


'''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					:  Any											=      "\\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"

__A					:  List[str]											=      "\\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"

__A					:  List[str]											=      "\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    __snake_case       (   datasets.Metric):







    """simple docstring"""



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






class    __snake_case       :







    """simple docstring"""



    def __init__(   self    :  int    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  int=None    ,						lowerCamelCase    :  int=None		)   ->  str:
      lowerCAmelCase_			:      str			=   data
      lowerCAmelCase_			:      Optional[Any]			=   previous
      lowerCAmelCase_			:      int			=   next_node

    def __str__(   self    :  Any		)   ->  str:
      return F'{self.data}'

    def __lowercase   (   self    :  Optional[Any]		)   ->  int:
      return self.data

    def __lowercase   (   self    :  str		)   ->  List[str]:
      return self.next





    def __lowercase   (   self    :  int		)   ->  Optional[int]:
      return self.previous







class    __snake_case       :







    """simple docstring"""



    def __init__(   self    :  Union[str, Any]    ,						lowerCamelCase    :  Optional[int]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Optional[Any]			=   head

    def __iter__(   self    :  str		)   ->  Optional[Any]:
      return self





    def __lowercase   (   self    :  Union[str, Any]		)   ->  Dict:
      if not self.current:
        raise StopIteration
      else:
        lowerCAmelCase_			:      Dict			=   self.current.get_data()
        lowerCAmelCase_			:      Tuple			=   self.current.get_next()
        return value







class    __snake_case       :







    """simple docstring"""



    def __init__(   self    :  Union[str, Any]		)   ->  Any:
      lowerCAmelCase_			:      Optional[Any]			=   None  # First node in list
      lowerCAmelCase_			:      Optional[Any]			=   None  # Last node in list

    def __str__(   self    :  Optional[int]		)   ->  Dict:
      lowerCAmelCase_			:      str			=   self.head
      lowerCAmelCase_			:      Tuple			=   []
      while current is not None:
        nodes.append(current.get_data()		)
        lowerCAmelCase_			:      str			=   current.get_next()
      return " ".join(str(lowerCamelCase		) for node in nodes		)

    def __contains__(   self    :  List[Any]    ,						lowerCamelCase    :  int		)   ->  List[str]:
      lowerCAmelCase_			:      List[str]			=   self.head
      while current:
        if current.get_data() == value:
          return True
        lowerCAmelCase_			:      List[Any]			=   current.get_next()
      return False

    def __iter__(   self    :  str		)   ->  Optional[Any]:
      return LinkedListIterator(self.head		)

    def __lowercase   (   self    :  Dict		)   ->  Optional[int]:
      if self.head:
        return self.head.get_data()
      return None

    def __lowercase   (   self    :  List[str]		)   ->  Optional[Any]:
      if self.tail:
        return self.tail.get_data()
      return None

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  Node		)   ->  None:
      if self.head is None:
        lowerCAmelCase_			:      Union[str, Any]			=   node
        lowerCAmelCase_			:      List[str]			=   node
      else:
        self.insert_before_node(self.head    ,						lowerCamelCase		)

    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  Node		)   ->  None:
      if self.head is None:
        self.set_head(lowerCamelCase		)
      else:
        self.insert_after_node(self.tail    ,						lowerCamelCase		)

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  int		)   ->  None:
      lowerCAmelCase_			:      int			=   Node(lowerCamelCase		)
      if self.head is None:
        self.set_head(lowerCamelCase		)
      else:
        self.set_tail(lowerCamelCase		)

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  Node    ,						lowerCamelCase    :  Node		)   ->  None:
      lowerCAmelCase_			:      Optional[int]			=   node
      lowerCAmelCase_			:      List[Any]			=   node.previous

      if node.get_previous() is None:
        lowerCAmelCase_			:      Tuple			=   node_to_insert
      else:
        lowerCAmelCase_			:      Dict			=   node_to_insert

      lowerCAmelCase_			:      Optional[int]			=   node_to_insert

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  Node    ,						lowerCamelCase    :  Node		)   ->  None:
      lowerCAmelCase_			:      Optional[int]			=   node
      lowerCAmelCase_			:      Tuple			=   node.next

      if node.get_next() is None:
        lowerCAmelCase_			:      Tuple			=   node_to_insert
      else:
        lowerCAmelCase_			:      Tuple			=   node_to_insert

      lowerCAmelCase_			:      Optional[Any]			=   node_to_insert

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  int		)   ->  None:
      lowerCAmelCase_			:      List[str]			=   1
      lowerCAmelCase_			:      Tuple			=   Node(lowerCamelCase		)
      lowerCAmelCase_			:      List[Any]			=   self.head
      while node:
        if current_position == position:
          self.insert_before_node(lowerCamelCase    ,						lowerCamelCase		)
          return
        current_position += 1
        lowerCAmelCase_			:      str			=   node.next
      self.insert_after_node(self.tail    ,						lowerCamelCase		)

    def __lowercase   (   self    :  int    ,						lowerCamelCase    :  int		)   ->  Node:
      lowerCAmelCase_			:      List[Any]			=   self.head
      while node:
        if node.get_data() == item:
          return node
        lowerCAmelCase_			:      List[Any]			=   node.get_next()
      raise Exception("""Node not found"""		)

    def __lowercase   (   self    :  str    ,						lowerCamelCase    :  str		)   ->  int:
      if (node := self.get_node(lowerCamelCase		)) is not None:
        if node == self.head:
          lowerCAmelCase_			:      Any			=   self.head.get_next()

        if node == self.tail:
          lowerCAmelCase_			:      Optional[int]			=   self.tail.get_previous()

        self.remove_node_pointers(lowerCamelCase		)

    @staticmethod
    def __lowercase   (   lowerCamelCase    :  Node		)   ->  None:
      if node.get_next():
        lowerCAmelCase_			:      Tuple			=   node.previous

      if node.get_previous():
        lowerCAmelCase_			:      Any			=   node.next

      lowerCAmelCase_			:      List[Any]			=   None
      lowerCAmelCase_			:      Any			=   None





    def __lowercase   (   self    :  str		)   ->  Optional[Any]:
      return self.head is None







def 				UpperCamelCase_			(    ):
  '''simple docstring'''







if __name__ == "__main__":
  import doctest

  doctest.testmod()



