


"""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


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

_snake_case          =							"\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"

_snake_case          =							"\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    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'].\n    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\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: \"c\" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric('mauve')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,		_KWARGS_DESCRIPTION )
class   _a      (      datasets.Metric ):
    def      _UpperCamelCase       ( self							:					Any				):
          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      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[Any]=None							,					SCREAMING_SNAKE_CASE__							:					int=None							,					SCREAMING_SNAKE_CASE__							:					List[str]=None							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=None							,					SCREAMING_SNAKE_CASE__							:					Optional[int]="auto"							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=-1							,					SCREAMING_SNAKE_CASE__							:					int=0.9							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=5							,					SCREAMING_SNAKE_CASE__							:					int=5_00							,					SCREAMING_SNAKE_CASE__							:					Optional[int]="gpt2-large"							,					SCREAMING_SNAKE_CASE__							:					Dict=-1							,					SCREAMING_SNAKE_CASE__							:					Any=10_24							,					SCREAMING_SNAKE_CASE__							:					int=25							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=5							,					SCREAMING_SNAKE_CASE__							:					int=True							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=25							,					):
          lowerCamelCase__           =		compute_mauve(
              p_text=SCREAMING_SNAKE_CASE__							,					q_text=SCREAMING_SNAKE_CASE__							,					p_features=SCREAMING_SNAKE_CASE__							,					q_features=SCREAMING_SNAKE_CASE__							,					p_tokens=SCREAMING_SNAKE_CASE__							,					q_tokens=SCREAMING_SNAKE_CASE__							,					num_buckets=SCREAMING_SNAKE_CASE__							,					pca_max_data=SCREAMING_SNAKE_CASE__							,					kmeans_explained_var=SCREAMING_SNAKE_CASE__							,					kmeans_num_redo=SCREAMING_SNAKE_CASE__							,					kmeans_max_iter=SCREAMING_SNAKE_CASE__							,					featurize_model_name=SCREAMING_SNAKE_CASE__							,					device_id=SCREAMING_SNAKE_CASE__							,					max_text_length=SCREAMING_SNAKE_CASE__							,					divergence_curve_discretization_size=SCREAMING_SNAKE_CASE__							,					mauve_scaling_factor=SCREAMING_SNAKE_CASE__							,					verbose=SCREAMING_SNAKE_CASE__							,					seed=SCREAMING_SNAKE_CASE__							,					)
          return out







"""simple docstring"""


def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      def count_of_possible_combinations(_a:     int							) -> int:
            if target < 0:
                  return 0
            if target == 0:
                  return 1
            return sum(count_of_possible_combinations(target - item							) for item in array							)

      return count_of_possible_combinations(_a							)



def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      def count_of_possible_combinations_with_dp_array(
          _a:     int		,   _a:     list[int]							) -> int:
            if target < 0:
                  return 0
            if target == 0:
                  return 1
            if dp_array[target] != -1:
                  return dp_array[target]
            lowerCamelCase__           =		sum(
                count_of_possible_combinations_with_dp_array(target - item		,   _a							)
                for item in array							)
            lowerCamelCase__           =		answer
            return answer

      lowerCamelCase__           =		[-1] * (target + 1)
      return count_of_possible_combinations_with_dp_array(_a		,   _a							)



def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[0] * (target + 1)
      lowerCamelCase__           =		1

      for i in range(1		,   target + 1							):
            for j in range(_a							):
                  if i - array[j] >= 0:
                        dp_array[i] += dp_array[i - array[j]]

      return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _snake_case          =							3
   _snake_case          =							5
   _snake_case          =							[1, 2, 5]
   print(combination_sum_iv(n, array, target))



