




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	:       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}
}

"""

__lowerCAmelCase	:       Any     =	"""\
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	:       Optional[Any]     =	"""
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     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  str							)      ->  Optional[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 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  List[Any]=None      ,   __lowerCamelCase       :  List[str]=None      ,   __lowerCamelCase       :  Union[str, Any]=None      ,   __lowerCamelCase       :  int=None      ,   __lowerCamelCase       :  Tuple="auto"      ,   __lowerCamelCase       :  Dict=-1      ,   __lowerCamelCase       :  Any=0.9      ,   __lowerCamelCase       :  Dict=5      ,   __lowerCamelCase       :  Tuple=5_00      ,   __lowerCamelCase       :  List[str]="gpt2-large"      ,   __lowerCamelCase       :  str=-1      ,   __lowerCamelCase       :  str=10_24      ,   __lowerCamelCase       :  Union[str, Any]=25      ,   __lowerCamelCase       :  Optional[int]=5      ,   __lowerCamelCase       :  Union[str, Any]=True      ,   __lowerCamelCase       :  Optional[int]=25      ,   )      ->  Any:
							a					=    compute_mauve(
							    p_text=snake_case__      ,   q_text=snake_case__      ,   p_features=snake_case__      ,   q_features=snake_case__      ,   p_tokens=snake_case__      ,   q_tokens=snake_case__      ,   num_buckets=snake_case__      ,   pca_max_data=snake_case__      ,   kmeans_explained_var=snake_case__      ,   kmeans_num_redo=snake_case__      ,   kmeans_max_iter=snake_case__      ,   featurize_model_name=snake_case__      ,   device_id=snake_case__      ,   max_text_length=snake_case__      ,   divergence_curve_discretization_size=snake_case__      ,   mauve_scaling_factor=snake_case__      ,   verbose=snake_case__      ,   seed=snake_case__      ,   )
							return out






import math

import flax.linen as nn
import jax.numpy as jnp




def     __magic_name__       (				A	:      jnp.ndarray,						A	:      int,						A	:      float = 1,						A	:      float = 1,						A	:      float = 1.0E4,						A	:      bool = False,						A	:      float = 1.0,						):






			'''simple docstring'''







			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
			a					=    float(embedding_dim // 2     )
			a					=    math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			a					=    min_timescale * jnp.exp(jnp.arange(A,						dtype=jnp.floataa     ) * -log_timescale_increment     )
			a					=    jnp.expand_dims(A,						1     ) * jnp.expand_dims(A,						0     )

			# scale embeddings
			a					=    scale * emb

			if flip_sin_to_cos:
						a					=    jnp.concatenate([jnp.cos(A     ), jnp.sin(A     )],						axis=1     )
			else:
						a					=    jnp.concatenate([jnp.sin(A     ), jnp.cos(A     )],						axis=1     )
			a					=    jnp.reshape(A,						[jnp.shape(A     )[0], embedding_dim]     )
			return signal




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							jnp.dtype											=      jnp.floataa





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  Optional[Any]							)      ->  List[Any]:
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_1"							)(__lowerCamelCase							)
							a					=    nn.silu(__lowerCamelCase							)
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_2"							)(__lowerCamelCase							)
							return temb




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							bool											=      False
				SCREAMING_SNAKE_CASE_ :							float											=      1





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  int							)      ->  Union[str, Any]:
							return get_sinusoidal_embeddings(
							    __lowerCamelCase      ,   embedding_dim=self.dim      ,   flip_sin_to_cos=self.flip_sin_to_cos      ,   freq_shift=self.freq_shift							)






