



'''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


lowercase_		       =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowercase_		       =  "cuda" if torch.cuda.is_available() else "cpu"




def lowerCAmelCase  (__A  ,	__A=100  ,	__A=" "):



     """simple docstring"""



     _a      =  text.split(__A)
     return [character.join(text[i : i + n]).strip() for i in range(0  ,	len(__A)  ,	__A)]




def lowerCAmelCase  (__A):



     """simple docstring"""



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




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



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




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	):



     """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      =  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      =  dataset.map(__A  ,	batched=__A  ,	num_proc=processing_args.num_proc)

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

     # And finally save your dataset
     _a      =  os.path.join(rag_example_args.output_dir  ,	'''my_knowledge_dataset''')
     dataset.save_to_disk(__A)
     # 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      =  faiss.IndexHNSWFlat(index_hnsw_args.d  ,	index_hnsw_args.m  ,	faiss.METRIC_INNER_PRODUCT)
     dataset.add_faiss_index('''embeddings'''  ,	custom_index=__A)

     # And save the index
     _a      =  os.path.join(rag_example_args.output_dir  ,	'''my_knowledge_dataset_hnsw_index.faiss''')
     dataset.get_index('''embeddings''').save(__A)
     # dataset.load_faiss_index("embeddings", index_path)  # to reload the index






@dataclass
class __A			:



 '''simple docstring'''







 __lowerCamelCase    :       str          =       field(
     default=str(Path(A						).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'						)   ,   metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}   ,   )
 __lowerCamelCase    :       Optional[str]          =       field(
     default=A   ,   metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}   ,   )
 __lowerCamelCase    :       str          =       field(
     default='facebook/rag-sequence-nq'   ,   metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}   ,   )
 __lowerCamelCase    :       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\''
         )
     }   ,   )
 __lowerCamelCase    :       Optional[str]          =       field(
     default=str(Path(A						).parent / 'test_run' / 'dummy-kb'						)   ,   metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}   ,   )






@dataclass
class __A			:



 '''simple docstring'''







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






@dataclass
class __A			:



 '''simple docstring'''







 __lowerCamelCase    :       int          =       field(
     default=768   ,   metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}   ,   )
 __lowerCamelCase    :       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)

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








'''simple docstring'''





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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       'glpn'



 def __init__(self ,   A=3 ,   A=4 ,   A=[2, 2, 2, 2] ,   A=[8, 4, 2, 1] ,   A=[32, 64, 160, 256] ,   A=[7, 3, 3, 3] ,   A=[4, 2, 2, 2] ,   A=[1, 2, 5, 8] ,   A=[4, 4, 4, 4] ,   A="gelu" ,   A=0.0 ,   A=0.0 ,   A=0.02 ,   A=0.1 ,   A=1E-6 ,   A=64 ,   A=10 ,   A=-1 ,   **A ,   )    ->     Any:


      """simple docstring"""



      super().__init__(**A       )

      _a      =  num_channels
      _a      =  num_encoder_blocks
      _a      =  depths
      _a      =  sr_ratios
      _a      =  hidden_sizes
      _a      =  patch_sizes
      _a      =  strides
      _a      =  mlp_ratios
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  initializer_range
      _a      =  drop_path_rate
      _a      =  layer_norm_eps
      _a      =  decoder_hidden_size
      _a      =  max_depth
      _a      =  head_in_index



