




'''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


a       :						Any								=		logging.getLogger(__name__)
torch.set_grad_enabled(False)
a       :						Tuple								=		'''cuda''' if torch.cuda.is_available() else '''cpu'''


def __UpperCAmelCase	(   _UpperCAmelCase		:   str		,				_UpperCAmelCase		:   Tuple=1_00		,				_UpperCAmelCase		:   List[Any]=" "						)      ->      List[str]:
			__snake_case			=		text.split(_UpperCAmelCase						)
			return [character.join(text[i : i + n]						).strip() for i in range(0		,				len(_UpperCAmelCase						)		,				_UpperCAmelCase						)]


def __UpperCAmelCase	(   _UpperCAmelCase		:   dict						)      ->      dict:
			__snake_case		,     __snake_case			=		[], []
			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	(   _UpperCAmelCase		:   dict		,				_UpperCAmelCase		:   DPRContextEncoder		,				_UpperCAmelCase		:   DPRContextEncoderTokenizerFast						)      ->      dict:
			__snake_case			=		ctx_tokenizer(
			    documents["title"]		,				documents["text"]		,				truncation=_UpperCAmelCase		,				padding="longest"		,				return_tensors="pt"						)["input_ids"]
			__snake_case			=		ctx_encoder(input_ids.to(device=_UpperCAmelCase						)		,				return_dict=_UpperCAmelCase						).pooler_output
			return {"embeddings": embeddings.detach().cpu().numpy()}


def __UpperCAmelCase	(   _UpperCAmelCase		:   "RagExampleArguments"		,				_UpperCAmelCase		:   "ProcessingArguments"		,				_UpperCAmelCase		:   "IndexHnswArguments"		,				)      ->      Union[str, 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
			__snake_case			=		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
			__snake_case			=		dataset.map(_UpperCAmelCase		,				batched=_UpperCAmelCase		,				num_proc=processing_args.num_proc						)

			# And compute the embeddings
			__snake_case			=		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=_UpperCAmelCase						)
			__snake_case			=		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
			__snake_case			=		Features(
			    {"text": Value("string"						), "title": Value("string"						), "embeddings": Sequence(Value("float32"						)						)}						)  # optional, save as float32 instead of float64 to save space
			__snake_case			=		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
			__snake_case			=		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
			__snake_case			=		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
			__snake_case			=		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 		SCREAMING_SNAKE_CASE__  :
			__SCREAMING_SNAKE_CASE    =			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'"""}				,     )
			__SCREAMING_SNAKE_CASE    =			field(
			    default=_UpperCamelCase				,     metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}				,     )
			__SCREAMING_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'"""}				,     )
			__SCREAMING_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'"""
			        )
			    }				,     )
			__SCREAMING_SNAKE_CASE    =			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 		SCREAMING_SNAKE_CASE__  :
			__SCREAMING_SNAKE_CASE    =			field(
			    default=_UpperCamelCase				,     metadata={
			        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
			    }				,     )
			__SCREAMING_SNAKE_CASE    =			field(
			    default=16				,     metadata={
			        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
			    }				,     )







@dataclass
class 		SCREAMING_SNAKE_CASE__  :
			__SCREAMING_SNAKE_CASE    =			field(
			    default=768				,     metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}				,     )
			__SCREAMING_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)

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









import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowerCAmelCase__			:      Optional[int]								=0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
lowerCAmelCase__			:      str								=[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					WATERMARK_BITS
										SCREAMING_SNAKE_CASE_		:     int					=					WatermarkEncoder()

										self.encoder.set_watermark('bits'			,						self.watermark       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if images.shape[-1] < 2_5_6:
													return images

										SCREAMING_SNAKE_CASE_		:     Tuple					=					(2_5_5 * (images / 2 + 0.5)).cpu().permute(0			,						2			,						3			,						1       ).float().numpy()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[self.encoder.encode(lowerCAmelCase__			,						'dwtDct'       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.from_numpy(np.array(lowerCAmelCase__       )       ).permute(0			,						3			,						1			,						2       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.clamp(2 * (images / 2_5_5 - 0.5)			,						min=-1.0			,						max=1.0       )
										return images





