



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

def 				_lowerCAmelCase   (     lowercase_		):
						print(F"""Reading saved model from {model_file}"""		)
						UpperCAmelCase    =				torch.load(lowercase_	,			map_location=lambda lowercase_	,			lowercase_		: default_restore_location(lowercase_	,			'cpu'		)		)
						return CheckpointState(**lowercase_		)







class    A_    :



						"""simple docstring"""






						def __init__(      self				:Optional[Any]     ,						lowercase_				:Path      )       ->    Union[str, Any]:
												UpperCAmelCase    =				src_file






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






						@staticmethod
						def 			UpperCAmelCase__  (      lowercase_				:str     ,						*lowercase_				:str     ,						**lowercase_				:int      )       ->    "DPRState":
												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    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""






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







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""






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







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""






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


def 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_		):
						UpperCAmelCase    =				Path(lowercase_		)
						dest_dir.mkdir(exist_ok=lowercase_		)

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


if __name__ == "__main__":
				snake_case_         = 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_         = parser.parse_args()

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

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class    A_    (  nn.Linear       ):



						"""simple docstring"""






						def __init__(      self				:List[str]     ,						lowercase_				:int     ,						lowercase_				:int     ,						lowercase_				:bool = True     ,						lowercase_				:str = "constant"     ,						lowercase_				:float = 0.0     ,						lowercase_				:str = "topK"     ,						)       ->    Union[str, Any]:
												super(lowercase_     ,						self      ).__init__(in_features=lowercase_     ,						out_features=lowercase_     ,						bias=lowercase_      )
												assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
												UpperCAmelCase    =				pruning_method

												if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
																		UpperCAmelCase    =				mask_scale
																		UpperCAmelCase    =				mask_init
																		UpperCAmelCase    =				nn.Parameter(torch.empty(self.weight.size()      )      )
																		self.init_mask()






						def 			UpperCAmelCase__  (      self				:Optional[Any]      )       ->    Dict:
												if self.mask_init == "constant":
																		init.constant_(self.mask_scores     ,						val=self.mask_scale      )
												elif self.mask_init == "uniform":
																		init.uniform_(self.mask_scores     ,						a=-self.mask_scale     ,						b=self.mask_scale      )
												elif self.mask_init == "kaiming":
																		init.kaiming_uniform_(self.mask_scores     ,						a=math.sqrt(5      )      )






						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:torch.tensor     ,						lowercase_				:float      )       ->    List[str]:
												# Get the mask
												if self.pruning_method == "topK":
																		UpperCAmelCase    =				TopKBinarizer.apply(self.mask_scores     ,						lowercase_      )
												elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																		UpperCAmelCase    =				'sigmoied' in self.pruning_method
																		UpperCAmelCase    =				ThresholdBinarizer.apply(self.mask_scores     ,						lowercase_     ,						lowercase_      )
												elif self.pruning_method == "magnitude":
																		UpperCAmelCase    =				MagnitudeBinarizer.apply(self.weight     ,						lowercase_      )
												elif self.pruning_method == "l0":
																		UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase    =				-0.1, 1.1, 2 / 3
																		if self.training:
																								UpperCAmelCase    =				torch.zeros_like(self.mask_scores      ).uniform_().clamp(0.0001     ,						0.9999      )
																								UpperCAmelCase    =				torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b      )
																		else:
																								UpperCAmelCase    =				torch.sigmoid(self.mask_scores      )
																		UpperCAmelCase    =				s * (r - l) + l
																		UpperCAmelCase    =				s_bar.clamp(min=0.0     ,						max=1.0      )
												# Mask weights with computed mask
												UpperCAmelCase    =				mask * self.weight
												# Compute output (linear layer) with masked weights
												return nn.functional.linear(lowercase_     ,						lowercase_     ,						self.bias      )





