



'''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


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



def 	a			( lowerCamelCase__						,      lowerCamelCase__=1_00						,      lowerCamelCase__=" "    ):
			'''simple docstring'''





			A_	:						Union[str, Any]	=     text.split(lowerCamelCase__    )
			return [character.join(text[i : i + n]    ).strip() for i in range(0						,      len(lowerCamelCase__    )						,      lowerCamelCase__    )]



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_,    A_	:						Any	=     [], []
			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 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





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



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      ):
			'''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_	:						str	=     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_	:						Union[str, Any]	=     dataset.map(lowerCamelCase__						,      batched=lowerCamelCase__						,      num_proc=processing_args.num_proc    )

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







@dataclass
class 	_lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE   :		str					=					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   :		Optional[str]					=					field(
	    default=__UpperCAmelCase      ,			metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}      ,			)
	__SCREAMING_SNAKE_CASE   :		str					=					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   :		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\''
	        )
	    }      ,			)
	__SCREAMING_SNAKE_CASE   :		Optional[str]					=					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 	_lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					field(
	    default=__UpperCAmelCase      ,			metadata={
	        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
	    }      ,			)
	__SCREAMING_SNAKE_CASE   :		int					=					field(
	    default=16      ,			metadata={
	        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
	    }      ,			)







@dataclass
class 	_lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE   :		int					=					field(
	    default=768      ,			metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}      ,			)
	__SCREAMING_SNAKE_CASE   :		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							:Union[str, Any]				    =					HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
						lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase							:Union[str, Any]				    =					parser.parse_args_into_dataclasses()
						with TemporaryDirectory() as tmp_dir:
												lowerCamelCase							:List[Any]				    =					rag_example_args.output_dir or tmp_dir
												main(rag_example_args, processing_args, index_hnsw_args)




'''simple docstring'''




import argparse
import struct
import unittest







class 	_lowerCAmelCase							:


	def __init__(self       ,      lowercase							):
				A_	:						List[str]	=     data

				# Initialize hash values
				A_	:						Tuple	=     [
				    0X6A09_E667,
				    0XBB67_AE85,
				    0X3C6E_F372,
				    0XA54F_F53A,
				    0X510E_527F,
				    0X9B05_688C,
				    0X1F83_D9AB,
				    0X5BE0_CD19,
				]

				# Initialize round constants
				A_	:						List[Any]	=     [
				    0X428A_2F98,
				    0X7137_4491,
				    0XB5C0_FBCF,
				    0XE9B5_DBA5,
				    0X3956_C25B,
				    0X59F1_11F1,
				    0X923F_82A4,
				    0XAB1C_5ED5,
				    0XD807_AA98,
				    0X1283_5B01,
				    0X2431_85BE,
				    0X550C_7DC3,
				    0X72BE_5D74,
				    0X80DE_B1FE,
				    0X9BDC_06A7,
				    0XC19B_F174,
				    0XE49B_69C1,
				    0XEFBE_4786,
				    0X0FC1_9DC6,
				    0X240C_A1CC,
				    0X2DE9_2C6F,
				    0X4A74_84AA,
				    0X5CB0_A9DC,
				    0X76F9_88DA,
				    0X983E_5152,
				    0XA831_C66D,
				    0XB003_27C8,
				    0XBF59_7FC7,
				    0XC6E0_0BF3,
				    0XD5A7_9147,
				    0X06CA_6351,
				    0X1429_2967,
				    0X27B7_0A85,
				    0X2E1B_2138,
				    0X4D2C_6DFC,
				    0X5338_0D13,
				    0X650A_7354,
				    0X766A_0ABB,
				    0X81C2_C92E,
				    0X9272_2C85,
				    0XA2BF_E8A1,
				    0XA81A_664B,
				    0XC24B_8B70,
				    0XC76C_51A3,
				    0XD192_E819,
				    0XD699_0624,
				    0XF40E_3585,
				    0X106A_A070,
				    0X19A4_C116,
				    0X1E37_6C08,
				    0X2748_774C,
				    0X34B0_BCB5,
				    0X391C_0CB3,
				    0X4ED8_AA4A,
				    0X5B9C_CA4F,
				    0X682E_6FF3,
				    0X748F_82EE,
				    0X78A5_636F,
				    0X84C8_7814,
				    0X8CC7_0208,
				    0X90BE_FFFA,
				    0XA450_6CEB,
				    0XBEF9_A3F7,
				    0XC671_78F2,
				]

				A_	:						Tuple	=     self.preprocessing(self.data							)
				self.final_hash()


