


import numpy as np
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:Dict="utf-8"			,       _A				:Optional[Any]="surrogatepass"					)->						int:
			UpperCamelCase__   =    bin(int.from_bytes(text.encode(_A			,       _A					)			,       "big"					)					)[2:]
			return bits.zfill(8 * ((len(_A					) + 7) // 8)					)
def     UpperCamelCase_(			_A				:Optional[int]			,       _A				:int="utf-8"			,       _A				:List[str]="surrogatepass"					)->						Optional[Any]:
			UpperCamelCase__   =    int(_A			,       2					)
			return n.to_bytes((n.bit_length() + 7) // 8			,       "big"					).decode(_A			,       _A					) or "\0"
def     UpperCamelCase_(			_A				:Union[str, Any]			,       _A				:Tuple					)->						Optional[int]:
			if size_par + len(_A					) <= 2**size_par - (len(_A					) - 1):
						raise ValueError("size of parity don't match with size of data"					)

			UpperCamelCase__   =    []
			UpperCamelCase__   =    []
			UpperCamelCase__   =    [bin(_A					)[2:] for x in range(1			,       size_par + len(_A					) + 1					)]

			# sorted information data for the size of the output data
			UpperCamelCase__   =    []
			# data position template + parity
			UpperCamelCase__   =    []
			# parity bit counter
			UpperCamelCase__   =    0
			# counter position of data bits
			UpperCamelCase__   =    0

			for x in range(1			,       size_par + len(_A					) + 1					):
						# Performs a template of bit positions - who should be given,
						# and who should be parity
						if qtd_bp < size_par:
									if (np.log(_A					) / np.log(2					)).is_integer():
												data_out_gab.append("P"					)
												UpperCamelCase__   =    qtd_bp + 1
									else:
												data_out_gab.append("D"					)
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data[cont_data]					)
									cont_data += 1
						else:
									data_ord.append(_A					)

    # Calculates parity
			UpperCamelCase__   =    0  # parity bit counter
			for bp in range(1			,       size_par + 1					):
						# Bit counter one for a given parity
						UpperCamelCase__   =    0
						# counter to control the loop reading
						UpperCamelCase__   =    0
						for x in data_ord:
									if x is not None:
												try:
															UpperCamelCase__   =    (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCamelCase__   =    "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(cont_bo % 2					)

						qtd_bp += 1

			# Mount the message
			UpperCamelCase__   =    0  # parity bit counter
			for x in range(0			,       size_par + len(_A					)					):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]					)					)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]					)

			return data_out







def     UpperCamelCase_(			_A				:int			,       _A				:str					)->						Dict:
			UpperCamelCase__   =    []
			# Parity bit counter
			UpperCamelCase__   =    0
			# Counter p data bit reading
			UpperCamelCase__   =    0
			# list of parity received
			UpperCamelCase__   =    []
			UpperCamelCase__   =    []

			for x in range(1			,       len(_A					) + 1					):
						# Performs a template of bit positions - who should be given,
						#  and who should be parity
						if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
									data_out_gab.append("P"					)
									UpperCamelCase__   =    qtd_bp + 1
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_output.append(data[cont_data]					)
						else:
									parity_received.append(data[cont_data]					)
						cont_data += 1

			# -----------calculates the parity with the data
			UpperCamelCase__   =    []
			UpperCamelCase__   =    []
			UpperCamelCase__   =    [bin(_A					)[2:] for x in range(1			,       size_par + len(_A					) + 1					)]

			#  sorted information data for the size of the output data
			UpperCamelCase__   =    []
			# Data position feedback + parity
			UpperCamelCase__   =    []
			# Parity bit counter
			UpperCamelCase__   =    0
			# Counter p data bit reading
			UpperCamelCase__   =    0

			for x in range(1			,       size_par + len(_A					) + 1					):
						# Performs a template position of bits - who should be given,
						# and who should be parity
						if qtd_bp < size_par and (np.log(_A					) / np.log(2					)).is_integer():
									data_out_gab.append("P"					)
									UpperCamelCase__   =    qtd_bp + 1
						else:
									data_out_gab.append("D"					)

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data_output[cont_data]					)
									cont_data += 1
						else:
									data_ord.append(_A					)

