


'''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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


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






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Union[str, Any]   						=["pixel_values"]






						def __init__(  self							:      Optional[int] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[int, float] = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      bool = True ,  **lowerCAmelCase							:      List[Any] ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 3_84, "width": 3_84}
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		do_resize
													A_			    =		size
													A_			    =		resample
													A_			    =		do_rescale
													A_			    =		rescale_factor
													A_			    =		do_normalize
													A_			    =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													A_			    =		image_std if image_std is not None else OPENAI_CLIP_STD
													A_			    =		do_convert_rgb






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)
													if "height" not in size or "width" not in size:
																				raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[int, float] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      str ,  ):
													return rescale(lowerCAmelCase ,  scale=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Dict ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Dict[str, int]] = None ,  lowerCAmelCase							:      PILImageResampling = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[float] = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		do_resize if do_resize is not None else self.do_resize
													A_			    =		resample if resample is not None else self.resample
													A_			    =		do_rescale if do_rescale is not None else self.do_rescale
													A_			    =		rescale_factor if rescale_factor is not None else self.rescale_factor
													A_			    =		do_normalize if do_normalize is not None else self.do_normalize
													A_			    =		image_mean if image_mean is not None else self.image_mean
													A_			    =		image_std if image_std is not None else self.image_std
													A_			    =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													A_			    =		size if size is not None else self.size
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		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_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."	)

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																				A_			    =		[convert_to_rgb(lowerCAmelCase	) for image in images]

													# All transformations expect numpy arrays.
													A_			    =		[to_numpy_array(lowerCAmelCase	) for image in images]

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

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

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

													A_			    =		[to_channel_dimension_format(lowerCAmelCase ,  lowerCAmelCase	) for image in images]

													A_			    =		BatchFeature(data={"pixel_values": images} ,  tensor_type=lowerCAmelCase	)

													return encoded_outputs









'''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					:				Dict									=		'''cuda''' if torch.cuda.is_available() else '''cpu'''







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=1_0_0						,  UpperCamelCase_=" " ):
							A_			    =		text.split(UpperCamelCase_ )
							return [character.join(text[i : i + n] ).strip() for i in range(0						,  len(UpperCamelCase_ )						,  UpperCamelCase_ )]







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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  ):
							######################################
							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
							A_			    =		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
							A_			    =		dataset.map(UpperCamelCase_						,  batched=UpperCamelCase_						,  num_proc=processing_args.num_proc )

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

						"""simple docstring"""






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






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






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






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






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

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





