






"""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__  (  UpperCamelCase__       ):



  '''simple docstring'''
  print(F"""Reading saved model from {model_file}"""       )
  _a						: Optional[int]    =			torch.load(UpperCamelCase__  ,     map_location=lambda UpperCamelCase__  ,     UpperCamelCase__       : default_restore_location(UpperCamelCase__  ,     """cpu"""       )       )
  return CheckpointState(**UpperCamelCase__       )






class  UpperCamelCase    :


 def __init__(  self	:		Optional[Any]							,		UpperCAmelCase__	:		Path							)      -> Optional[Any]:
   _a						: int    =			src_file


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







 @staticmethod
 def _lowercase			(  UpperCAmelCase__	:		str							,		*UpperCAmelCase__	:		str							,		**UpperCAmelCase__	:		int							)      -> "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  UpperCamelCase    (			snake_case_   ):


 def _lowercase			(  self	:		Optional[Any]							)      -> Dict:
   _a						: Tuple    =			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						: 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(UpperCAmelCase__							):
       _a						: Dict    =			key[len(UpperCAmelCase__							) :]
       if not key.startswith("""encode_proj."""							):
         _a						: Optional[Any]    =			"""bert_model.""" + key
       _a						: Any    =			value
   encoder.load_state_dict(UpperCAmelCase__							)
   return model







class  UpperCamelCase    (			snake_case_   ):


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







class  UpperCamelCase    (			snake_case_   ):


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







def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: int    =			Path(UpperCamelCase__       )
  dest_dir.mkdir(exist_ok=UpperCamelCase__       )

  _a						: List[Any]    =			DPRState.from_type(UpperCamelCase__  ,     src_file=UpperCamelCase__       )
  _a						: Tuple    =			dpr_state.load_dpr_model()
  model.save_pretrained(UpperCamelCase__       )
  model.from_pretrained(UpperCamelCase__       )  # 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 logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import (
    DPRContextEncoder,
    DPRContextEncoderTokenizerFast,
    HfArgumentParser,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


_snake_case					   =     logging.getLogger(__name__)
torch.set_grad_enabled(False)
_snake_case					   =     'cuda' if torch.cuda.is_available() else 'cpu'

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__=1_0_0  ,     UpperCamelCase__=" "       ):



  '''simple docstring'''
  _a						: str    =			text.split(UpperCamelCase__       )
  return [character.join(text[i : i + n]       ).strip() for i in range(0  ,     len(UpperCamelCase__       )  ,     UpperCamelCase__       )]

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a   ,						_a						: Optional[int]    =			[], []
  for title, text in zip(documents["""title"""]  ,     documents["""text"""]       ):
    if text is not None:
      for passage in split_text(UpperCamelCase__       ):
        titles.append(title if title is not None else """"""       )
        texts.append(UpperCamelCase__       )
  return {"title": titles, "text": texts}

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[str]    =			ctx_tokenizer(
      documents["""title"""]  ,     documents["""text"""]  ,     truncation=UpperCamelCase__  ,     padding="""longest"""  ,     return_tensors="""pt"""       )["""input_ids"""]
  _a						: Optional[int]    =			ctx_encoder(input_ids.to(device=UpperCamelCase__       )  ,     return_dict=UpperCamelCase__       ).pooler_output
  return {"embeddings": embeddings.detach().cpu().numpy()}

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     ):



  '''simple docstring'''
  ######################################
  logger.info("""Step 1 - Create the dataset"""       )
  ######################################

  # The dataset needed for RAG must have three columns:
  # - title (string): title of the document
  # - text (string): text of a passage of the document
  # - embeddings (array of dimension d): DPR representation of the passage

  # Let's say you have documents in tab-separated csv files with columns "title" and "text"
  assert os.path.isfile(rag_example_args.csv_path       ), "Please provide a valid path to a csv file"

  # You can load a Dataset object this way
  _a						: Dict    =			load_dataset(
      """csv"""  ,     data_files=[rag_example_args.csv_path]  ,     split="""train"""  ,     delimiter="""\t"""  ,     column_names=["""title""", """text"""]       )

  # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

  # Then split the documents into passages of 100 words
  _a						: Any    =			dataset.map(UpperCamelCase__  ,     batched=UpperCamelCase__  ,     num_proc=processing_args.num_proc       )

