"""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


SCREAMING_SNAKE_CASE__   :      Optional[int]							=logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE__   :      Union[str, Any]							='cuda' if torch.cuda.is_available() else 'cpu'



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_=100						,	SCREAMING_SNAKE_CASE_=" " )      ->List[str]:
    _lowerCamelCase	: Optional[int]       =       text.split(__UpperCAmelCase )
    return [character.join(text[i : i + n] ).strip() for i in range(0						,	len(__UpperCAmelCase )						,	__UpperCAmelCase )]



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_ )      ->dict:
    _lowerCamelCase,					_lowerCamelCase	: Optional[int]       =       [], []
    for title, text in zip(documents['''title''']						,	documents['''text'''] ):
        if text is not None:
            for passage in split_text(__UpperCAmelCase ):
                titles.append(title if title is not None else '''''' )
                texts.append(__UpperCAmelCase )
    return {"title": titles, "text": texts}



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->dict:
    _lowerCamelCase	: str       =       ctx_tokenizer(
        documents['''title''']						,	documents['''text''']						,	truncation=__UpperCAmelCase						,	padding='''longest'''						,	return_tensors='''pt''' )['''input_ids''']
    _lowerCamelCase	: Optional[Any]       =       ctx_encoder(input_ids.to(device=__UpperCAmelCase )						,	return_dict=__UpperCAmelCase ).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	)      ->Any:
    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	: Optional[int]       =       dataset.map(__UpperCAmelCase						,	batched=__UpperCAmelCase						,	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=__UpperCAmelCase )
    _lowerCamelCase	: Union[str, Any]       =       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(__UpperCAmelCase						,	ctx_encoder=__UpperCAmelCase						,	ctx_tokenizer=__UpperCAmelCase )						,	batched=__UpperCAmelCase						,	batch_size=processing_args.batch_size						,	features=__UpperCAmelCase						,	)

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

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



@dataclass
class      _UpperCAmelCase						:






    """simple docstring"""
    __snake_case    =       field(
        default=str(Path(_snake_case						).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=_snake_case ,			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(_snake_case						).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"""
    __snake_case    =       field(
        default=_snake_case ,			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      _UpperCAmelCase						:






    """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)

  SCREAMING_SNAKE_CASE__   :      int							=HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
  SCREAMING_SNAKE_CASE__		,				SCREAMING_SNAKE_CASE__		,				SCREAMING_SNAKE_CASE__   :      Union[str, Any]							=parser.parse_args_into_dataclasses()
  with TemporaryDirectory() as tmp_dir:
    SCREAMING_SNAKE_CASE__   :      int							=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


a   	=    logging.get_logger(__name__)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							b.T
   __SCREAMING_SNAKE_CASE   				=							np.sum(np.square(__UpperCAmelCase   )				,						axis=1   )
   __SCREAMING_SNAKE_CASE   				=							np.sum(np.square(__UpperCAmelCase   )				,						axis=0   )
   __SCREAMING_SNAKE_CASE   				=							np.matmul(__UpperCAmelCase				,						__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							aa[:, None] - 2 * ab + ba[None, :]
   return d

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							x.reshape(-1				,						3   )
   __SCREAMING_SNAKE_CASE   				=							squared_euclidean_distance(__UpperCAmelCase				,						__UpperCAmelCase   )
   return np.argmin(__UpperCAmelCase				,						axis=1   )







class __a			( _snake_case      ):
   __UpperCamelCase :			Any						   =						['pixel_values']





   def __init__(       self					:      Any					,lowerCamelCase					:      Optional[Union[List[List[int]], np.ndarray]] = None					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      bool = True					,**lowerCamelCase					:      Optional[Any]					,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							size if size is not None else {"""height""": 256, """width""": 256}
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							np.array(lowerCamelCase     ) if clusters is not None else None
      __SCREAMING_SNAKE_CASE   				=							do_resize
      __SCREAMING_SNAKE_CASE   				=							size
      __SCREAMING_SNAKE_CASE   				=							resample
      __SCREAMING_SNAKE_CASE   				=							do_normalize
      __SCREAMING_SNAKE_CASE   				=							do_color_quantize





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Dict[str, int]					,lowerCamelCase					:      PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      int					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							rescale(image=lowerCamelCase					,scale=1 / 127.5					,data_format=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							image - 1
      return image





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      ImageInput					,lowerCamelCase					:      bool = None					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      PILImageResampling = None					,lowerCamelCase					:      bool = None					,lowerCamelCase					:      Optional[bool] = None					,lowerCamelCase					:      Optional[Union[List[List[int]], np.ndarray]] = None					,lowerCamelCase					:      Optional[Union[str, TensorType]] = None					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,**lowerCamelCase					:      Any					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							do_resize if do_resize is not None else self.do_resize
      __SCREAMING_SNAKE_CASE   				=							size if size is not None else self.size
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							resample if resample is not None else self.resample
      __SCREAMING_SNAKE_CASE   				=							do_normalize if do_normalize is not None else self.do_normalize
      __SCREAMING_SNAKE_CASE   				=							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      __SCREAMING_SNAKE_CASE   				=							clusters if clusters is not None else self.clusters
      __SCREAMING_SNAKE_CASE   				=							np.array(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							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.
      __SCREAMING_SNAKE_CASE   				=							[to_numpy_array(lowerCamelCase     ) for image in images]

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

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

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

         # flatten to (batch_size, height*width)
         __SCREAMING_SNAKE_CASE   				=							images.shape[0]
         __SCREAMING_SNAKE_CASE   				=							images.reshape(lowerCamelCase					,-1     )

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

      __SCREAMING_SNAKE_CASE   				=							{"""input_ids""": images}
      return BatchFeature(data=lowerCamelCase					,tensor_type=lowerCamelCase     )





