



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


__SCREAMING_SNAKE_CASE           =  """\
@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}
}

"""

__SCREAMING_SNAKE_CASE           =  """\
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
"""

__SCREAMING_SNAKE_CASE           =  """
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 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				int:
									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      :			Optional[Any]	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Union[str, Any]=None	,	__lowerCamelCase      :			Union[str, Any]=None	,	__lowerCamelCase      :			Union[str, Any]=None	,	__lowerCamelCase      :			int=None	,	__lowerCamelCase      :			Union[str, Any]="auto"	,	__lowerCamelCase      :			List[Any]=-1	,	__lowerCamelCase      :			Dict=0.9	,	__lowerCamelCase      :			List[str]=5	,	__lowerCamelCase      :			List[Any]=5_00	,	__lowerCamelCase      :			str="gpt2-large"	,	__lowerCamelCase      :			Optional[Any]=-1	,	__lowerCamelCase      :			Optional[int]=10_24	,	__lowerCamelCase      :			Optional[Any]=25	,	__lowerCamelCase      :			Dict=5	,	__lowerCamelCase      :			Union[str, Any]=True	,	__lowerCamelCase      :			Dict=25	,	)      ->				List[Any]:
									A							:					List[Any]              =						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




from sklearn.metrics import recall_score

import datasets


__SCREAMING_SNAKE_CASE           =  """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


__SCREAMING_SNAKE_CASE           =  """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


__SCREAMING_SNAKE_CASE           =  """
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,_KWARGS_DESCRIPTION						)
class 	lowerCamelCase_    ( datasets.Metric						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				str:
									return datasets.MetricInfo(
									    description=_DESCRIPTION	,	citation=_CITATION	,	inputs_description=_KWARGS_DESCRIPTION	,	features=datasets.Features(
									        {
									            "predictions": datasets.Sequence(datasets.Value("int32" ) ),
									            "references": datasets.Sequence(datasets.Value("int32" ) ),
									        }
									        if self.config_name == "multilabel"
									        else {
									            "predictions": datasets.Value("int32" ),
									            "references": datasets.Value("int32" ),
									        } )	,	reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"]	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			str=None	,	__lowerCamelCase      :			List[Any]=1	,	__lowerCamelCase      :			Tuple="binary"	,	__lowerCamelCase      :			Tuple=None	,	__lowerCamelCase      :			Tuple="warn"	,	)      ->				Optional[Any]:
									A							:					str              =						recall_score(
									    __lowerCamelCase	,	__lowerCamelCase	,	labels=__lowerCamelCase	,	pos_label=__lowerCamelCase	,	average=__lowerCamelCase	,	sample_weight=__lowerCamelCase	,	zero_division=__lowerCamelCase	,	)
									return {"recall": float(__lowerCamelCase ) if score.size == 1 else score}