





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


__lowercase       :str       =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
__lowercase       :Optional[Any]       =  "cuda" if torch.cuda.is_available() else "cpu"







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  str=100			,   _lowerCamelCase      :  str=" "     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Tuple   =					text.split(_lowerCamelCase     )
						return [character.join(text[i : i + n]     ).strip() for i in range(0			,   len(_lowerCamelCase     )			,   _lowerCamelCase     )]







def       UpperCAmelCase							(     _lowerCamelCase      :  dict     ):





						'''simple docstring'''


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







def       UpperCAmelCase							(     _lowerCamelCase      :  dict			,   _lowerCamelCase      :  DPRContextEncoder			,   _lowerCamelCase      :  DPRContextEncoderTokenizerFast     ):





						'''simple docstring'''


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







def       UpperCAmelCase							(     _lowerCamelCase      :  "RagExampleArguments"			,   _lowerCamelCase      :  "ProcessingArguments"			,   _lowerCamelCase      :  "IndexHnswArguments"			,   ):





						'''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
						SCREAMING_SNAKE_CASE__     :				Dict   =					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__     :				Tuple   =					dataset.map(_lowerCamelCase			,   batched=_lowerCamelCase			,   num_proc=processing_args.num_proc     )

						# And compute the embeddings
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     ).to(device=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Any   =					DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     )
						SCREAMING_SNAKE_CASE__     :				str   =					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(_lowerCamelCase			,   ctx_encoder=_lowerCamelCase			,   ctx_tokenizer=_lowerCamelCase     )			,   batched=_lowerCamelCase			,   batch_size=processing_args.batch_size			,   features=_lowerCamelCase			,   )

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

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



@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						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'"}					,							)
							snake_case_							 =						field(
							    default=lowercase__					,							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(lowercase__							).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"""

							snake_case_							 =						field(
							    default=lowercase__					,							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    _a     :






							"""simple docstring"""

							snake_case_							 =						field(
							    default=7_68					,							metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}					,							)
							snake_case_							 =						field(
							    default=1_28					,							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)

				__lowercase       :str       =  HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
				__lowercase       ,     __lowercase       ,     __lowercase       :List[Any]       =  parser.parse_args_into_dataclasses()
				with TemporaryDirectory() as tmp_dir:
								__lowercase       :str       =  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,
    get_resize_output_image_size,
    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, logging


__lowercase       :int       =  logging.get_logger(__name__)



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["pixel_values"]

							def __init__(      self				:     int							,	a				:     bool = True							,	a				:     Optional[Dict[str, int]] = None							,	a				:     PILImageResampling = PILImageResampling.BILINEAR							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	**a				:     List[str]							,	)	->None:
													super().__init__(**a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size if size is not None else {"shortest_edge": 2_56}
													SCREAMING_SNAKE_CASE__     :				Any   =					get_size_dict(a							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_resize
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					resample
													SCREAMING_SNAKE_CASE__     :				int   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					crop_size
													SCREAMING_SNAKE_CASE__     :				Any   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Any   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				int   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				str   =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													SCREAMING_SNAKE_CASE__     :				int   =					image_std if image_std is not None else IMAGENET_STANDARD_STD

							def      A_  (      self				:     Tuple							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     PILImageResampling = PILImageResampling.BICUBIC							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Optional[int]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					get_size_dict(a							,	default_to_square=a    )
													if "shortest_edge" not in size:
																			raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					get_resize_output_image_size(a							,	size=size["shortest_edge"]							,	default_to_square=a    )
													return resize(a							,	size=a							,	resample=a							,	data_format=a							,	**a    )

							def      A_  (      self				:     List[Any]							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     List[Any]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					get_size_dict(a    )
													return center_crop(a							,	size=(size["height"], size["width"])							,	data_format=a							,	**a    )

							def      A_  (      self				:     Optional[int]							,	a				:     np.ndarray							,	a				:     float							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Dict    )	->np.ndarray:
													return rescale(a							,	scale=a							,	data_format=a							,	**a    )

							def      A_  (      self				:     Union[str, Any]							,	a				:     np.ndarray							,	a				:     Union[float, List[float]]							,	a				:     Union[float, List[float]]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Union[str, Any]							,	)	->np.ndarray:
													return normalize(a							,	mean=a							,	std=a							,	data_format=a							,	**a    )






							def      A_  (      self				:     Tuple							,	a				:     ImageInput							,	a				:     Optional[bool] = None							,	a				:     Dict[str, int] = None							,	a				:     PILImageResampling = None							,	a				:     bool = None							,	a				:     Dict[str, int] = None							,	a				:     Optional[bool] = None							,	a				:     Optional[float] = None							,	a				:     Optional[bool] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[float, List[float]]] = None							,	a				:     Optional[Union[str, TensorType]] = None							,	a				:     Union[str, ChannelDimension] = ChannelDimension.FIRST							,	**a				:     Any							,	)	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					size if size is not None else self.size
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a							,	default_to_square=a    )
													SCREAMING_SNAKE_CASE__     :				str   =					resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_center_crop if do_center_crop is not None else self.do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					crop_size if crop_size is not None else self.crop_size
													SCREAMING_SNAKE_CASE__     :				Dict   =					get_size_dict(a    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE__     :				int   =					rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_std if image_std is not None else self.image_std

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					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:
																			raise ValueError("Size 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.
													SCREAMING_SNAKE_CASE__     :				List[str]   =					[to_numpy_array(a    ) for image in images]

													if do_resize:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					[self.resize(image=a							,	size=a							,	resample=a    ) for image in images]

													if do_center_crop:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[self.center_crop(image=a							,	size=a    ) for image in images]

													if do_rescale:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					[self.rescale(image=a							,	scale=a    ) for image in images]

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

													SCREAMING_SNAKE_CASE__     :				Dict   =					[to_channel_dimension_format(a							,	a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					{"pixel_values": images}
													return BatchFeature(data=a							,	tensor_type=a    )