



'''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 	a_					(							_lowerCAmelCase	,_lowerCAmelCase=100	,_lowerCAmelCase=" "	)				->				str:
  __lowerCamelCase	:	List[Any]   	=			text.split(_lowerCamelCase	)
  return [character.join(text[i : i + n]	).strip() for i in range(0	,len(_lowerCamelCase	)	,_lowerCamelCase	)]

def 	a_					(							_lowerCAmelCase	)				->				Tuple:
  __lowerCamelCase	:	str   	=			[], []
  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	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				List[str]:
  __lowerCamelCase	:	int   	=			load_dataset(
      'csv'	,data_files=[csv_path]	,split='train'	,delimiter='\t'	,column_names=['title', 'text']	)
  __lowerCamelCase	:	Dict   	=			kb_dataset.map(
      _lowerCamelCase	,batched=_lowerCamelCase	,num_proc=1	)  # if you want you can load already splitted csv.
  __lowerCamelCase	:	Optional[int]   	=			[kb_dataset.shard(_lowerCamelCase	,_lowerCamelCase	,contiguous=_lowerCamelCase	) for i in range(_lowerCamelCase	)]
  __lowerCamelCase	:	Union[str, Any]   	=			kb_list[process_num]

  __lowerCamelCase	:	Dict   	=			"""data_""" + str(_lowerCamelCase	)
  __lowerCamelCase	:	Union[str, Any]   	=			os.path.join(_lowerCamelCase	,_lowerCamelCase	)

  __lowerCamelCase	:	Any   	=			DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'	)
  __lowerCamelCase	:	int   	=			ctx_encoder.to(device=_lowerCamelCase	)

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

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

  __lowerCamelCase	:	List[str]   	=			data_shrad.map(
      partial(_lowerCamelCase	,ctx_encoder=_lowerCamelCase	,ctx_tokenizer=_lowerCamelCase	,device=_lowerCamelCase	)	,batched=_lowerCamelCase	,batch_size=16	,features=_lowerCamelCase	,)
  dataset.save_to_disk(_lowerCamelCase	)


def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				Any:
  __lowerCamelCase	:	Any   	=			[]

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

  __lowerCamelCase	:	List[Any]   	=			concatenate_datasets(_lowerCamelCase	)
  faiss.omp_set_num_threads(96	)

  __lowerCamelCase	:	Tuple   	=			faiss.IndexHNSWFlat(768	,128	,faiss.METRIC_INNER_PRODUCT	)
  concat.add_faiss_index('embeddings'	,custom_index=_lowerCamelCase	)
  concat.get_index('embeddings'	).save(
      _lowerCamelCase	)  # since we load the index in to memory,we can directly update the index in the disk










'''simple docstring'''





import random






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Union[str, Any]      ,     _a				:     float      ,     _a				:     float       ) -> None:
    __lowerCamelCase	:	Optional[int]   	=			x
    __lowerCamelCase	:	Union[str, Any]   	=			y




  def 						_lowercase					(			self				:     Tuple       ) -> bool:
    return (self.x**2 + self.y**2) <= 1




  @classmethod
  def 						_lowercase					(			cls				:     List[Any]       ) -> Optional[int]:
    return cls(x=random.random()      ,     y=random.random()       )




def 	a_					(							_lowerCAmelCase	)				->				float:
  if number_of_simulations < 1:
    raise ValueError('At least one simulation is necessary to estimate PI.'	)

  __lowerCamelCase	:	Optional[int]   	=			0
  for _ in range(_lowerCAmelCase	):
    __lowerCamelCase	:	Dict   	=			Point.random_unit_square()

    if random_point.is_in_unit_circle():
      number_in_unit_circle += 1

  return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
   # import doctest

   # doctest.testmod()
   from math import pi

   _UpperCamelCase							    =						'Please enter the desired number of Monte Carlo simulations: '
   _UpperCamelCase							    =						estimate_pi(int(input(prompt).strip()))
   print(f'''An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}''')






