

"""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


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





def 				__UpperCamelCase   (    a__: str			)				->			CheckpointState:




					"""simple docstring"""



					print(f'''Reading saved model from {model_file}'''			)
					__lowercase												=			torch.load(a__ ,	map_location=lambda a__ ,	a__			: default_restore_location(a__ ,	'''cpu'''			)			)
					return CheckpointState(**a__			)


class 			_snake_case     :







						def __init__(self     ,							UpperCAmelCase_							)			->						List[Any]:
											__lowercase												=			src_file







						def        _UpperCamelCase    (self							)			->						List[Any]:
											raise NotImplementedError







						@staticmethod
						def        _UpperCamelCase    (UpperCAmelCase_     ,							*UpperCAmelCase_     ,							**UpperCAmelCase_							)			->						"DPRState":
											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       ):







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



class 			_snake_case     (						UpperCamelCase       ):







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



class 			_snake_case     (						UpperCamelCase       ):







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





def 				__UpperCamelCase   (    a__: str ,	a__: Path ,	a__: Path			)				->			Optional[Any]:




					"""simple docstring"""



					__lowercase												=			Path(a__			)
					dest_dir.mkdir(exist_ok=a__			)

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


if __name__ == "__main__":
							a__ :	Union[str, 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.""")
							a__ :	Any						= parser.parse_args()

							a__ :	str						= Path(args.src)
							a__ :	Union[str, Any]						= f"""converted-{src_file.name}""" if args.dest is None else args.dest
							a__ :	Tuple						= 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 unittest
import warnings

from datasets.utils import experimental





@experimental
def 				__UpperCamelCase   (    )				->			Optional[Any]:




					"""simple docstring"""



					return "success"


class 			_snake_case     (						unittest.TestCase       ):







						def        _UpperCamelCase    (self							)			->						Tuple:
											with warnings.catch_warnings(record=UpperCAmelCase_							) as w:
																warnings.simplefilter('''always'''							)
																self.assertEqual(dummy_function()     ,							'''success'''							)
											self.assertEqual(len(UpperCAmelCase_							)     ,							1							)


