



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				:    Optional[int]     						="""\
@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				:    str     						="""\
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				:    str     						="""
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 		lowerCamelCase__      (						datasets.Metric     ):




					'''simple docstring'''



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



						'''simple docstring'''





						_lowercase					:       List[Any]       =		compute_mauve(
						    p_text=__A     ,  q_text=__A     ,  p_features=__A     ,  q_features=__A     ,  p_tokens=__A     ,  q_tokens=__A     ,  num_buckets=__A     ,  pca_max_data=__A     ,  kmeans_explained_var=__A     ,  kmeans_num_redo=__A     ,  kmeans_max_iter=__A     ,  featurize_model_name=__A     ,  device_id=__A     ,  max_text_length=__A     ,  divergence_curve_discretization_size=__A     ,  mauve_scaling_factor=__A     ,  verbose=__A     ,  seed=__A     ,  )
						return out





'''simple docstring'''

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_A				:    Optional[int]     						=logging.get_logger(__name__)



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						["""input_features""", """is_longer"""]




					def __init__(  self				:	List[Any]     ,  UpperCamelCase_				:	List[Any]=64     ,  UpperCamelCase_				:	int=4_8000     ,  UpperCamelCase_				:	Union[str, Any]=480     ,  UpperCamelCase_				:	Any=10     ,  UpperCamelCase_				:	Optional[int]=1024     ,  UpperCamelCase_				:	Optional[int]=0.0     ,  UpperCamelCase_				:	Tuple=False     ,  UpperCamelCase_				:	float = 0     ,  UpperCamelCase_				:	float = 1_4000     ,  UpperCamelCase_				:	int = None     ,  UpperCamelCase_				:	str = "fusion"     ,  UpperCamelCase_				:	str = "repeatpad"     ,  **UpperCamelCase_				:	Optional[Any]     ,  )  ->       Dict:



						'''simple docstring'''





						super().__init__(
						    feature_size=UpperCamelCase_     ,  sampling_rate=UpperCamelCase_     ,  padding_value=UpperCamelCase_     ,  return_attention_mask=UpperCamelCase_     ,  **UpperCamelCase_     ,  )
						_lowercase					:       Tuple       =		top_db
						_lowercase					:       Any       =		truncation
						_lowercase					:       str       =		padding
						_lowercase					:       int       =		fft_window_size
						_lowercase					:       Any       =		(fft_window_size >> 1) + 1
						_lowercase					:       int       =		hop_length
						_lowercase					:       Any       =		max_length_s
						_lowercase					:       str       =		max_length_s * sampling_rate
						_lowercase					:       Any       =		sampling_rate
						_lowercase					:       List[Any]       =		frequency_min
						_lowercase					:       Tuple       =		frequency_max
						_lowercase					:       Tuple       =		mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins     ,  num_mel_filters=UpperCamelCase_     ,  min_frequency=UpperCamelCase_     ,  max_frequency=UpperCamelCase_     ,  sampling_rate=UpperCamelCase_     ,  norm=UpperCamelCase_     ,  mel_scale='htk'     ,  )
						_lowercase					:       Any       =		mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins     ,  num_mel_filters=UpperCamelCase_     ,  min_frequency=UpperCamelCase_     ,  max_frequency=UpperCamelCase_     ,  sampling_rate=UpperCamelCase_     ,  norm='slaney'     ,  mel_scale='slaney'     ,  )




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Dict[str, Any]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		copy.deepcopy(self.__dict__					)
						_lowercase					:       int       =		self.__class__.__name__
						if "mel_filters" in output:
							del output["mel_filters"]
						if "mel_filters_slaney" in output:
							del output["mel_filters_slaney"]
						return output




					def 						__UpperCAmelCase     (  self				:	Dict     ,  UpperCamelCase_				:	np.array     ,  UpperCamelCase_				:	Optional[np.array] = None					)  ->       np.ndarray:



						'''simple docstring'''





						_lowercase					:       List[str]       =		spectrogram(
						    UpperCamelCase_     ,  window_function(self.fft_window_size     ,  'hann'					)     ,  frame_length=self.fft_window_size     ,  hop_length=self.hop_length     ,  power=2.0     ,  mel_filters=UpperCamelCase_     ,  log_mel='dB'     ,  )
						return log_mel_spectrogram.T




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	Any     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Optional[int]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		np.array_split(list(range(0     ,  total_frames - chunk_frames + 1					)					)     ,  3					)
						if len(ranges[1]					) == 0:
							# if the audio is too short, we just use the first chunk
							_lowercase					:       int       =		[0]
						if len(ranges[2]					) == 0:
							# if the audio is too short, we just use the first chunk
							_lowercase					:       Union[str, Any]       =		[0]
						# randomly choose index for each part
						_lowercase					:       Tuple       =		np.random.choice(ranges[0]					)
						_lowercase					:       int       =		np.random.choice(ranges[1]					)
						_lowercase					:       Any       =		np.random.choice(ranges[2]					)

						_lowercase					:       int       =		mel[idx_front : idx_front + chunk_frames, :]
						_lowercase					:       int       =		mel[idx_middle : idx_middle + chunk_frames, :]
						_lowercase					:       Tuple       =		mel[idx_back : idx_back + chunk_frames, :]

						_lowercase					:       List[Any]       =		torch.tensor(mel[None, None, :]					)
						_lowercase					:       Optional[int]       =		torch.nn.functional.interpolate(
						    UpperCamelCase_     ,  size=[chunk_frames, 64]     ,  mode='bilinear'     ,  align_corners=UpperCamelCase_					)
						_lowercase					:       str       =		mel_shrink[0][0].numpy()
						_lowercase					:       int       =		np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,  axis=0					)
						return mel_fusion




					def 						__UpperCAmelCase     (  self				:	List[str]     ,  UpperCamelCase_				:	np.array     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	int     ,  UpperCamelCase_				:	Optional[int]					)  ->       np.array:



						'''simple docstring'''





						if waveform.shape[0] > max_length:
							if truncation == "rand_trunc":
								_lowercase					:       Tuple       =		True
								# random crop to max_length (for compatibility) -> this should be handled by self.pad
								_lowercase					:       Any       =		len(UpperCamelCase_					) - max_length
								_lowercase					:       Dict       =		np.random.randint(0     ,  overflow + 1					)
								_lowercase					:       Optional[int]       =		waveform[idx : idx + max_length]
								_lowercase					:       Dict       =		self._np_extract_fbank_features(UpperCamelCase_     ,  self.mel_filters_slaney					)[None, :]
							elif truncation == "fusion":
								_lowercase					:       List[Any]       =		self._np_extract_fbank_features(UpperCamelCase_     ,  self.mel_filters					)
								_lowercase					:       List[Any]       =		max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
								_lowercase					:       Optional[int]       =		mel.shape[0]
								if chunk_frames == total_frames:
									# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
									# In this case, we just use the whole audio.
									_lowercase					:       Optional[Any]       =		np.stack([mel, mel, mel, mel]     ,  axis=0					)
									_lowercase					:       List[Any]       =		False
								else:
									_lowercase					:       Union[str, Any]       =		self._random_mel_fusion(UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_					)
									_lowercase					:       int       =		True
							else:
								raise NotImplementedError(F'''data_truncating {truncation} not implemented'''					)

						else:
							_lowercase					:       Any       =		False
							# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
							if waveform.shape[0] < max_length:
								if padding == "repeat":
									_lowercase					:       List[Any]       =		int(max_length / len(UpperCamelCase_					)					)
									_lowercase					:       List[str]       =		np.stack(np.tile(UpperCamelCase_     ,  n_repeat + 1					)					)[:max_length]
								if padding == "repeatpad":
									_lowercase					:       Union[str, Any]       =		int(max_length / len(UpperCamelCase_					)					)
									_lowercase					:       Union[str, Any]       =		np.stack(np.tile(UpperCamelCase_     ,  UpperCamelCase_					)					)
								_lowercase					:       Dict       =		np.pad(UpperCamelCase_     ,  (0, max_length - waveform.shape[0])     ,  mode='constant'     ,  constant_values=0					)

