





'''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


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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      str )			->						Union[str, Any]:
						print(F"""Reading saved model from {model_file}""" )
						UpperCAmelCase_      :   Dict      =	torch.load(_a,      map_location=lambda SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ : default_restore_location(_a,      '''cpu''' ) )
						return CheckpointState(**_a )


class        __a   :

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







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	src_file

							def      UpperCAmelCase__						(				self				:  Tuple )       ->							int:







													"""simple docstring"""
													raise NotImplementedError



							@staticmethod
							def      UpperCAmelCase__						(				__magic_name__				:  Dict							,  *__magic_name__				:  Optional[Any]							,  **__magic_name__				:  Optional[Any] )       ->							List[str]:







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





class        __a   (_lowerCamelCase  ):

							def      UpperCAmelCase__						(				self				:  int )       ->							Dict:







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





class        __a   (_lowerCamelCase  ):

							def      UpperCAmelCase__						(				self				:  Dict )       ->							List[Any]:







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





class        __a   (_lowerCamelCase  ):

							def      UpperCAmelCase__						(				self				:  List[str] )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased''' )[0] ) )
													print(F"""Loading DPR reader from {self.src_file}""" )
													UpperCAmelCase_      :   Optional[int]      =	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_      :   Tuple      =	'encoder.bert_model.' + key[len('''encoder.''' ) :]
																			UpperCAmelCase_      :   List[Any]      =	value
													model.span_predictor.load_state_dict(_lowerCamelCase )
													return model





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      str,      SCREAMING_SNAKE_CASE__   :      Path,      SCREAMING_SNAKE_CASE__   :      Path )			->						List[Any]:
						UpperCAmelCase_      :   List[str]      =	Path(_a )
						dest_dir.mkdir(exist_ok=_a )

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


if __name__ == "__main__":
					snake_case_  :							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.")
					snake_case_  :							List[Any]            =	parser.parse_args()

					snake_case_  :							List[str]            =	Path(args.src)
					snake_case_  :							Union[str, Any]            =	f'''converted-{src_file.name}''' if args.dest is None else args.dest
					snake_case_  :							Union[str, 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'''
import numpy as np







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						Any					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    __UpperCamelCase						:						Optional[Any]		     =	np.floor(_a      )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    __UpperCamelCase						:						Tuple		     =	int(n_draws - floored.sum()      )
    if need_to_add > 0:
        __UpperCamelCase						:						Any		     =	continuous - floored
        __UpperCamelCase						:						Any		     =	np.sort(np.unique(_a      )      )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((__UpperCamelCase)     ,       )						:						Any		     =	np.where(remainder == value      )
            # if we need_to_add less than what's in inds
            # we draw randomly from them.
            # if we need to add more, we add them all and
            # go to the next value
            __UpperCamelCase						:						List[Any]		     =	min(len(_a      )					,  _a      )
            __UpperCamelCase						:						Tuple		     =	rng.choice(_a					,  size=_a					,  replace=_a      )
            floored[inds] += 1
            need_to_add -= add_now
            if need_to_add == 0:
                break
    return floored.astype(np.intaa      )







def 			_UpperCamelCase			(  _a						:						Optional[int]					,  _a						:						Union[str, Any]					,  _a						:						int					,  _a						:						Union[str, Any]					,  _a						:						str=1_0      ):




    """simple docstring"""



    __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	np.unique(_a					,  return_inverse=_a      )
    __UpperCamelCase						:						str		     =	classes.shape[0]
    __UpperCamelCase						:						str		     =	np.bincount(_a      )
    if np.min(_a      ) < 2:
        raise ValueError('Minimum class count error'      )
    if n_train < n_classes:
        raise ValueError(
            'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
    if n_test < n_classes:
        raise ValueError(
            'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
    __UpperCamelCase						:						Optional[int]		     =	np.split(np.argsort(_a					,  kind='mergesort'      )					,  np.cumsum(_a      )[:-1]      )
    for _ in range(_a      ):
        __UpperCamelCase						:						List[str]		     =	approximate_mode(_a					,  _a					,  _a      )
        __UpperCamelCase						:						Tuple		     =	class_counts - n_i
        __UpperCamelCase						:						int		     =	approximate_mode(_a					,  _a					,  _a      )

        __UpperCamelCase						:						List[Any]		     =	[]
        __UpperCamelCase						:						str		     =	[]

        for i in range(_a      ):
            __UpperCamelCase						:						Optional[Any]		     =	rng.permutation(class_counts[i]      )
            __UpperCamelCase						:						List[Any]		     =	class_indices[i].take(_a					,  mode='clip'      )
            train.extend(perm_indices_class_i[: n_i[i]]      )
            test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )
        __UpperCamelCase						:						int		     =	rng.permutation(_a      )

        yield train, test