  # And compute the embeddings
  _a						: Optional[Any]    =			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       ).to(device=UpperCamelCase__       )
  _a						: str    =			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       )
  _a						: int    =			Features(
      {"""text""": Value("""string"""       ), """title""": Value("""string"""       ), """embeddings""": Sequence(Value("""float32"""       )       )}       )  # optional, save as float32 instead of float64 to save space
  _a						: str    =			dataset.map(
      partial(UpperCamelCase__  ,     ctx_encoder=UpperCamelCase__  ,     ctx_tokenizer=UpperCamelCase__       )  ,     batched=UpperCamelCase__  ,     batch_size=processing_args.batch_size  ,     features=UpperCamelCase__  ,     )

  # And finally save your dataset
  _a						: List[Any]    =			os.path.join(rag_example_args.output_dir  ,     """my_knowledge_dataset"""       )
  dataset.save_to_disk(UpperCamelCase__       )
  # from datasets import load_from_disk
  # dataset = load_from_disk(passages_path)  # to reload the dataset

  ######################################
  logger.info("""Step 2 - Index the dataset"""       )
  ######################################

  # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
  _a						: List[str]    =			faiss.IndexHNSWFlat(index_hnsw_args.d  ,     index_hnsw_args.m  ,     faiss.METRIC_INNER_PRODUCT       )
  dataset.add_faiss_index("""embeddings"""  ,     custom_index=UpperCamelCase__       )

  # And save the index
  _a						: List[str]    =			os.path.join(rag_example_args.output_dir  ,     """my_knowledge_dataset_hnsw_index.faiss"""       )
  dataset.get_index("""embeddings"""       ).save(UpperCamelCase__       )
  # dataset.load_faiss_index("embeddings", index_path)  # to reload the index

  ######################################
  logger.info("""Step 3 - Load RAG"""       )
  ######################################

  # Easy way to load the model
  _a						: str    =			RagRetriever.from_pretrained(
      rag_example_args.rag_model_name  ,     index_name="""custom"""  ,     indexed_dataset=UpperCamelCase__       )
  _a						: Any    =			RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name  ,     retriever=UpperCamelCase__       )
  _a						: List[Any]    =			RagTokenizer.from_pretrained(rag_example_args.rag_model_name       )

  # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
  # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

  ######################################
  logger.info("""Step 4 - Have fun"""       )
  ######################################

  _a						: Optional[Any]    =			rag_example_args.question or """What does Moses' rod turn into ?"""
  _a						: int    =			tokenizer.question_encoder(UpperCamelCase__  ,     return_tensors="""pt"""       )["""input_ids"""]
  _a						: Union[str, Any]    =			model.generate(UpperCamelCase__       )
  _a						: Any    =			tokenizer.batch_decode(UpperCamelCase__  ,     skip_special_tokens=UpperCamelCase__       )[0]
  logger.info("""Q: """ + question       )
  logger.info("""A: """ + generated_string       )






@dataclass
class  UpperCamelCase    :
 UpperCamelCase      : str    =      field(
     default=str(Path(snake_case_   ).parent / '''test_data''' / '''my_knowledge_dataset.csv'''   )	,  metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}	,  )
 UpperCamelCase      : Optional[str]    =      field(
     default=snake_case_	,  metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}	,  )
 UpperCamelCase      : str    =      field(
     default='''facebook/rag-sequence-nq'''	,  metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}	,  )
 UpperCamelCase      : str    =      field(
     default='''facebook/dpr-ctx_encoder-multiset-base'''	,  metadata={
         '''help''': (
             '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
             ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
         )
     }	,  )
 UpperCamelCase      : Optional[str]    =      field(
     default=snake_case_	,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}	,  )







@dataclass
class  UpperCamelCase    :
 UpperCamelCase      : Optional[int]    =      field(
     default=snake_case_	,  metadata={
         '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
     }	,  )
 UpperCamelCase      : int    =      field(
     default=16	,  metadata={
         '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
     }	,  )







@dataclass
class  UpperCamelCase    :
 UpperCamelCase      : int    =      field(
     default=768	,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}	,  )
 UpperCamelCase      : int    =      field(
     default=128	,  metadata={
         '''help''': (
             '''The number of bi-directional links created for every new element during the HNSW index construction.'''
         )
     }	,  )


if __name__ == "__main__":
 logging.basicConfig(level=logging.WARNING)
 logger.setLevel(logging.INFO)

 _snake_case					   =     HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 _snake_case   ,				_snake_case   ,				_snake_case					   =     parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  _snake_case					   =     rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)

