"""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


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

def    _A(		lowerCAmelCase       ):
							print(F'''Reading saved model from {model_file}'''       )
							A__					:    str														= torch.load(lowerCAmelCase ,     map_location=lambda lowerCAmelCase ,     lowerCAmelCase       : default_restore_location(lowerCAmelCase ,     """cpu"""       )       )
							return CheckpointState(**lowerCAmelCase       )
class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Dict														= src_file





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										raise NotImplementedError





			@staticmethod
			def 		lowerCamelCase				( snake_case_      ,			*snake_case_      ,			**snake_case_       ):







										'''simple docstring'''


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





class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase       ):
							A__					:    Union[str, Any]														= Path(lowerCAmelCase       )
							dest_dir.mkdir(exist_ok=lowerCAmelCase       )

							A__					:    str														= DPRState.from_type(lowerCAmelCase ,     src_file=lowerCAmelCase       )
							A__					:    List[str]														= dpr_state.load_dpr_model()
							model.save_pretrained(lowerCAmelCase       )
							model.from_pretrained(lowerCAmelCase       )  # 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)






"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase         =		logging.get_logger(__name__)

_UpperCamelCase         =		{
    "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json",
    "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json",
    "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json",
    "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json",
    "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json",
    "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json",
}
class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Dict				    =							'opt'
			_UpperCamelCase     :	Optional[Any]				    =							['past_key_values']





			def __init__( self      ,			snake_case_=50_272      ,			snake_case_=768      ,			snake_case_=12      ,			snake_case_=3_072      ,			snake_case_=2_048      ,			snake_case_=True      ,			snake_case_=False      ,			snake_case_=None      ,			snake_case_=0.1      ,			snake_case_=0.0      ,			snake_case_=12      ,			snake_case_="relu"      ,			snake_case_=0.0      ,			snake_case_=0.02      ,			snake_case_=True      ,			snake_case_=1      ,			snake_case_=2      ,			snake_case_=2      ,			snake_case_=True      ,			snake_case_=True      ,			**snake_case_      ,			):







										'''simple docstring'''


										super().__init__(
										    pad_token_id=snake_case_      ,			bos_token_id=snake_case_      ,			eos_token_id=snake_case_      ,			**snake_case_      ,			)
										A__					:    int														= vocab_size
										A__					:    List[str]														= max_position_embeddings
										A__					:    str														= num_attention_heads
										A__					:    Dict														= word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
										A__					:    Optional[int]														= ffn_dim
										A__					:    Optional[int]														= hidden_size
										A__					:    int														= num_hidden_layers
										A__					:    str														= dropout
										A__					:    Tuple														= attention_dropout
										A__					:    Optional[Any]														= activation_function
										A__					:    Any														= init_std
										A__					:    Optional[int]														= layerdrop
										A__					:    Optional[int]														= use_cache
										A__					:    int														= do_layer_norm_before
										# We keep these variables at `True` for backward compatibility.
										A__					:    str														= enable_bias
										A__					:    List[str]														= layer_norm_elementwise_affine

										# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
										# with checkpoints that have been fine-tuned before transformers v4.20.1
										# see https://github.com/facebookresearch/metaseq/pull/164
										A__					:    str														= _remove_final_layer_norm





