			:							int    ,					A			:							T     )      ->      bool:






         """simple docstring"""
         return item in self.stack






def 							__lowerCAmelCase			():
    a__  			=							Stack(10		)
    assert bool(UpperCAmelCase_		) is False
    assert stack.is_empty() is True
    assert stack.is_full() is False
    assert str(UpperCAmelCase_		) == "[]"

    try:
        a__  			=							stack.pop()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    try:
        a__  			=							stack.peek()
        raise AssertionError  # This should not happen
    except StackUnderflowError:
        assert True  # This should happen

    for i in range(10		):
        assert stack.size() == i
        stack.push(UpperCAmelCase_		)

    assert bool(UpperCAmelCase_		)
    assert not stack.is_empty()
    assert stack.is_full()
    assert str(UpperCAmelCase_		) == str(list(range(10		)		)		)
    assert stack.pop() == 9
    assert stack.peek() == 8

    stack.push(100		)
    assert str(UpperCAmelCase_		) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]		)

    try:
        stack.push(200		)
        raise AssertionError  # This should not happen
    except StackOverflowError:
        assert True  # This should happen

    assert not stack.is_empty()
    assert stack.size() == 10

    assert 5 in stack
    assert 55 not in stack


if __name__ == "__main__":
 test_stack()






