



from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)



class      _UpperCAmelCase      (							A__  ):




     def __init__(					self			,    a__			,    a__=None			,    a__=None			,    a__=0):
         A__					=       1.0 if scale is None else scale
         A__					=       0.0 if loc is None else loc

         super().__init__(a__			,    [AffineTransform(loc=self.loc			,    scale=self.scale			,    event_dim=a__)])




     @property
     def 		snake_case_		(					self):
         return self.base_dist.mean * self.scale + self.loc




     @property
     def 		snake_case_		(					self):
         return self.base_dist.variance * self.scale**2




     @property
     def 		snake_case_		(					self):
         return self.variance.sqrt()







class      _UpperCAmelCase      (							nn.Module  ):




     def __init__(					self			,    a__			,    a__			,    a__			,    **a__):
         super().__init__(**a__)
         A__					=       args_dim
         A__					=       nn.ModuleList([nn.Linear(a__			,    a__) for dim in args_dim.values()])
         A__					=       domain_map




     def 		snake_case_		(					self			,    a__):
         A__					=       [proj(a__) for proj in self.proj]

         return self.domain_map(*a__)







class      _UpperCAmelCase      (							nn.Module  ):




     def __init__(					self			,    a__):
         super().__init__()
         A__					=       function




     def 		snake_case_		(					self			,    a__			,    *a__):
         return self.function(a__			,    *a__)







class      _UpperCAmelCase      :
     UpperCamelCase__		     =					42
     UpperCamelCase__		     =					42
     UpperCamelCase__		     =					42




     def __init__(					self			,    a__ = 1):
         A__					=       dim
         A__					=       {k: dim * self.args_dim[k] for k in self.args_dim}




     def 		snake_case_		(					self			,    a__):
         if self.dim == 1:
             return self.distribution_class(*a__)
         else:
             return Independent(self.distribution_class(*a__)			,    1)




     def 		snake_case_		(					self			,    a__			,    a__ = None			,    a__ = None			,    ):
         A__					=       self._base_distribution(a__)
         if loc is None and scale is None:
             return distr
         else:
             return AffineTransformed(a__			,    loc=a__			,    scale=a__			,    event_dim=self.event_dim)




     @property
     def 		snake_case_		(					self):
         return () if self.dim == 1 else (self.dim,)




     @property
     def 		snake_case_		(					self):
         return len(self.event_shape)




     @property
     def 		snake_case_		(					self):
         return 0.0




     def 		snake_case_		(					self			,    a__):
         return ParameterProjection(
             in_features=a__			,    args_dim=self.args_dim			,    domain_map=LambdaLayer(self.domain_map)			,    )




     def 		snake_case_		(					self			,    *a__):
         raise NotImplementedError()




     @staticmethod
     def 		snake_case_		(					a__):
         return (x + torch.sqrt(torch.square(a__) + 4.0)) / 2.0







class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					{"df": 1, "loc": 1, "scale": 1}
     UpperCamelCase__		     =					StudentT




     @classmethod
     def 		snake_case_		(					cls			,    a__			,    a__			,    a__):
         A__					=       cls.squareplus(a__).clamp_min(torch.finfo(scale.dtype).eps)
         A__					=       2.0 + cls.squareplus(a__)
         return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)







class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					{"loc": 1, "scale": 1}
     UpperCamelCase__		     =					Normal




     @classmethod
     def 		snake_case_		(					cls			,    a__			,    a__):
         A__					=       cls.squareplus(a__).clamp_min(torch.finfo(scale.dtype).eps)
         return loc.squeeze(-1), scale.squeeze(-1)







class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					{"total_count": 1, "logits": 1}
     UpperCamelCase__		     =					NegativeBinomial




     @classmethod
     def 		snake_case_		(					cls			,    a__			,    a__):
         A__					=       cls.squareplus(a__)
         return total_count.squeeze(-1), logits.squeeze(-1)




     def 		snake_case_		(					self			,    a__):
         A__			,						A__					=       distr_args
         if self.dim == 1:
             return self.distribution_class(total_count=a__			,    logits=a__)
         else:
             return Independent(self.distribution_class(total_count=a__			,    logits=a__)			,    1)




     def 		snake_case_		(					self			,    a__			,    a__ = None			,    a__ = None):
         A__			,						A__					=       distr_args

         if scale is not None:
             # See scaling property of Gamma.
             logits += scale.log()

         return self._base_distribution((total_count, logits))





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


_lowercase    	=		"\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n  booktitle = {NeurIPS},\n  year      = {2021}\n}\n\n"

_lowercase    	=		"\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"

_lowercase    	=		"\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    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'].\n    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\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: \"c\" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric('mauve')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n"



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




     def 		snake_case_		(					self):
         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 		snake_case_		(					self			,    a__			,    a__			,    a__=None			,    a__=None			,    a__=None			,    a__=None			,    a__="auto"			,    a__=-1			,    a__=0.9			,    a__=5			,    a__=5_0_0			,    a__="gpt2-large"			,    a__=-1			,    a__=1_0_2_4			,    a__=2_5			,    a__=5			,    a__=True			,    a__=2_5			,    ):
         A__					=       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
