



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


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

def 	a						(				_UpperCAmelCase							: str						):







		'''simple docstring'''





		print(f'Reading saved model from {model_file}'						)
		__UpperCAmelCase   :					int													=     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			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     src_file




			def 			snake_case__					(       self							:			int			):
					'''simple docstring'''





					raise NotImplementedError




			@staticmethod
			def 			snake_case__					(       a_							:			str	,	*a_							:			Union[str, Any]	,	**a_							:			Union[str, Any]			):
					'''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__      (      __UpperCamelCase       ):




			'''simple docstring'''






			def 			snake_case__					(       self							:			List[str]			):
					'''simple docstring'''





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

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





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

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





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

def 	a						(				_UpperCAmelCase							: str			,       _UpperCAmelCase							: Path			,       _UpperCAmelCase							: Path						):







		'''simple docstring'''





		__UpperCAmelCase   :					Optional[int]													=     Path(_UpperCAmelCase						)
		dest_dir.mkdir(exist_ok=_UpperCAmelCase						)

		__UpperCAmelCase   :					List[str]													=     DPRState.from_type(_UpperCAmelCase			,       src_file=_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     dpr_state.load_dpr_model()
		model.save_pretrained(_UpperCAmelCase						)
		model.from_pretrained(_UpperCAmelCase						)  # sanity check


if __name__ == "__main__":
				__A             =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.")
				__A             =parser.parse_args()

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

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

__A             =TypeVar("T", bound=bool)

class 		UpperCAmelCase__      (      Generic[T]       ):




			'''simple docstring'''






			def __init__(       self							:			List[Any]	,	a_							:			T			):
					'''simple docstring'''





					__UpperCAmelCase   :					T													=     value
					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None
					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None




			@property
			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					return self._value




			@staticmethod
			def 			snake_case__					(       a_							:			RandomizedHeapNode[T] | None	,	a_							:			RandomizedHeapNode[T] | None			):
					'''simple docstring'''





					if not roota:
							return roota

					if not roota:
							return roota

					if roota.value > roota.value:
							__UpperCAmelCase							, __UpperCAmelCase   :					int													=     roota, roota

					if random.choice([True, False]			):
							__UpperCAmelCase							, __UpperCAmelCase   :					Union[str, Any]													=     roota.right, roota.left

					__UpperCAmelCase   :					Optional[Any]													=     RandomizedHeapNode.merge(roota.left	,	a_			)

					return roota

class 		UpperCAmelCase__      (      Generic[T]       ):




			'''simple docstring'''






			def __init__(       self							:			Tuple	,	a_							:			Iterable[T] | None = ()			):
					'''simple docstring'''





					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None

					if data:
							for item in data:
									self.insert(a_			)




			def 			snake_case__					(       self							:			List[str]	,	a_							:			T			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     RandomizedHeapNode.merge(self._root	,	RandomizedHeapNode(a_			)			)




			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     self.top()

					if self._root is None:
							return None

					__UpperCAmelCase   :					Tuple													=     RandomizedHeapNode.merge(self._root.left	,	self._root.right			)

					return result




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					if not self._root:
							raise IndexError('''Can\'t get top element for the empty heap.'''			)
					return self._root.value




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					str													=     None




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     []
					while self:
							result.append(self.pop()			)

					return result




			def __bool__(       self							:			List[Any]			):
					'''simple docstring'''





					return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





