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


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







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







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







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase						,							_lowerCamelCase     :		Optional[Any] 						=						[], []
    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 						A__  (					__A   , __A   , __A	):







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







def 						A__  (					__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
    _lowerCamelCase     :		str 						=						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
    _lowerCamelCase     :		Dict 						=						dataset.map(__A   , batched=__A   , num_proc=processing_args.num_proc	)

    # And compute the embeddings
    _lowerCamelCase     :		List[Any] 						=						DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	).to(device=__A	)
    _lowerCamelCase     :		List[str] 						=						DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	)
    _lowerCamelCase     :		List[str] 						=						Features(
        {"""text""": Value("""string"""	), """title""": Value("""string"""	), """embeddings""": Sequence(Value("""float32"""	)	)}	)  # optional, save as float32 instead of float64 to save space
    _lowerCamelCase     :		Dict 						=						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
    _lowerCamelCase     :		Optional[int] 						=						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
    _lowerCamelCase     :		Optional[int] 						=						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
    _lowerCamelCase     :		Dict 						=						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 	__snake_case	:





      '''simple docstring'''






      _snake_case    		=       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\''} ,  )
      _snake_case    		=       field(
          default=__lowerCAmelCase ,  metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'} ,  )
      _snake_case    		=       field(
          default='facebook/rag-sequence-nq' ,  metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''} ,  )
      _snake_case    		=       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\''
              )
          } ,  )
      _snake_case    		=       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 	__snake_case	:





      '''simple docstring'''






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





@dataclass
class 	__snake_case	:





      '''simple docstring'''






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

    lowerCAmelCase			:							List[Any]     =HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    lowerCAmelCase , lowerCAmelCase , lowerCAmelCase			:							Dict     =parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        lowerCAmelCase			:							List[str]     =rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCAmelCase			:							Any     =logging.get_logger(__name__)

lowerCAmelCase			:							List[Any]     ={
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       'swin'

      _snake_case    		=       {
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
      }



      def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				List[str]=224    ,  _UpperCamelCase     :				List[str]=4    ,  _UpperCamelCase     :				List[Any]=3    ,  _UpperCamelCase     :				Dict=96    ,  _UpperCamelCase     :				Any=[2, 2, 6, 2]    ,  _UpperCamelCase     :				Any=[3, 6, 12, 24]    ,  _UpperCamelCase     :				Tuple=7    ,  _UpperCamelCase     :				Tuple=4.0    ,  _UpperCamelCase     :				Dict=True    ,  _UpperCamelCase     :				Tuple=0.0    ,  _UpperCamelCase     :				Any=0.0    ,  _UpperCamelCase     :				Optional[int]=0.1    ,  _UpperCamelCase     :				Any="gelu"    ,  _UpperCamelCase     :				str=False    ,  _UpperCamelCase     :				str=0.0_2    ,  _UpperCamelCase     :				Dict=1E-5    ,  _UpperCamelCase     :				List[str]=32    ,  _UpperCamelCase     :				Optional[Any]=None    ,  _UpperCamelCase     :				List[Any]=None    ,  **_UpperCamelCase     :				List[Any]    ,  )				->Tuple:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						image_size
          _lowerCamelCase     :		Tuple 						=						patch_size
          _lowerCamelCase     :		Dict 						=						num_channels
          _lowerCamelCase     :		Union[str, Any] 						=						embed_dim
          _lowerCamelCase     :		str 						=						depths
          _lowerCamelCase     :		str 						=						len(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						num_heads
          _lowerCamelCase     :		Tuple 						=						window_size
          _lowerCamelCase     :		int 						=						mlp_ratio
          _lowerCamelCase     :		Optional[int] 						=						qkv_bias
          _lowerCamelCase     :		List[str] 						=						hidden_dropout_prob
          _lowerCamelCase     :		str 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		Tuple 						=						drop_path_rate
          _lowerCamelCase     :		List[str] 						=						hidden_act
          _lowerCamelCase     :		Dict 						=						use_absolute_embeddings
          _lowerCamelCase     :		int 						=						layer_norm_eps
          _lowerCamelCase     :		str 						=						initializer_range
          _lowerCamelCase     :		Dict 						=						encoder_stride
          # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          _lowerCamelCase     :		int 						=						int(embed_dim * 2 ** (len(_UpperCamelCase) - 1))
          _lowerCamelCase     :		Dict 						=						["""stem"""] + [F"""stage{idx}""" for idx in range(1    ,  len(_UpperCamelCase) + 1)]
          _lowerCamelCase						,							_lowerCamelCase     :		List[str] 						=						get_aligned_output_features_output_indices(
              out_features=_UpperCamelCase    ,  out_indices=_UpperCamelCase    ,  stage_names=self.stage_names)





class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       version.parse('1.11'		)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Mapping[str, Mapping[int, str]]:


          """simple docstring"""


          return OrderedDict(
              [
                  ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
              ])




      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->float:


          """simple docstring"""


          return 1E-4