    # Calculates parity
			UpperCamelCase__   =    0  # parity bit counter
			for bp in range(1			,       size_par + 1					):
						# Bit counter one for a certain parity
						UpperCamelCase__   =    0
						# Counter to control loop reading
						UpperCamelCase__   =    0
						for x in data_ord:
									if x is not None:
												try:
															UpperCamelCase__   =    (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															UpperCamelCase__   =    "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(str(cont_bo % 2					)					)

						qtd_bp += 1

			# Mount the message
			UpperCamelCase__   =    0  # Parity bit counter
			for x in range(0			,       size_par + len(_A					)					):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]					)					)
									cont_bp += 1
						else:
									data_out.append(data_ord[x]					)

			UpperCamelCase__   =    parity_received == parity
			return data_output, ack






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,
)


__UpperCamelCase      						=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
__UpperCamelCase      						=    'cuda' if torch.cuda.is_available() else 'cpu'
def     UpperCamelCase_(			_A				:str			,       _A				:Tuple=1_00			,       _A				:str=" "					)->						List[str]:
			UpperCamelCase__   =    text.split(_A					)
			return [character.join(text[i : i + n]					).strip() for i in range(0			,       len(_A					)			,       _A					)]
def     UpperCamelCase_(			_A				:dict					)->						dict:
			UpperCamelCase__, UpperCamelCase__   =    [], []
			for title, text in zip(documents["title"]			,       documents["text"]					):
						if text is not None:
									for passage in split_text(_A					):
												titles.append(title if title is not None else ""					)
												texts.append(_A					)
			return {"title": titles, "text": texts}
def     UpperCamelCase_(			_A				:dict			,       _A				:DPRContextEncoder			,       _A				:DPRContextEncoderTokenizerFast					)->						dict:
			UpperCamelCase__   =    ctx_tokenizer(
			    documents["title"]			,       documents["text"]			,       truncation=_A			,       padding="longest"			,       return_tensors="pt"					)["input_ids"]
			UpperCamelCase__   =    ctx_encoder(input_ids.to(device=_A					)			,       return_dict=_A					).pooler_output
			return {"embeddings": embeddings.detach().cpu().numpy()}
def     UpperCamelCase_(			_A				:"RagExampleArguments"			,       _A				:"ProcessingArguments"			,       _A				:"IndexHnswArguments"			,       )->						List[str]:
			######################################
			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__   =    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__   =    dataset.map(_A			,       batched=_A			,       num_proc=processing_args.num_proc					)

			# And compute the embeddings
			UpperCamelCase__   =    DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=_A					)
			UpperCamelCase__   =    DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
			UpperCamelCase__   =    Features(
			    {"text": Value("string"					), "title": Value("string"					), "embeddings": Sequence(Value("float32"					)					)}					)  # optional, save as float32 instead of float64 to save space
			UpperCamelCase__   =    dataset.map(
			    partial(_A			,       ctx_encoder=_A			,       ctx_tokenizer=_A					)			,       batched=_A			,       batch_size=processing_args.batch_size			,       features=_A			,       )

			# And finally save your dataset
			UpperCamelCase__   =    os.path.join(rag_example_args.output_dir			,       "my_knowledge_dataset"					)
			dataset.save_to_disk(_A					)
			# 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__   =    faiss.IndexHNSWFlat(index_hnsw_args.d			,       index_hnsw_args.m			,       faiss.METRIC_INNER_PRODUCT					)
			dataset.add_faiss_index("embeddings"			,       custom_index=_A					)

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

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

			# Easy way to load the model
			UpperCamelCase__   =    RagRetriever.from_pretrained(
			    rag_example_args.rag_model_name			,       index_name="custom"			,       indexed_dataset=_A					)
			UpperCamelCase__   =    RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,       retriever=_A					)
			UpperCamelCase__   =    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__   =    rag_example_args.question or "What does Moses' rod turn into ?"
			UpperCamelCase__   =    tokenizer.question_encoder(_A			,       return_tensors="pt"					)["input_ids"]
			UpperCamelCase__   =    model.generate(_A					)
			UpperCamelCase__   =    tokenizer.batch_decode(_A			,       skip_special_tokens=_A					)[0]
			logger.info("Q: " + question					)
			logger.info("A: " + generated_string					)







@dataclass
class     lowerCamelCase__		:






			"""simple docstring"""






			_UpperCamelCase						:   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\''}   ,		)
			_UpperCamelCase						:   Optional[str]					=						field(
			    default=UpperCAmelCase   ,		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=UpperCAmelCase   ,		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=UpperCAmelCase   ,		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)

						__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)