	@staticmethod
	def     _a	(lowercase							):
				A_	:						Any	=     b"""\x80""" + (b"""\x00""" * (63 - (len(lowercase							) + 8) % 64))
				A_	:						Optional[Any]	=     struct.pack(""">Q"""       ,      (len(lowercase							) * 8)							)
				return data + padding + big_endian_integer


	def     _a	(self							):
				# Convert into blocks of 64 bytes
				A_	:						str	=     [
				    self.preprocessed_data[x : x + 64]
				    for x in range(0       ,      len(self.preprocessed_data							)       ,      64							)
				]

				for block in self.blocks:
							# Convert the given block into a list of 4 byte integers
							A_	:						Tuple	=     list(struct.unpack(""">16L"""       ,      lowercase							)							)
							# add 48 0-ed integers
							words += [0] * 48

							A_,    A_,    A_,    A_,    A_,    A_,    A_,    A_	:						Dict	=     self.hashes

							for index in range(0       ,      64							):
										if index > 15:
													# modify the zero-ed indexes at the end of the array
													A_	:						Optional[Any]	=     (
													    self.ror(words[index - 15]       ,      7							)
													    ^ self.ror(words[index - 15]       ,      18							)
													    ^ (words[index - 15] >> 3)
													)
													A_	:						Tuple	=     (
													    self.ror(words[index - 2]       ,      17							)
													    ^ self.ror(words[index - 2]       ,      19							)
													    ^ (words[index - 2] >> 10)
													)

													A_	:						Any	=     (
													    words[index - 16] + sa + words[index - 7] + sa
													) % 0X1_0000_0000

										# Compression
										A_	:						Union[str, Any]	=     self.ror(lowercase       ,      6							) ^ self.ror(lowercase       ,      11							) ^ self.ror(lowercase       ,      25							)
										A_	:						List[Any]	=     (e & f) ^ ((~e & 0XFFFF_FFFF) & g)
										A_	:						Dict	=     (
										    h + sa + ch + self.round_constants[index] + words[index]
										) % 0X1_0000_0000
										A_	:						List[str]	=     self.ror(lowercase       ,      2							) ^ self.ror(lowercase       ,      13							) ^ self.ror(lowercase       ,      22							)
										A_	:						str	=     (a & b) ^ (a & c) ^ (b & c)
										A_	:						int	=     (sa + maj) % 0X1_0000_0000

										A_,    A_,    A_,    A_,    A_,    A_,    A_,    A_	:						List[Any]	=     (
										    g,
										    f,
										    e,
										    ((d + tempa) % 0X1_0000_0000),
										    c,
										    b,
										    a,
										    ((tempa + tempa) % 0X1_0000_0000),
										)

							A_	:						int	=     [a, b, c, d, e, f, g, h]

							# Modify final values
							A_	:						Dict	=     [
							    ((element + mutated_hash_values[index]) % 0X1_0000_0000)
							    for index, element in enumerate(self.hashes							)
							]

				A_	:						Any	=     """""".join([hex(lowercase							)[2:].zfill(8							) for value in self.hashes]							)


	def     _a	(self       ,      lowercase       ,      lowercase							):
				return 0XFFFF_FFFF & (value << (32 - rotations)) | (value >> rotations)







class 	_lowerCAmelCase							(			unittest.TestCase ):


	def     _a	(self							):
				import hashlib

				A_	:						Optional[Any]	=     bytes("""Test String"""       ,      """utf-8"""							)
				self.assertEqual(SHAaaa(lowercase							).hash       ,      hashlib.shaaaa(lowercase							).hexdigest()							)






def 	a			( ):
			'''simple docstring'''





			import doctest

			doctest.testmod()

			A_	:						Any	=     argparse.ArgumentParser()
			parser.add_argument(
			    """-s"""						,      """--string"""						,      dest="""input_string"""						,      default="""Hello World!! Welcome to Cryptography"""						,      help="""Hash the string"""						,      )
			parser.add_argument(
			    """-f"""						,      """--file"""						,      dest="""input_file"""						,      help="""Hash contents of a file"""    )

			A_	:						List[str]	=     parser.parse_args()

			A_	:						Tuple	=     args.input_string

			# hash input should be a bytestring
			if args.input_file:
						with open(args.input_file						,      """rb"""    ) as f:
									A_	:						Union[str, Any]	=     f.read()
			else:
						A_	:						Optional[Any]	=     bytes(lowerCamelCase__						,      """utf-8"""    )

			print(SHAaaa(lowerCamelCase__    ).hash    )


if __name__ == "__main__":
						main()