



"""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


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





def   __magic_name__					(				lowercase				,      lowercase=100				,      lowercase=" "			):
   SCREAMING_SNAKE_CASE_:			Any        =text.split(lowercase			)
   return [character.join(text[i : i + n]			).strip() for i in range(0				,      len(lowercase			)				,      lowercase			)]





def   __magic_name__					(				lowercase			):
   SCREAMING_SNAKE_CASE_:			Tuple        =[], []
   for title, text in zip(documents["""title"""]				,      documents["""text"""]			):
      if text is not None:
         for passage in split_text(lowercase			):
            titles.append(title if title is not None else """"""			)
            texts.append(lowercase			)
   return {"title": titles, "text": texts}





def   __magic_name__					(				lowercase				,      lowercase				,      lowercase			):
   SCREAMING_SNAKE_CASE_:			Tuple        =ctx_tokenizer(
       documents["""title"""]				,      documents["""text"""]				,      truncation=lowercase				,      padding="""longest"""				,      return_tensors="""pt"""			)["input_ids"]
   SCREAMING_SNAKE_CASE_:			str        =ctx_encoder(input_ids.to(device=lowercase			)				,      return_dict=lowercase			).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}





def   __magic_name__					(				lowercase				,      lowercase				,      lowercase				,      ):
   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
   SCREAMING_SNAKE_CASE_:			Optional[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
   SCREAMING_SNAKE_CASE_:			List[str]        =dataset.map(lowercase				,      batched=lowercase				,      num_proc=processing_args.num_proc			)

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

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

   # And save the index
   SCREAMING_SNAKE_CASE_:			str        =os.path.join(rag_example_args.output_dir				,      """my_knowledge_dataset_hnsw_index.faiss"""			)
   dataset.get_index("""embeddings"""			).save(lowercase			)
   # dataset.load_faiss_index("embeddings", index_path)  # to reload the index
@dataclass
class        a						:
  UpperCamelCase						:					int      =       field(
      default=str(Path(UpperCAmelCase__							).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'							)						,   metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}						,   )
  UpperCamelCase						:					Optional[int]      =       field(
      default=UpperCAmelCase__						,   metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}						,   )
  UpperCamelCase						:					int      =       field(
      default='facebook/rag-sequence-nq'						,   metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}						,   )
  UpperCamelCase						:					Optional[Any]      =       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						:					str      =       field(
      default=str(Path(UpperCAmelCase__							).parent / 'test_run' / 'dummy-kb'							)						,   metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}						,   )







@dataclass
class        a						:
  UpperCamelCase						:					Optional[int]      =       field(
      default=UpperCAmelCase__						,   metadata={
          'help': 'The number of processes to use to split the documents into passages. Default is single process.'
      }						,   )
  UpperCamelCase						:					Union[str, Any]      =       field(
      default=1_6						,   metadata={
          'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
      }						,   )







@dataclass
class        a						:
  UpperCamelCase						:					Optional[int]      =       field(
      default=7_6_8						,   metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}						,   )
  UpperCamelCase						:					List[str]      =       field(
      default=1_2_8						,   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)







'''simple docstring'''


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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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__)


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							b.T
     __A	:       Optional[Any]      =							np.sum(np.square(SCREAMING_SNAKE_CASE							)       ,					axis=1							)
     __A	:       List[Any]      =							np.sum(np.square(SCREAMING_SNAKE_CASE							)       ,					axis=0							)
     __A	:       List[Any]      =							np.matmul(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     __A	:       str      =							aa[:, None] - 2 * ab + ba[None, :]
     return d


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       Union[str, Any]      =							x.reshape(-1       ,					3							)
     __A	:       Any      =							squared_euclidean_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     return np.argmin(SCREAMING_SNAKE_CASE       ,					axis=1							)







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				['pixel_values']


     def __init__(   self			,     lowerCamelCase = None			,     lowerCamelCase = True			,     lowerCamelCase = None			,     lowerCamelCase = PILImageResampling.BILINEAR			,     lowerCamelCase = True			,     lowerCamelCase = True			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          super().__init__(**lowerCamelCase		)
          __A	:       Optional[int]      =							size if size is not None else {"height": 256, "width": 256}
          __A	:       int      =							get_size_dict(lowerCamelCase		)
          __A	:       str      =							np.array(lowerCamelCase		) if clusters is not None else None
          __A	:       List[Any]      =							do_resize
          __A	:       Dict      =							size
          __A	:       str      =							resample
          __A	:       Optional[Any]      =							do_normalize
          __A	:       Dict      =							do_color_quantize


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase = PILImageResampling.BILINEAR			,     lowerCamelCase = None			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       Optional[Any]      =							get_size_dict(lowerCamelCase		)
          if "height" not in size or "width" not in size:
               raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}"		)
          return resize(
              lowerCamelCase			,     size=(size["height"], size["width"])			,     resample=lowerCamelCase			,     data_format=lowerCamelCase			,     **lowerCamelCase		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     ):


          '''simple docstring'''





          __A	:       List[str]      =							rescale(image=lowerCamelCase			,     scale=1 / 127.5			,     data_format=lowerCamelCase		)
          __A	:       List[Any]      =							image - 1
          return image


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = None			,     lowerCamelCase = ChannelDimension.FIRST			,     **lowerCamelCase			,     ):


          '''simple docstring'''





          __A	:       List[Any]      =							do_resize if do_resize is not None else self.do_resize
          __A	:       int      =							size if size is not None else self.size
          __A	:       Tuple      =							get_size_dict(lowerCamelCase		)
          __A	:       Tuple      =							resample if resample is not None else self.resample
          __A	:       Union[str, Any]      =							do_normalize if do_normalize is not None else self.do_normalize
          __A	:       Tuple      =							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
          __A	:       Any      =							clusters if clusters is not None else self.clusters
          __A	:       Dict      =							np.array(lowerCamelCase		)

          __A	:       Optional[int]      =							make_list_of_images(lowerCamelCase		)

          if not valid_images(lowerCamelCase		):
               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_color_quantize and clusters is None:
               raise ValueError("Clusters must be specified if do_color_quantize is True."		)

          # All transformations expect numpy arrays.
          __A	:       Optional[Any]      =							[to_numpy_array(lowerCamelCase		) for image in images]

          if do_resize:
               __A	:       Dict      =							[self.resize(image=lowerCamelCase			,     size=lowerCamelCase			,     resample=lowerCamelCase		) for image in images]

          if do_normalize:
               __A	:       List[Any]      =							[self.normalize(image=lowerCamelCase		) for image in images]

          if do_color_quantize:
               __A	:       Union[str, Any]      =							[to_channel_dimension_format(lowerCamelCase			,     ChannelDimension.LAST		) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               __A	:       int      =							np.array(lowerCamelCase		)
               __A	:       Any      =							color_quantize(lowerCamelCase			,     lowerCamelCase		).reshape(images.shape[:-1]		)

               # flatten to (batch_size, height*width)
               __A	:       Tuple      =							images.shape[0]
               __A	:       List[Any]      =							images.reshape(lowerCamelCase			,     -1		)

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               __A	:       Optional[int]      =							list(lowerCamelCase		)
          else:
               __A	:       Tuple      =							[to_channel_dimension_format(lowerCamelCase			,     lowerCamelCase		) for image in images]

          __A	:       Optional[int]      =							{"input_ids": images}
          return BatchFeature(data=lowerCamelCase			,     tensor_type=lowerCamelCase		)