							if truncation == "fusion":
								_lowercase					:       str       =		self._np_extract_fbank_features(UpperCamelCase_     ,  self.mel_filters					)
								_lowercase					:       Dict       =		np.stack([input_mel, input_mel, input_mel, input_mel]     ,  axis=0					)
							else:
								_lowercase					:       List[Any]       =		self._np_extract_fbank_features(UpperCamelCase_     ,  self.mel_filters_slaney					)[None, :]

						return input_mel, longer




					def __call__(  self				:	Union[str, Any]     ,  UpperCamelCase_				:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,  UpperCamelCase_				:	str = None     ,  UpperCamelCase_				:	Optional[str] = None     ,  UpperCamelCase_				:	Optional[int] = None     ,  UpperCamelCase_				:	Optional[int] = None     ,  UpperCamelCase_				:	Optional[Union[str, TensorType]] = None     ,  **UpperCamelCase_				:	Dict     ,  )  ->       BatchFeature:



						'''simple docstring'''





						_lowercase					:       Dict       =		truncation if truncation is not None else self.truncation
						_lowercase					:       int       =		padding if padding else self.padding

						if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
								raise ValueError(
								    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
								    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
								    F''' was sampled with {self.sampling_rate} and not {sampling_rate}.'''					)
						else:
							logger.warning(
							    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
							    'Failing to do so can result in silent errors that might be hard to debug.'					)

						_lowercase					:       Optional[Any]       =		isinstance(UpperCamelCase_     ,  np.ndarray					) and len(raw_speech.shape					) > 1
						if is_batched_numpy and len(raw_speech.shape					) > 2:
							raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''					)
						_lowercase					:       List[str]       =		is_batched_numpy or (
						    isinstance(UpperCamelCase_     ,  (list, tuple)					) and (isinstance(raw_speech[0]     ,  (np.ndarray, tuple, list)					))
						)

						if is_batched:
							_lowercase					:       Dict       =		[np.asarray(UpperCamelCase_     ,  dtype=np.floataa					) for speech in raw_speech]
						elif not is_batched and not isinstance(UpperCamelCase_     ,  np.ndarray					):
							_lowercase					:       Any       =		np.asarray(UpperCamelCase_     ,  dtype=np.floataa					)
						elif isinstance(UpperCamelCase_     ,  np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
							_lowercase					:       Tuple       =		raw_speech.astype(np.floataa					)

						# always return batch
						if not is_batched:
							_lowercase					:       int       =		[np.asarray(UpperCamelCase_					)]

						# convert to mel spectrogram, truncate and pad if needed.
						_lowercase					:       Optional[Any]       =		[
						    self._get_input_mel(UpperCamelCase_     ,  max_length if max_length else self.nb_max_samples     ,  UpperCamelCase_     ,  UpperCamelCase_					)
						    for waveform in raw_speech
						]

						_lowercase					:       List[Any]       =		[]
						_lowercase					:       Dict       =		[]
						for mel, longer in padded_inputs:
							input_mel.append(UpperCamelCase_					)
							is_longer.append(UpperCamelCase_					)

						if truncation == "fusion" and sum(UpperCamelCase_					) == 0:
							# if no audio is longer than 10s, then randomly select one audio to be longer
							_lowercase					:       Optional[Any]       =		np.random.randint(0     ,  len(UpperCamelCase_					)					)
							_lowercase					:       str       =		True

						if isinstance(input_mel[0]     ,  UpperCamelCase_					):
							_lowercase					:       str       =		[np.asarray(UpperCamelCase_     ,  dtype=np.floataa					) for feature in input_mel]

						# is_longer is a list of bool
						_lowercase					:       Tuple       =		[[longer] for longer in is_longer]

						_lowercase					:       Optional[Any]       =		{'input_features': input_mel, 'is_longer': is_longer}
						_lowercase					:       Optional[int]       =		BatchFeature(UpperCamelCase_					)

						if return_tensors is not None:
							_lowercase					:       List[Any]       =		input_features.convert_to_tensors(UpperCamelCase_					)

						return input_features
