



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


a__      :				Optional[int]   	=			logging.getLogger(__name__)
torch.set_grad_enabled(False)
a__      :				Tuple   	=			"cuda" if torch.cuda.is_available() else "cpu"

def   _lowerCAmelCase   (						A__						,  A__=100						,  A__=" "	):
      lowercase__     =  text.split(A__	)
      return [character.join(text[i : i + n]	).strip() for i in range(0						,  len(A__	)						,  A__	)]

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

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  ):
      ######################################
      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
      lowercase__     =  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
      lowercase__     =  dataset.map(A__						,  batched=A__						,  num_proc=processing_args.num_proc	)

      # And compute the embeddings
      lowercase__     =  DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	).to(device=A__	)
      lowercase__     =  DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	)
      lowercase__     =  Features(
          {'text': Value('string'	), 'title': Value('string'	), 'embeddings': Sequence(Value('float32'	)	)}	)  # optional, save as float32 instead of float64 to save space
      lowercase__     =  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
      lowercase__     =  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
      lowercase__     =  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
      lowercase__     =  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       UpperCAmelCase__:

   '''simple docstring'''




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



@dataclass
class       UpperCAmelCase__:

   '''simple docstring'''




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



@dataclass
class       UpperCAmelCase__:

   '''simple docstring'''




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

       a__      :				Optional[int]   	=			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
       a__	,		a__	,		a__      :				Optional[int]   	=			parser.parse_args_into_dataclasses()
       with TemporaryDirectory() as tmp_dir:
              a__      :				int   	=			rag_example_args.output_dir or tmp_dir
              main(rag_example_args, processing_args, index_hnsw_args)





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a__      :				List[str]   	=			logging.get_logger(__name__)

a__      :				List[Any]   	=			{
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}



class       UpperCAmelCase__(							lowerCamelCase     ,							lowerCamelCase	):

   '''simple docstring'''




   A    :  List[str]       				=    "focalnet"


   def __init__(						self  :			Dict			,				lowerCAmelCase  :			Union[str, Any]=2_24			,				lowerCAmelCase  :			List[str]=4			,				lowerCAmelCase  :			int=3			,				lowerCAmelCase  :			Union[str, Any]=96			,				lowerCAmelCase  :			List[Any]=False			,				lowerCAmelCase  :			int=[1_92, 3_84, 7_68, 7_68]			,				lowerCAmelCase  :			str=[2, 2, 6, 2]			,				lowerCAmelCase  :			Tuple=[2, 2, 2, 2]			,				lowerCAmelCase  :			Optional[Any]=[3, 3, 3, 3]			,				lowerCAmelCase  :			int="gelu"			,				lowerCAmelCase  :			Any=4.0			,				lowerCAmelCase  :			List[str]=0.0			,				lowerCAmelCase  :			Union[str, Any]=0.1			,				lowerCAmelCase  :			Union[str, Any]=False			,				lowerCAmelCase  :			Tuple=1E-4			,				lowerCAmelCase  :			List[Any]=False			,				lowerCAmelCase  :			Optional[int]=False			,				lowerCAmelCase  :			List[str]=False			,				lowerCAmelCase  :			str=0.02			,				lowerCAmelCase  :			Optional[int]=1E-5			,				lowerCAmelCase  :			List[Any]=32			,				lowerCAmelCase  :			List[Any]=None			,				lowerCAmelCase  :			Union[str, Any]=None			,				**lowerCAmelCase  :			str			,				)	->				List[str]:




         """simple docstring"""

         super().__init__(**lowerCAmelCase)

         lowercase__     =  image_size
         lowercase__     =  patch_size
         lowercase__     =  num_channels
         lowercase__     =  embed_dim
         lowercase__     =  use_conv_embed
         lowercase__     =  hidden_sizes
         lowercase__     =  depths
         lowercase__     =  focal_levels
         lowercase__     =  focal_windows
         lowercase__     =  hidden_act
         lowercase__     =  mlp_ratio
         lowercase__     =  hidden_dropout_prob
         lowercase__     =  drop_path_rate
         lowercase__     =  use_layerscale
         lowercase__     =  layerscale_value
         lowercase__     =  use_post_layernorm
         lowercase__     =  use_post_layernorm_in_modulation
         lowercase__     =  normalize_modulator
         lowercase__     =  initializer_range
         lowercase__     =  layer_norm_eps
         lowercase__     =  encoder_stride
         lowercase__     =  ['stem'] + [f'''stage{idx}''' for idx in range(1			,				len(self.depths) + 1)]
         lowercase__,	lowercase__     =  get_aligned_output_features_output_indices(
             out_features=lowerCAmelCase			,				out_indices=lowerCAmelCase			,				stage_names=self.stage_names)
