


'''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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


lowerCamelCase__     						=  logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase__     						=  'cuda' if torch.cuda.is_available() else 'cpu'






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase=100   ,			__lowerCAmelCase=" " ):
		_UpperCAmelCase		:							str             =   text.split(__lowerCAmelCase )
		return [character.join(text[i : i + n] ).strip() for i in range(0   ,			len(__lowerCAmelCase )   ,			__lowerCAmelCase )]






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






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase   ,			):
		######################################
		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
		_UpperCAmelCase		:							Tuple             =   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
		_UpperCAmelCase		:							List[str]             =   dataset.map(__lowerCAmelCase   ,			batched=__lowerCAmelCase   ,			num_proc=processing_args.num_proc )

		# And compute the embeddings
		_UpperCAmelCase		:							Union[str, Any]             =   DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=__lowerCAmelCase )
		_UpperCAmelCase		:							Union[str, Any]             =   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
		_UpperCAmelCase		:							Tuple             =   Features(
		    {"text": Value("string" ), "title": Value("string" ), "embeddings": Sequence(Value("float32" ) )} )  # optional, save as float32 instead of float64 to save space
		_UpperCAmelCase		:							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
		_UpperCAmelCase		:							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
		_UpperCAmelCase		:							Dict             =   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
		_UpperCAmelCase		:							Optional[int]             =   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

		######################################
		logger.info("Step 3 - Load RAG" )
		######################################

		# Easy way to load the model
		_UpperCAmelCase		:							Tuple             =   RagRetriever.from_pretrained(
		    rag_example_args.rag_model_name   ,			index_name="custom"   ,			indexed_dataset=__lowerCAmelCase )
		_UpperCAmelCase		:							Union[str, Any]             =   RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name   ,			retriever=__lowerCAmelCase )
		_UpperCAmelCase		:							Any             =   RagTokenizer.from_pretrained(rag_example_args.rag_model_name )

		# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
		# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

		######################################
		logger.info("Step 4 - Have fun" )
		######################################

		_UpperCAmelCase		:							Dict             =   rag_example_args.question or "What does Moses' rod turn into ?"
		_UpperCAmelCase		:							List[str]             =   tokenizer.question_encoder(__lowerCAmelCase   ,			return_tensors="pt" )["input_ids"]
		_UpperCAmelCase		:							Optional[int]             =   model.generate(__lowerCAmelCase )
		_UpperCAmelCase		:							str             =   tokenizer.batch_decode(__lowerCAmelCase   ,			skip_special_tokens=__lowerCAmelCase )[0]
		logger.info("Q: " + question )
		logger.info("A: " + generated_string )






@dataclass
class 						lowerCAmelCase__      :
			lowerCAmelCase			:						str							 =  field(
			    default=str(Path(UpperCAmelCase__						).parent / "test_data" / "my_knowledge_dataset.csv"						)     ,							metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}     ,							)
			lowerCAmelCase			:						Optional[str]							 =  field(
			    default=UpperCAmelCase__     ,							metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}     ,							)
			lowerCAmelCase			:						str							 =  field(
			    default="facebook/rag-sequence-nq"     ,							metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}     ,							)
			lowerCAmelCase			:						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'"
			        )
			    }     ,							)
			lowerCAmelCase			:						Optional[str]							 =  field(
			    default=UpperCAmelCase__     ,							metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}     ,							)






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






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

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







'''simple docstring'''


import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			**__lowerCAmelCase ):
		return RunBeamCommand(
		    args.dataset   ,			args.name   ,			args.cache_dir   ,			args.beam_pipeline_options   ,			args.data_dir   ,			args.all_configs   ,			args.save_info or args.save_infos   ,			args.ignore_verifications   ,			args.force_redownload   ,			**__lowerCAmelCase   ,			)






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  ArgumentParser			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Union[str, Any]             =   parser.add_parser("run_beam"						,	help="Run a Beam dataset processing pipeline"			)
					run_beam_parser.add_argument("dataset"						,	type=lowerCamelCase__						,	help="Name of the dataset to download"			)
					run_beam_parser.add_argument("--name"						,	type=lowerCamelCase__						,	default=lowerCamelCase__						,	help="Dataset config name"			)
					run_beam_parser.add_argument(
					    "--cache_dir"						,	type=lowerCamelCase__						,	default=lowerCamelCase__						,	help="Cache directory where the datasets are stored"						,	)
					run_beam_parser.add_argument(
					    "--beam_pipeline_options"						,	type=lowerCamelCase__						,	default=""						,	help="Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`"						,	)
					run_beam_parser.add_argument(
					    "--data_dir"						,	type=lowerCamelCase__						,	default=lowerCamelCase__						,	help="Can be used to specify a manual directory to get the files from"						,	)
					run_beam_parser.add_argument("--all_configs"						,	action="store_true"						,	help="Test all dataset configurations"			)
					run_beam_parser.add_argument("--save_info"						,	action="store_true"						,	help="Save the dataset infos file"			)
					run_beam_parser.add_argument(
					    "--ignore_verifications"						,	action="store_true"						,	help="Run the test without checksums and splits checks"			)
					run_beam_parser.add_argument("--force_redownload"						,	action="store_true"						,	help="Force dataset redownload"			)
					# aliases
					run_beam_parser.add_argument("--save_infos"						,	action="store_true"						,	help="alias for save_info"			)
					run_beam_parser.set_defaults(func=lowerCamelCase__			)




