

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


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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=100		,							SCREAMING_SNAKE_CASE=" "				)       ->      List[str]:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[Any]        =    text.split(SCREAMING_SNAKE_CASE				)
     return [character.join(text[i : i + n]				).strip() for i in range(0		,							len(SCREAMING_SNAKE_CASE				)		,							SCREAMING_SNAKE_CASE				)]







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      dict:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      dict:

     """simple docstring"""







     _lowerCAmelCase   :					List[str]        =    ctx_tokenizer(
         documents["title"]		,							documents["text"]		,							truncation=SCREAMING_SNAKE_CASE		,							padding="longest"		,							return_tensors="pt"				)["input_ids"]
     _lowerCAmelCase   :					Union[str, Any]        =    ctx_encoder(input_ids.to(device=SCREAMING_SNAKE_CASE				)		,							return_dict=SCREAMING_SNAKE_CASE				).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							)       ->      List[Any]:

     """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   :					int        =    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   :					Any        =    dataset.map(SCREAMING_SNAKE_CASE		,							batched=SCREAMING_SNAKE_CASE		,							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=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name				)
     _lowerCAmelCase   :					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(SCREAMING_SNAKE_CASE		,							ctx_encoder=SCREAMING_SNAKE_CASE		,							ctx_tokenizer=SCREAMING_SNAKE_CASE				)		,							batched=SCREAMING_SNAKE_CASE		,							batch_size=processing_args.batch_size		,							features=SCREAMING_SNAKE_CASE		,							)

     # And finally save your dataset
     _lowerCAmelCase   :					Optional[Any]        =    os.path.join(rag_example_args.output_dir		,							"my_knowledge_dataset"				)
     dataset.save_to_disk(SCREAMING_SNAKE_CASE				)
     # 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[Any]        =    faiss.IndexHNSWFlat(index_hnsw_args.d		,							index_hnsw_args.m		,							faiss.METRIC_INNER_PRODUCT				)
     dataset.add_faiss_index("embeddings"		,							custom_index=SCREAMING_SNAKE_CASE				)

     # And save the index
     _lowerCAmelCase   :					str        =    os.path.join(rag_example_args.output_dir		,							"my_knowledge_dataset_hnsw_index.faiss"				)
     dataset.get_index("embeddings"				).save(SCREAMING_SNAKE_CASE				)
     # dataset.load_faiss_index("embeddings", index_path)  # to reload the index





@dataclass
class A__    :



 """simple docstring"""







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







 _lowercase     :					Optional[int]      =						field(
     default=A  ,		metadata={
         '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
     }  ,		)
 _lowercase     :					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"""







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

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









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

 def __init__(		self :	List[str]     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PIL.Image.BICUBIC     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	bool = True     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					str        =    size if size is not None else {"height": 2_5_6, "width": 2_5_6}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					str        =    do_resize
      _lowerCAmelCase   :					str        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Union[str, Any]        =    do_center_crop
      _lowerCAmelCase   :					Union[str, Any]        =    crop_size
      _lowerCAmelCase   :					List[Any]        =    do_rescale
      _lowerCAmelCase   :					Tuple        =    rescale_factor
      _lowerCAmelCase   :					Any        =    do_normalize
      _lowerCAmelCase   :					List[Any]        =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase   :					List[str]        =    image_std if image_std is not None else IMAGENET_STANDARD_STD

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PIL.Image.BICUBIC     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	str     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return resize(
          A_     ,						size=(size["height"], size["width"])     ,						resample=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Any     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	str     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	np.ndarray     ,						A_ :	Union[int, float]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




      return rescale(A_     ,						scale=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	np.ndarray     ,						A_ :	Union[float, List[float]]     ,						A_ :	Union[float, List[float]]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      return normalize(A_     ,						mean=A_     ,						std=A_     ,						data_format=A_     ,						**A_       )


 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	Union[str, Any]=None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = None     ,						A_ :	float = None     ,						A_ :	bool = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					str        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					int        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					str        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					Optional[Any]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					int        =    do_normalize if do_normalize is not None else self.do_normalize
      _lowerCAmelCase   :					str        =    image_mean if image_mean is not None else self.image_mean
      _lowerCAmelCase   :					List[Any]        =    image_std if image_std is not None else self.image_std

      _lowerCAmelCase   :					Optional[int]        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					int        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Tuple        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."       )

      if do_resize and size is None or resample is None:
           raise ValueError("Size and resample must be specified if do_resize is True."       )

      if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."       )

      if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."       )

      if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Union[str, Any]        =    [to_numpy_array(A_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					List[str]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					Any        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

      if do_rescale:
           _lowerCAmelCase   :					str        =    [self.rescale(image=A_     ,						scale=A_       ) for image in images]

      if do_normalize:
           _lowerCAmelCase   :					Dict        =    [self.normalize(image=A_     ,						mean=A_     ,						std=A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					int        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )






