


'''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


__lowercase  :      Optional[Any]      		=				collections.namedtuple(
    """CheckpointState""", ["""model_dict""", """optimizer_dict""", """scheduler_dict""", """offset""", """epoch""", """encoder_params"""]
)





def 							a_       (						lowercase    )			->CheckpointState:






    '''simple docstring'''

    print(F'Reading saved model from {model_file}'    )
    __lowerCamelCase		:      Optional[int]											=					torch.load(snake_case_							,						map_location=lambda lowercase							,						lowercase    : default_restore_location(snake_case_							,						"cpu"    )    )
    return CheckpointState(**snake_case_    )





class _snake_case   :
       """simple docstring"""





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



           """simple docstring"""







           __lowerCamelCase		:      Dict											=					src_file







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



           """simple docstring"""







           raise NotImplementedError







       @staticmethod
       def 		__SCREAMING_SNAKE_CASE(   __A			,				*__A			,				**__A	)     ->"DPRState":



           """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 _snake_case   ( UpperCamelCase_   ):
       """simple docstring"""





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



           """simple docstring"""







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





class _snake_case   ( UpperCamelCase_   ):
       """simple docstring"""





       def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



           """simple docstring"""







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





class _snake_case   ( UpperCamelCase_   ):
       """simple docstring"""





       def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:



           """simple docstring"""







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





def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Optional[int]:






    '''simple docstring'''

    __lowerCamelCase		:      Optional[int]											=					Path(snake_case_    )
    dest_dir.mkdir(exist_ok=snake_case_    )

    __lowerCamelCase		:      Tuple											=					DPRState.from_type(snake_case_							,						src_file=snake_case_    )
    __lowerCamelCase		:      str											=					dpr_state.load_dpr_model()
    model.save_pretrained(snake_case_    )
    model.from_pretrained(snake_case_    )  # sanity check


if __name__ == "__main__":
    __lowercase  :      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.""")
    __lowercase  :      Tuple      		=				parser.parse_args()

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









'''simple docstring'''




# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowercase  :      Tuple      		=				abspath(join(dirname(dirname(dirname(__file__))), """src"""))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)





def 							a_       (						lowercase    )			->Any:






				'''simple docstring'''

				from diffusers.utils.testing_utils import pytest_addoption_shared

				pytest_addoption_shared(lowercase    )





def 							a_       (						lowercase    )			->str:






				'''simple docstring'''

				from diffusers.utils.testing_utils import pytest_terminal_summary_main

				__lowerCamelCase		:      Union[str, Any]											=					terminalreporter.config.getoption("--make-reports"    )
				if make_reports:
								pytest_terminal_summary_main(lowercase							,						id=lowercase    )





