

import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment



def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				str						,							SCREAMING_SNAKE_CASE_:				Optional[Any]=()						,							SCREAMING_SNAKE_CASE_:				Dict=None						,							SCREAMING_SNAKE_CASE_:				Optional[int]="no"						,							SCREAMING_SNAKE_CASE_:				Tuple="29500"   )      ->   Optional[int]:




				'''simple docstring'''





				A__          =      False
				A__          =      False
				if any(key.startswith("KAGGLE"   ) for key in os.environ.keys()   ):
								A__          =      True
				elif "IPython" in sys.modules:
								A__          =      "google.colab" in str(sys.modules["IPython"].get_ipython()   )

				try:
								A__          =      PrecisionType(mixed_precision.lower()   )
				except ValueError:
								raise ValueError(
								    F'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'   )

				if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"						,							UpperCamelCase_   ) is not None):
								# TPU launch
								import torch_xla.distributed.xla_multiprocessing as xmp

								if len(AcceleratorState._shared_state   ) > 0:
												raise ValueError(
												    "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
												    "your training function. Restart your notebook and make sure no cells initializes an "
												    "`Accelerator`."   )
								if num_processes is None:
												A__          =      8

								A__          =      PrepareForLaunch(UpperCamelCase_						,							distributed_type="TPU"   )
								print(F'Launching a training on {num_processes} TPU cores.'   )
								xmp.spawn(UpperCamelCase_						,							args=UpperCamelCase_						,							nprocs=UpperCamelCase_						,							start_method="fork"   )
				elif in_colab:
								# No need for a distributed launch otherwise as it's either CPU or one GPU.
								if torch.cuda.is_available():
												print("Launching training on one GPU."   )
								else:
												print("Launching training on one CPU."   )
								function(*UpperCamelCase_   )
				else:
								if num_processes is None:
												raise ValueError(
												    "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."   )

								if num_processes > 1:
												# Multi-GPU launch
												from torch.multiprocessing import start_processes
												from torch.multiprocessing.spawn import ProcessRaisedException

												if len(AcceleratorState._shared_state   ) > 0:
																raise ValueError(
																    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
																    "inside your training function. Restart your notebook and make sure no cells initializes an "
																    "`Accelerator`."   )

												if torch.cuda.is_initialized():
																raise ValueError(
																    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
																    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
																    "function."   )

												# torch.distributed will expect a few environment variable to be here. We set the ones common to each
												# process here (the other ones will be set be the launcher).
												with patch_environment(
												    world_size=UpperCamelCase_						,							master_addr="127.0.01"						,							master_port=UpperCamelCase_						,							mixed_precision=UpperCamelCase_   ):
																A__          =      PrepareForLaunch(UpperCamelCase_						,							distributed_type="MULTI_GPU"   )
																print(F'Launching training on {num_processes} GPUs.'   )
																try:
																				start_processes(UpperCamelCase_						,							args=UpperCamelCase_						,							nprocs=UpperCamelCase_						,							start_method="fork"   )
																except ProcessRaisedException as e:
																				if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																								raise RuntimeError(
																								    "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
																								    "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
																								    "Please review your imports and test them when running the `notebook_launcher()` to identify "
																								    "which one is problematic."   ) from e

								else:
												# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
												if is_mps_available():
																A__          =      "1"
																print("Launching training on MPS."   )
												elif torch.cuda.is_available():
																print("Launching training on one GPU."   )
												else:
																print("Launching training on CPU."   )
												function(*UpperCamelCase_   )





def        lowerCAmelCase__				(						SCREAMING_SNAKE_CASE_:				List[str]						,							SCREAMING_SNAKE_CASE_:				Tuple=()						,							SCREAMING_SNAKE_CASE_:				List[Any]=2   )      ->   Union[str, Any]:




				'''simple docstring'''





				from torch.multiprocessing import start_processes

				with tempfile.NamedTemporaryFile() as tmp_file:
								# torch.distributed will expect a few environment variable to be here. We set the ones common to each
								# process here (the other ones will be set be the launcher).
								with patch_environment(
								    world_size=UpperCamelCase_						,							master_addr="127.0.01"						,							master_port="29500"						,							accelerate_mixed_precision="no"						,							accelerate_debug_rdv_file=tmp_file.name						,							accelerate_use_cpu="yes"						,							):
												A__          =      PrepareForLaunch(UpperCamelCase_						,							debug=UpperCamelCase_   )
												start_processes(UpperCamelCase_						,							args=UpperCamelCase_						,							nprocs=UpperCamelCase_						,							start_method="fork"   )





'''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)





