"""simple docstring"""



import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  int		, __snake_case      :  Tuple=1_00		, __snake_case      :  Any=" "   ):





  '''simple docstring'''





  lowercase							   =	text.split(SCREAMING_SNAKE_CASE__   )
  return [character.join(text[i : i + n]   ).strip() for i in range(0		, len(SCREAMING_SNAKE_CASE__   )		, SCREAMING_SNAKE_CASE__   )]
def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  Union[str, Any]   ):





  '''simple docstring'''





  lowercase							   =	[], []
  for title, text in zip(documents['title']		, documents['text']   ):
    if text is not None:
      for passage in split_text(SCREAMING_SNAKE_CASE__   ):
        titles.append(title if title is not None else ''   )
        texts.append(SCREAMING_SNAKE_CASE__   )
  return {"title": titles, "text": texts}
def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  Dict		, __snake_case      :  Dict		, __snake_case      :  str		, __snake_case      :  Union[str, Any]		, __snake_case      :  Dict		, __snake_case      :  int   ):





  '''simple docstring'''





  lowercase							   =	load_dataset(
      'csv'		, data_files=[csv_path]		, split='train'		, delimiter='\t'		, column_names=['title', 'text']   )
  lowercase							   =	kb_dataset.map(
      SCREAMING_SNAKE_CASE__		, batched=SCREAMING_SNAKE_CASE__		, num_proc=1   )  # if you want you can load already splitted csv.
  lowercase							   =	[kb_dataset.shard(SCREAMING_SNAKE_CASE__		, SCREAMING_SNAKE_CASE__		, contiguous=SCREAMING_SNAKE_CASE__   ) for i in range(SCREAMING_SNAKE_CASE__   )]
  lowercase							   =	kb_list[process_num]

  lowercase							   =	'data_' + str(SCREAMING_SNAKE_CASE__   )
  lowercase							   =	os.path.join(SCREAMING_SNAKE_CASE__		, SCREAMING_SNAKE_CASE__   )

  lowercase							   =	DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'   )
  lowercase							   =	ctx_encoder.to(device=SCREAMING_SNAKE_CASE__   )

  def embed(
      __snake_case      :  dict		, __snake_case      :  DPRContextEncoder		, __snake_case      :  DPRContextEncoderTokenizerFast		, __snake_case      :  List[str]   ) -> dict:
    lowercase							   =	ctx_tokenizer(
        documents['title']		, documents['text']		, truncation=SCREAMING_SNAKE_CASE__		, padding='longest'		, return_tensors='pt'   )['input_ids']
    lowercase							   =	ctx_encoder(input_ids.to(device=SCREAMING_SNAKE_CASE__   )		, return_dict=SCREAMING_SNAKE_CASE__   ).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}

  lowercase							   =	Features(
      {'text': Value('string'   ), 'title': Value('string'   ), 'embeddings': Sequence(Value('float32'   )   )}   )  # optional, save as float32 instead of float64 to save space

  lowercase							   =	data_shrad.map(
      partial(SCREAMING_SNAKE_CASE__		, ctx_encoder=SCREAMING_SNAKE_CASE__		, ctx_tokenizer=SCREAMING_SNAKE_CASE__		, device=SCREAMING_SNAKE_CASE__   )		, batched=SCREAMING_SNAKE_CASE__		, batch_size=16		, features=SCREAMING_SNAKE_CASE__		, )
  dataset.save_to_disk(SCREAMING_SNAKE_CASE__   )



def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  Union[str, Any]		, __snake_case      :  Optional[int]   ):





  '''simple docstring'''





  lowercase							   =	[]

  for shard_address in glob(str(SCREAMING_SNAKE_CASE__   ) + '/*/'   ):
    data_shard_list.append(load_from_disk(SCREAMING_SNAKE_CASE__   )   )

  lowercase							   =	concatenate_datasets(SCREAMING_SNAKE_CASE__   )
  faiss.omp_set_num_threads(96   )

  lowercase							   =	faiss.IndexHNSWFlat(7_68		, 1_28		, faiss.METRIC_INNER_PRODUCT   )
  concat.add_faiss_index('embeddings'		, custom_index=SCREAMING_SNAKE_CASE__   )
  concat.get_index('embeddings'   ).save(
      SCREAMING_SNAKE_CASE__   )  # since we load the index in to memory,we can directly update the index in the disk










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  str   ):





  '''simple docstring'''





  lowercase							   =	len(__snake_case   )
  lowercase							   =	[0] * length
  lowercase							   =	[[False for i in range(__snake_case   )] for j in range(__snake_case   )]
  for i, c in enumerate(__snake_case   ):
    lowercase							   =	i
    for j in range(i + 1   ):
      if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
        lowercase							   =	True
        lowercase							   =	min(__snake_case		, 0 if j == 0 else (cut[j - 1] + 1)   )
    lowercase							   =	mincut
  return cut[length - 1]


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


