'''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


_UpperCAmelCase    :     Dict         =       '''\
@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}
}

'''

_UpperCAmelCase    :     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
'''

_UpperCAmelCase    :     Dict         =       '''
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      :			Tuple     )  ->			List[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, UpperCamelCase__      :			Dict, UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			Optional[int]=None, UpperCamelCase__      :			Any=None, UpperCamelCase__      :			Tuple=None, UpperCamelCase__      :			str=None, UpperCamelCase__      :			Any="auto", UpperCamelCase__      :			Optional[int]=-1, UpperCamelCase__      :			Dict=0.9, UpperCamelCase__      :			List[str]=5, UpperCamelCase__      :			Dict=5_00, UpperCamelCase__      :			Optional[int]="gpt2-large", UpperCamelCase__      :			Dict=-1, UpperCamelCase__      :			Union[str, Any]=10_24, UpperCamelCase__      :			str=25, UpperCamelCase__      :			Any=5, UpperCamelCase__      :			str=True, UpperCamelCase__      :			List[Any]=25, )  ->			Tuple:
        _A    		=  compute_mauve(
            p_text=UpperCamelCase__, q_text=UpperCamelCase__, p_features=UpperCamelCase__, q_features=UpperCamelCase__, p_tokens=UpperCamelCase__, q_tokens=UpperCamelCase__, num_buckets=UpperCamelCase__, pca_max_data=UpperCamelCase__, kmeans_explained_var=UpperCamelCase__, kmeans_num_redo=UpperCamelCase__, kmeans_max_iter=UpperCamelCase__, featurize_model_name=UpperCamelCase__, device_id=UpperCamelCase__, max_text_length=UpperCamelCase__, divergence_curve_discretization_size=UpperCamelCase__, mauve_scaling_factor=UpperCamelCase__, verbose=UpperCamelCase__, seed=UpperCamelCase__, )
        return out




'''simple docstring'''






import os
from pathlib import Path





def  _SCREAMING_SNAKE_CASE		(      ):
     from torch.utils.cpp_extension import load

     _A    		=  Path(__snake_case				).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
     _A    		=  [
         root / filename
         for filename in [
             'vision.cpp',
             os.path.join('cpu'   ,		'ms_deform_attn_cpu.cpp'				),
             os.path.join('cuda'   ,		'ms_deform_attn_cuda.cu'				),
         ]
     ]

     load(
         'MultiScaleDeformableAttention'   ,		__snake_case   ,		with_cuda=__snake_case   ,		extra_include_paths=[str(__snake_case				)]   ,		extra_cflags=['-DWITH_CUDA=1']   ,		extra_cuda_cflags=[
             '-DCUDA_HAS_FP16=1',
             '-D__CUDA_NO_HALF_OPERATORS__',
             '-D__CUDA_NO_HALF_CONVERSIONS__',
             '-D__CUDA_NO_HALF2_OPERATORS__',
         ]   ,		)

     import MultiScaleDeformableAttention as MSDA

     return MSDA



