



"""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


lowerCAmelCase__              =							'''\
@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}
}

'''

lowerCAmelCase__              =							'''\
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
'''

lowerCAmelCase__              =							'''
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 			__snake_case			(      datasets.Metric):




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, 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 		SCREAMING_SNAKE_CASE							(					self	:		List[str]		,   __lowerCAmelCase	:		List[str]		,   __lowerCAmelCase	:		Optional[Any]		,   __lowerCAmelCase	:		Optional[Any]=None		,   __lowerCAmelCase	:		str=None		,   __lowerCAmelCase	:		Optional[Any]=None		,   __lowerCAmelCase	:		List[Any]=None		,   __lowerCAmelCase	:		Optional[int]="auto"		,   __lowerCAmelCase	:		List[Any]=-1		,   __lowerCAmelCase	:		Optional[int]=0.9		,   __lowerCAmelCase	:		str=5		,   __lowerCAmelCase	:		Dict=5_0_0		,   __lowerCAmelCase	:		Any="gpt2-large"		,   __lowerCAmelCase	:		Union[str, Any]=-1		,   __lowerCAmelCase	:		Optional[Any]=1_0_2_4		,   __lowerCAmelCase	:		str=2_5		,   __lowerCAmelCase	:		Union[str, Any]=5		,   __lowerCAmelCase	:		List[str]=True		,   __lowerCAmelCase	:		str=2_5		,   ):






         """simple docstring"""






         _lowerCamelCase		:							int            =					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




__a         =   'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'




def 							a		(				snake_case__:   bytes   ):


				'''simple docstring'''
				# Make sure the supplied data is a bytes-like object
				if not isinstance(snake_case__		,  snake_case__   ):
								lowercase_	    =   F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
								raise TypeError(snake_case__   )

				lowercase_	    =   ''''''.join(bin(snake_case__   )[2:].zfill(8   ) for byte in data   )

				lowercase_	    =   len(snake_case__   ) % 6 != 0

				if padding_needed:
								# The padding that will be added later
								lowercase_	    =   B'''=''' * ((6 - len(snake_case__   ) % 6) // 2)

								# Append binary_stream with arbitrary binary digits (0's by default) to make its
								# length a multiple of 6.
								binary_stream += "0" * (6 - len(snake_case__   ) % 6)
				else:
								lowercase_	    =   B''''''

				# Encode every 6 binary digits to their corresponding Base64 character
				return (
				    "".join(
				        B64_CHARSET[int(binary_stream[index : index + 6]		,  2   )]
				        for index in range(0		,  len(snake_case__   )		,  6   )   ).encode()
				    + padding
				)




def 							a		(				snake_case__:   str   ):


				'''simple docstring'''
				# Make sure encoded_data is either a string or a bytes-like object
				if not isinstance(snake_case__		,  snake_case__   ) and not isinstance(snake_case__		,  snake_case__   ):
								lowercase_	    =   (
								    '''argument should be a bytes-like object or ASCII string, '''
								    F'''not \'{encoded_data.__class__.__name__}\''''
								)
								raise TypeError(snake_case__   )

				# In case encoded_data is a bytes-like object, make sure it contains only
				# ASCII characters so we convert it to a string object
				if isinstance(snake_case__		,  snake_case__   ):
								try:
												lowercase_	    =   encoded_data.decode('''utf-8'''   )
								except UnicodeDecodeError:
												raise ValueError('''base64 encoded data should only contain ASCII characters'''   )

				lowercase_	    =   encoded_data.count('''='''   )

				# Check if the encoded string contains non base64 characters
				if padding:
								assert all(
								    char in B64_CHARSET for char in encoded_data[:-padding]   ), "Invalid base64 character(s) found."
				else:
								assert all(
								    char in B64_CHARSET for char in encoded_data   ), "Invalid base64 character(s) found."

				# Check the padding
				assert len(snake_case__   ) % 4 == 0 and padding < 3, "Incorrect padding"

				if padding:
								# Remove padding if there is one
								lowercase_	    =   encoded_data[:-padding]

								lowercase_	    =   ''''''.join(
								    bin(B64_CHARSET.index(snake_case__   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
				else:
								lowercase_	    =   ''''''.join(
								    bin(B64_CHARSET.index(snake_case__   )   )[2:].zfill(6   ) for char in encoded_data   )

				lowercase_	    =   [
				    int(binary_stream[index : index + 8]		,  2   )
				    for index in range(0		,  len(snake_case__   )		,  8   )
				]

				return bytes(snake_case__   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




