




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


UpperCamelCase__						=		collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)






def    snake_case   (				A     )				->				CheckpointState:




	'''simple docstring'''


	print(f"Reading saved model from {model_file}"     )
	lowerCamelCase_				:						List[str]				  =				torch.load(A		,  map_location=lambda A		,  A     : default_restore_location(A		,  'cpu'     )     )
	return CheckpointState(**A     )


class 							__SCREAMING_SNAKE_CASE				:

					def __init__(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						str				  =				src_file

					def       lowercase_				(							self   ):







						'''simple docstring'''




						raise NotImplementedError





					@staticmethod
					def       lowercase_				(							__lowercase					,			*__lowercase					,			**__lowercase   ):







						'''simple docstring'''




						if comp_type.startswith('c'   ):
							return DPRContextEncoderState(*__lowercase					,			**__lowercase   )
						if comp_type.startswith('q'   ):
							return DPRQuestionEncoderState(*__lowercase					,			**__lowercase   )
						if comp_type.startswith('r'   ):
							return DPRReaderState(*__lowercase					,			**__lowercase   )
						else:
							raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'   )


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'   )[0]   )   )
						print(f"Loading DPR biencoder from {self.src_file}"   )
						lowerCamelCase_				:						List[Any]				  =				load_states_from_checkpoint(self.src_file   )
						lowerCamelCase_  ,     lowerCamelCase_				:						Tuple				  =				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(__lowercase   ):
								lowerCamelCase_				:						Tuple				  =				key[len(__lowercase   ) :]
								if not key.startswith('encode_proj.'   ):
									lowerCamelCase_				:						Any				  =				'bert_model.' + key
								lowerCamelCase_				:						List[str]				  =				value
						encoder.load_state_dict(__lowercase   )
						return model


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						List[Any]				  =				DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'   )[0]   )   )
						print(f"Loading DPR biencoder from {self.src_file}"   )
						lowerCamelCase_				:						str				  =				load_states_from_checkpoint(self.src_file   )
						lowerCamelCase_  ,     lowerCamelCase_				:						str				  =				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(__lowercase   ):
								lowerCamelCase_				:						str				  =				key[len(__lowercase   ) :]
								if not key.startswith('encode_proj.'   ):
									lowerCamelCase_				:						Optional[int]				  =				'bert_model.' + key
								lowerCamelCase_				:						str				  =				value
						encoder.load_state_dict(__lowercase   )
						return model


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					def       lowercase_				(							self   ):







						'''simple docstring'''




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






def    snake_case   (				A		,  A		,  A     )				->				int:




	'''simple docstring'''


	lowerCamelCase_				:						List[Any]				  =				Path(A     )
	dest_dir.mkdir(exist_ok=A     )

	lowerCamelCase_				:						Optional[Any]				  =				DPRState.from_type(A		,  src_file=A     )
	lowerCamelCase_				:						List[Any]				  =				dpr_state.load_dpr_model()
	model.save_pretrained(A     )
	model.from_pretrained(A     )  # sanity check


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

							UpperCamelCase__						=		Path(args.src)
							UpperCamelCase__						=		F'converted-{src_file.name}' if args.dest is None else args.dest
							UpperCamelCase__						=		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)






class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Optional[Any]			,							snake_case       :			int					):
          __UpperCamelCase			=    data
          __UpperCamelCase			=    42
          __UpperCamelCase			=    42







def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						None:

    """simple docstring"""


    __UpperCamelCase			=    0
    __UpperCamelCase			=    x




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						None:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    find_set(lowercase_   ), find_set(lowercase_   )
    if x == y:
        return

    elif x.rank > y.rank:
        __UpperCamelCase			=    x
    else:
        __UpperCamelCase			=    y
        if x.rank == y.rank:
            y.rank += 1




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Node:

    """simple docstring"""


    if x != x.parent:
        __UpperCamelCase			=    find_set(x.parent   )
    return x.parent




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						set:

    """simple docstring"""


    __UpperCamelCase			=    ({0, 1, 2}, {3, 4, 5})
    for s in sets:
        if node.data in s:
            return s
    __UpperCamelCase			=    F"{node.data} is not in {sets}"
    raise ValueError(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	)							->						None:

    """simple docstring"""


    __UpperCamelCase			=    [Node(lowercase_   ) for i in range(6   )]
    for v in vertex:
        make_set(lowercase_   )

    union_set(vertex[0]       ,  vertex[1]   )
    union_set(vertex[1]       ,  vertex[2]   )
    union_set(vertex[3]       ,  vertex[4]   )
    union_set(vertex[3]       ,  vertex[5]   )

    for nodea in vertex:
        for nodea in vertex:
            if find_python_set(lowercase_   ).isdisjoint(find_python_set(lowercase_   )   ):
                assert find_set(lowercase_   ) != find_set(lowercase_   )
            else:
                assert find_set(lowercase_   ) == find_set(lowercase_   )


if __name__ == "__main__":
 test_disjoint_set()






