





import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


UpperCAmelCase_           =	False
try:
   UpperCAmelCase_           =	_is_package_available("google.colab")
except ModuleNotFoundError:
   pass




@input.register
class 			__UpperCamelCase    :





     def __init__(     self				,							_UpperCamelCase = None				,							_UpperCamelCase = []					):
        _UpperCAmelCase           =   0
        _UpperCAmelCase           =   choices
        _UpperCAmelCase           =   prompt
        if sys.platform == "win32":
           _UpperCAmelCase           =   '*'
        else:
           _UpperCAmelCase           =   '➔ '





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = ""					):
        if sys.platform != "win32":
           writeColor(self.choices[index]				,							32				,							_UpperCAmelCase					)
        else:
           forceWrite(self.choices[index]				,							_UpperCAmelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if index == self.position:
           forceWrite(f''' {self.arrow_char} '''					)
           self.write_choice(_UpperCAmelCase					)
        else:
           forceWrite(f'''    {self.choices[index]}'''					)
        reset_cursor()





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = 1					):
        _UpperCAmelCase           =   self.position
        if direction == Direction.DOWN:
           if self.position + 1 >= len(self.choices					):
              return
           self.position += num_spaces
        else:
           if self.position - 1 < 0:
              return
           self.position -= num_spaces
        clear_line()
        self.print_choice(_UpperCAmelCase					)
        move_cursor(_UpperCAmelCase				,							direction.name					)
        self.print_choice(self.position					)





     @input.mark(KEYMAP['''up''']					)
     def 		UpperCamelCase(     self					):
        self.move_direction(Direction.UP					)





     @input.mark(KEYMAP['''down''']					)
     def 		UpperCamelCase(     self					):
        self.move_direction(Direction.DOWN					)





     @input.mark(KEYMAP['''newline''']					)
     def 		UpperCamelCase(     self					):
        move_cursor(len(self.choices					) - self.position				,							'''DOWN'''					)
        return self.position





     @input.mark(KEYMAP['''interrupt''']					)
     def 		UpperCamelCase(     self					):
        move_cursor(len(self.choices					) - self.position				,							'''DOWN'''					)
        raise KeyboardInterrupt





     @input.mark_multiple(*[KEYMAP[str(_UpperCAmelCase					)] for number in range(10					)]					)
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   int(chr(self.current_selection					)					)
        _UpperCAmelCase           =   index - self.position
        if index == self.position:
           return
        if index < len(self.choices					):
           if self.position > index:
              self.move_direction(Direction.UP				,							-movement					)
           elif self.position < index:
              self.move_direction(Direction.DOWN				,							_UpperCAmelCase					)
           else:
              return
        else:
           return





     def 		UpperCamelCase(     self				,							_UpperCamelCase = 0					):
        if self.prompt:
           linebreak()
           forceWrite(self.prompt				,							'''\n'''					)
           if in_colab:
              forceWrite('''Please input a choice index (starting from 0), and press enter'''				,							'''\n'''					)
           else:
              forceWrite('''Please select a choice using the arrow or number keys, and selecting with enter'''				,							'''\n'''					)
        _UpperCAmelCase           =   default_choice
        for i in range(len(self.choices					)					):
           self.print_choice(_UpperCAmelCase					)
           forceWrite('''\n'''					)
        move_cursor(len(self.choices					) - self.position				,							'''UP'''					)
        with cursor.hide():
           while True:
              if in_colab:
                 try:
                    _UpperCAmelCase           =   int(builtins.input()					)
                 except ValueError:
                    _UpperCAmelCase           =   default_choice
              else:
                 _UpperCAmelCase           =   self.handle_input()
              if choice is not None:
                 reset_cursor()
                 for _ in range(len(self.choices					) + 1					):
                    move_cursor(1				,							'''UP'''					)
                    clear_line()
                 self.write_choice(_UpperCAmelCase				,							'''\n'''					)
                 return choice




'''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       __magic_name__		(       datasets.Metric  ):







							def 				__lowercase     (				self   :			Optional[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 				__lowercase     (				self   :			Tuple      ,_UpperCAmelCase   :			Optional[Any]      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			Any=None      ,_UpperCAmelCase   :			Tuple=None      ,_UpperCAmelCase   :			List[str]=None      ,_UpperCAmelCase   :			Optional[Any]=None      ,_UpperCAmelCase   :			Any="auto"      ,_UpperCAmelCase   :			Tuple=-1      ,_UpperCAmelCase   :			Union[str, Any]=0.9      ,_UpperCAmelCase   :			List[Any]=5      ,_UpperCAmelCase   :			Optional[int]=500      ,_UpperCAmelCase   :			int="gpt2-large"      ,_UpperCAmelCase   :			int=-1      ,_UpperCAmelCase   :			Tuple=1024      ,_UpperCAmelCase   :			Any=25      ,_UpperCAmelCase   :			int=5      ,_UpperCAmelCase   :			Optional[int]=True      ,_UpperCAmelCase   :			Optional[int]=25      ,):
									_a	: Any								=				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




