"""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,
)


_UpperCamelCase         =		logging.getLogger(__name__)
torch.set_grad_enabled(False)
_UpperCamelCase         =		"cuda" if torch.cuda.is_available() else "cpu"

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

def    _A(		lowerCAmelCase       ):
							A__							,       A__					:    List[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       ):
							A__					:    Union[str, 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 ,     ):
							######################################
							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__					:    List[Any]														= 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__					:    Optional[int]														= dataset.map(lowerCAmelCase ,     batched=lowerCAmelCase ,     num_proc=processing_args.num_proc       )

							# And compute the embeddings
							A__					:    List[Any]														= DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       ).to(device=lowerCAmelCase       )
							A__					:    List[Any]														= DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       )
							A__					:    List[str]														= Features(
							    {"""text""": Value("""string"""       ), """title""": Value("""string"""       ), """embeddings""": Sequence(Value("""float32"""       )       )}       )  # optional, save as float32 instead of float64 to save space
							A__					:    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
							A__					:    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
							A__					:    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
							A__					:    Any														= 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
							A__					:    str														= RagRetriever.from_pretrained(
							    rag_example_args.rag_model_name ,     index_name="""custom""" ,     indexed_dataset=lowerCAmelCase       )
							A__					:    Dict														= RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name ,     retriever=lowerCAmelCase       )
							A__					:    Dict														= 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"""       )
							######################################

							A__					:    Any														= rag_example_args.question or """What does Moses' rod turn into ?"""
							A__					:    Optional[Any]														= tokenizer.question_encoder(lowerCAmelCase ,     return_tensors="""pt"""       )["""input_ids"""]
							A__					:    int														= model.generate(lowerCAmelCase       )
							A__					:    str														= tokenizer.batch_decode(lowerCAmelCase ,     skip_special_tokens=lowerCAmelCase       )[0]
							logger.info("""Q: """ + question       )
							logger.info("""A: """ + generated_string       )
@dataclass
class 	__UpperCAmelCase						:







			'''simple docstring'''
			_UpperCamelCase     :	str				    =							field(
			    default=str(Path(__A    ).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=__A       ,      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=__A       ,      metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}       ,      )





@dataclass
class 	__UpperCAmelCase						:







			'''simple docstring'''
			_UpperCamelCase     :	Optional[int]				    =							field(
			    default=__A       ,      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 	__UpperCAmelCase						:







			'''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)






"""simple docstring"""



def    _A(		lowerCAmelCase       ):
							A__					:    str														= len(lowerCAmelCase       )
							A__					:    List[Any]														= [0] * length
							A__					:    Union[str, Any]														= [[False for i in range(lowerCAmelCase       )] for j in range(lowerCAmelCase       )]
							for i, c in enumerate(lowerCAmelCase       ):
														A__					:    Dict														= i
														for j in range(i + 1       ):
																					if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
																												A__					:    List[str]														= True
																												A__					:    Optional[Any]														= min(lowerCAmelCase ,     0 if j == 0 else (cut[j - 1] + 1)       )
														A__					:    Optional[int]														= mincut
							return cut[length - 1]


if __name__ == "__main__":
				_UpperCamelCase         =		input("Enter the string: ").strip()
				_UpperCamelCase         =		find_minimum_partitions(s)
				print(F'Minimum number of partitions required for the \'{s}\' is {ans}')





