




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_      			=       """\
@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_      			=       """\
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_      			=       """
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       lowercase(  datasets.Metric				):







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	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 				UpperCamelCase_     (       self:	Optional[Any],   a_:	str,   a_:	List[Any],   a_:	List[Any]=None,   a_:	Union[str, Any]=None,   a_:	List[str]=None,   a_:	Any=None,   a_:	Any="auto",   a_:	Optional[Any]=-1,   a_:	Any=0.9,   a_:	Optional[Any]=5,   a_:	List[str]=500,   a_:	Optional[Any]="gpt2-large",   a_:	Tuple=-1,   a_:	Optional[Any]=1_024,   a_:	Optional[Any]=25,   a_:	Optional[Any]=5,   a_:	Optional[int]=True,   a_:	Optional[int]=25,   ):




   '''simple docstring'''



   _snake_case							:	str			=						compute_mauve(
       p_text=snake_case__,   q_text=snake_case__,   p_features=snake_case__,   q_features=snake_case__,   p_tokens=snake_case__,   q_tokens=snake_case__,   num_buckets=snake_case__,   pca_max_data=snake_case__,   kmeans_explained_var=snake_case__,   kmeans_num_redo=snake_case__,   kmeans_max_iter=snake_case__,   featurize_model_name=snake_case__,   device_id=snake_case__,   max_text_length=snake_case__,   divergence_curve_discretization_size=snake_case__,   mauve_scaling_factor=snake_case__,   verbose=snake_case__,   seed=snake_case__,   )
   return out








"""simple docstring"""


from ...processing_utils import ProcessorMixin






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      ["image_processor", "feature_extractor"]
 lowercase__				      =      "TvltImageProcessor"
 lowercase__				      =      "TvltFeatureExtractor"


 def __init__(       self:	Dict,   a_:	Union[str, Any],   a_:	Union[str, Any]	):




   '''simple docstring'''



   super().__init__(image_processor=a_,   feature_extractor=a_	)

   _snake_case							:	Any			=						image_processor
   _snake_case							:	Dict			=						feature_extractor


 def __call__(       self:	int,   a_:	str=None,   a_:	Tuple=None,   a_:	Dict=None,   a_:	str=None,   a_:	Optional[int]=False,   a_:	Tuple=False,   *a_:	List[str],   **a_:	int,   ):




   '''simple docstring'''



   if images is None and audio is None:
     raise ValueError("""You need to specify either an `images` or `audio` input to process."""	)

   _snake_case							:	Optional[int]			=						None
   if images is not None:
     _snake_case							:	Tuple			=						self.image_processor(a_,   mask_pixel=a_,   *a_,   **a_	)
   if images_mixed is not None:
     _snake_case							:	Optional[int]			=						self.image_processor(a_,   is_mixed=a_,   *a_,   **a_	)
   if audio is not None:
     _snake_case							:	Any			=						self.feature_extractor(
         a_,   *a_,   sampling_rate=a_,   mask_audio=a_,   **a_	)

   _snake_case							:	List[str]			=						{}
   if audio is not None:
     output_dict.update(a_	)
   if images is not None:
     output_dict.update(a_	)
   if images_mixed_dict is not None:
     output_dict.update(a_	)
   return output_dict


 @property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.image_processor.model_input_names
   _snake_case							:	List[str]			=						self.feature_extractor.model_input_names
   return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names	)	)


