
'''simple docstring'''
import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


A__   :		int 		="""\
@inproceedings{pillutla-etal:mauve:neurips2021,
  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}

"""

A__   :		List[Any] 		="""\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
"""

A__   :		List[Any] 		="""
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].
    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: \"c\" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric('mauve')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,						_KWARGS_DESCRIPTION			)
class  __A				( datasets.Metric			):
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   return datasets.MetricInfo(
       description=_DESCRIPTION				,   citation=_CITATION				,   homepage="""https://github.com/krishnap25/mauve"""				,   inputs_description=_KWARGS_DESCRIPTION				,   features=datasets.Features(
           {
               """predictions""": datasets.Value("""string"""				,   id="""sequence"""       ),
               """references""": datasets.Value("""string"""				,   id="""sequence"""       ),
           }       )				,   codebase_urls=["""https://github.com/krishnap25/mauve"""]				,   reference_urls=[
           """https://arxiv.org/abs/2102.01454""",
           """https://github.com/krishnap25/mauve""",
       ]				,   )






 def 		lowercase_(   self			:    Dict				,   lowerCamelCase			:    str				,   lowerCamelCase			:    str				,   lowerCamelCase			:    List[Any]=None				,   lowerCamelCase			:    Tuple=None				,   lowerCamelCase			:    Optional[int]=None				,   lowerCamelCase			:    Optional[Any]=None				,   lowerCamelCase			:    str="auto"				,   lowerCamelCase			:    Optional[int]=-1				,   lowerCamelCase			:    Any=0.9				,   lowerCamelCase			:    Optional[Any]=5				,   lowerCamelCase			:    str=5_00				,   lowerCamelCase			:    Union[str, Any]="gpt2-large"				,   lowerCamelCase			:    Optional[int]=-1				,   lowerCamelCase			:    Tuple=10_24				,   lowerCamelCase			:    Dict=25				,   lowerCamelCase			:    Union[str, Any]=5				,   lowerCamelCase			:    Optional[int]=True				,   lowerCamelCase			:    Union[str, Any]=25				,   ):



   """simple docstring"""





   __A		:     Optional[Any]												=		compute_mauve(
       p_text=lowerCamelCase				,   q_text=lowerCamelCase				,   p_features=lowerCamelCase				,   q_features=lowerCamelCase				,   p_tokens=lowerCamelCase				,   q_tokens=lowerCamelCase				,   num_buckets=lowerCamelCase				,   pca_max_data=lowerCamelCase				,   kmeans_explained_var=lowerCamelCase				,   kmeans_num_redo=lowerCamelCase				,   kmeans_max_iter=lowerCamelCase				,   featurize_model_name=lowerCamelCase				,   device_id=lowerCamelCase				,   max_text_length=lowerCamelCase				,   divergence_curve_discretization_size=lowerCamelCase				,   mauve_scaling_factor=lowerCamelCase				,   verbose=lowerCamelCase				,   seed=lowerCamelCase				,   )
   return out





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       int:





  """simple docstring"""

  __A		:     Optional[int]												=		1
  for i in range(1			,     num + 1 ):
    fact *= i
  return fact







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       int:





  """simple docstring"""

  __A		:     List[str]												=		0
  while number > 0:
    __A		:     Union[str, Any]												=		number % 10
    sum_of_digits += last_digit
    __A		:     int												=		number // 10  # Removing the last_digit from the given number
  return sum_of_digits







def        A_     (						__SCREAMING_SNAKE_CASE						:  int = 100 )				->       int:





  """simple docstring"""

  __A		:     Union[str, Any]												=		factorial(__SCREAMING_SNAKE_CASE )
  __A		:     Any												=		split_and_add(__SCREAMING_SNAKE_CASE )
  return result


if __name__ == "__main__":
     print(solution(int(input('Enter the Number: ').strip())))