			def __init__(		self	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  str						,	lowerCamelCase__	:  bool						,	lowerCamelCase__	:  bool						,	lowerCamelCase__	:  bool						,	lowerCamelCase__	:  bool						,	**lowerCamelCase__	:  Optional[Any]						,	) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   dataset
					_UpperCAmelCase		:							str             =   name
					_UpperCAmelCase		:							int             =   cache_dir
					_UpperCAmelCase		:							Optional[int]             =   beam_pipeline_options
					_UpperCAmelCase		:							int             =   data_dir
					_UpperCAmelCase		:							Optional[int]             =   all_configs
					_UpperCAmelCase		:							Any             =   save_infos
					_UpperCAmelCase		:							Optional[Any]             =   ignore_verifications
					_UpperCAmelCase		:							Any             =   force_redownload
					_UpperCAmelCase		:							Tuple             =   config_kwargs






			def 				lowerCAmelCase__     (		self	:  Dict			) ->Union[str, Any]:



					'''simple docstring'''







					import apache_beam as beam

					if self._name is not None and self._all_configs:
							print("Both parameters `name` and `all_configs` can't be used at once."			)
							exit(1			)
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Union[str, Any]             =   self._dataset, self._name
					_UpperCAmelCase		:							Union[str, Any]             =   dataset_module_factory(lowerCamelCase__			)
					_UpperCAmelCase		:							Any             =   import_main_class(dataset_module.module_path			)
					_UpperCAmelCase		:							List[DatasetBuilder]             =   []
					if self._beam_pipeline_options:
							_UpperCAmelCase		:							Optional[int]             =   beam.options.pipeline_options.PipelineOptions(
							    flags=[F"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(","			) if opt]			)
					else:
							_UpperCAmelCase		:							str             =   None
					if self._all_configs and len(builder_cls.BUILDER_CONFIGS			) > 0:
							for builder_config in builder_cls.BUILDER_CONFIGS:
									builders.append(
									    builder_cls(
									        config_name=builder_config.name						,	data_dir=self._data_dir						,	hash=dataset_module.hash						,	beam_options=lowerCamelCase__						,	cache_dir=self._cache_dir						,	base_path=dataset_module.builder_kwargs.get("base_path"			)						,	)			)
					else:
							builders.append(
							    builder_cls(
							        config_name=lowerCamelCase__						,	data_dir=self._data_dir						,	beam_options=lowerCamelCase__						,	cache_dir=self._cache_dir						,	base_path=dataset_module.builder_kwargs.get("base_path"			)						,	**self._config_kwargs						,	)			)

					for builder in builders:
							builder.download_and_prepare(
							    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
							    if not self._force_redownload
							    else DownloadMode.FORCE_REDOWNLOAD						,	download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH			)						,	verification_mode=VerificationMode.NO_CHECKS
							    if self._ignore_verifications
							    else VerificationMode.ALL_CHECKS						,	try_from_hf_gcs=lowerCamelCase__						,	)
							if self._save_infos:
									builder._save_infos()

					print("Apache beam run successful."			)

					# If save_infos=True, the dataset infos file is created next to the loaded module file.
					# Let's move it to the original directory of the dataset script, to allow the user to
					# upload them on S3 at the same time afterwards.
					if self._save_infos:
							_UpperCAmelCase		:							Optional[Any]             =   os.path.join(builder_cls.get_imported_module_dir()						,	config.DATASETDICT_INFOS_FILENAME			)

							_UpperCAmelCase		:							List[str]             =   Path(lowerCamelCase__			).name + ".py"

							_UpperCAmelCase		:							int             =   os.path.join(lowerCamelCase__						,	lowerCamelCase__			)
							if os.path.isfile(lowerCamelCase__			):
									_UpperCAmelCase		:							Any             =   os.path.dirname(lowerCamelCase__			)
							elif os.path.isfile(lowerCamelCase__			):
									_UpperCAmelCase		:							Tuple             =   path
							else:  # in case of a remote dataset
									print(F"""Dataset Infos file saved at {dataset_infos_path}"""			)
									exit(1			)

							# Move datasetinfo back to the user
							_UpperCAmelCase		:							Union[str, Any]             =   os.path.join(lowerCamelCase__						,	config.DATASETDICT_INFOS_FILENAME			)
							copyfile(lowerCamelCase__						,	lowerCamelCase__			)
							print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""			)



