



"""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       _lowerCamelCase				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Tuple=100,	UpperCAmelCase_ :       str=" "     )	-> List[str]:






	"""simple docstring"""





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

def       _lowerCamelCase				(							UpperCAmelCase_ :       dict     )	-> dict:






	"""simple docstring"""





	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       _lowerCamelCase				(							UpperCAmelCase_ :       dict,	UpperCAmelCase_ :       DPRContextEncoder,	UpperCAmelCase_ :       DPRContextEncoderTokenizerFast     )	-> dict:






	"""simple docstring"""





	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       _lowerCamelCase				(							UpperCAmelCase_ :       "RagExampleArguments",	UpperCAmelCase_ :       "ProcessingArguments",	UpperCAmelCase_ :       "IndexHnswArguments",	)	-> int:






	"""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
	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       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					str							  =     field(
			    default=str(Path(_lowerCAmelCase      ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"      )       ,		metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}       ,		)
			A__	:					Optional[str]							  =     field(
			    default=_lowerCAmelCase       ,		metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}       ,		)
			A__	:					str							  =     field(
			    default="facebook/rag-sequence-nq"       ,		metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}       ,		)
			A__	:					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'"
			        )
			    }       ,		)
			A__	:					Optional[str]							  =     field(
			    default=str(Path(_lowerCAmelCase      ).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"""


			A__	:					Optional[int]							  =     field(
			    default=_lowerCAmelCase       ,		metadata={
			        "help": "The number of processes to use to split the documents into passages. Default is single process."
			    }       ,		)
			A__	:					int							  =     field(
			    default=1_6       ,		metadata={
			        "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
			    }       ,		)






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					int							  =     field(
			    default=7_6_8       ,		metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}       ,		)
			A__	:					int							  =     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 ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_pt_objects import *  # noqa F403
else:
			from .scheduling_consistency_models import CMStochasticIterativeScheduler
			from .scheduling_ddim import DDIMScheduler
			from .scheduling_ddim_inverse import DDIMInverseScheduler
			from .scheduling_ddim_parallel import DDIMParallelScheduler
			from .scheduling_ddpm import DDPMScheduler
			from .scheduling_ddpm_parallel import DDPMParallelScheduler
			from .scheduling_deis_multistep import DEISMultistepScheduler
			from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
			from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
			from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
			from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
			from .scheduling_euler_discrete import EulerDiscreteScheduler
			from .scheduling_heun_discrete import HeunDiscreteScheduler
			from .scheduling_ipndm import IPNDMScheduler
			from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
			from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
			from .scheduling_karras_ve import KarrasVeScheduler
			from .scheduling_pndm import PNDMScheduler
			from .scheduling_repaint import RePaintScheduler
			from .scheduling_sde_ve import ScoreSdeVeScheduler
			from .scheduling_sde_vp import ScoreSdeVpScheduler
			from .scheduling_unclip import UnCLIPScheduler
			from .scheduling_unipc_multistep import UniPCMultistepScheduler
			from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
			from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_flax_objects import *  # noqa F403
else:
			from .scheduling_ddim_flax import FlaxDDIMScheduler
			from .scheduling_ddpm_flax import FlaxDDPMScheduler
			from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
			from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
			from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
			from .scheduling_pndm_flax import FlaxPNDMScheduler
			from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
			from .scheduling_utils_flax import (
			    FlaxKarrasDiffusionSchedulers,
			    FlaxSchedulerMixin,
			    FlaxSchedulerOutput,
			    broadcast_to_shape_from_left,
			)


try:
			if not (is_torch_available() and is_scipy_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
			from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
			if not (is_torch_available() and is_torchsde_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
			from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler




