






from ...configuration_utils import PretrainedConfig


__lowerCamelCase      :    str   		=   {
    """google/tapas-base-finetuned-sqa""": (
        """https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wtq""": (
        """https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wikisql-supervised""": (
        """https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-tabfact""": (
        """https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
    ),
}



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"tapas"



					def __init__(      self  :						Any							,	__A  :						str=3_0_5_2_2							,	__A  :						Optional[Any]=7_6_8							,	__A  :						Optional[Any]=1_2							,	__A  :						Tuple=1_2							,	__A  :						Optional[Any]=3_0_7_2							,	__A  :						Optional[Any]="gelu"							,	__A  :						Optional[int]=0.1							,	__A  :						Dict=0.1							,	__A  :						Optional[Any]=1_0_2_4							,	__A  :						Optional[Any]=[3, 2_5_6, 2_5_6, 2, 2_5_6, 2_5_6, 1_0]							,	__A  :						List[Any]=0.0_2							,	__A  :						Union[str, Any]=1e-1_2							,	__A  :						List[Any]=0							,	__A  :						Dict=1_0.0							,	__A  :						Optional[int]=0							,	__A  :						Dict=1.0							,	__A  :						int=None							,	__A  :						Dict=1.0							,	__A  :						Dict=False							,	__A  :						List[str]=None							,	__A  :						List[Any]=1.0							,	__A  :						Dict=1.0							,	__A  :						str=False							,	__A  :						Optional[Any]=False							,	__A  :						List[str]="ratio"							,	__A  :						List[Any]=None							,	__A  :						str=None							,	__A  :						Dict=6_4							,	__A  :						int=3_2							,	__A  :						int=False							,	__A  :						Dict=True							,	__A  :						List[str]=False							,	__A  :						Dict=False							,	__A  :						Dict=True							,	__A  :						Dict=False							,	__A  :						str=None							,	__A  :						int=None							,	**__A  :						int							,	):
						super().__init__(pad_token_id=__A							,	**__A )

						# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
						snake_case__				:   Optional[int]     =						vocab_size
						snake_case__				:   int     =						hidden_size
						snake_case__				:   Dict     =						num_hidden_layers
						snake_case__				:   Tuple     =						num_attention_heads
						snake_case__				:   str     =						hidden_act
						snake_case__				:   Optional[Any]     =						intermediate_size
						snake_case__				:   Tuple     =						hidden_dropout_prob
						snake_case__				:   Dict     =						attention_probs_dropout_prob
						snake_case__				:   Tuple     =						max_position_embeddings
						snake_case__				:   Optional[int]     =						type_vocab_sizes
						snake_case__				:   Optional[Any]     =						initializer_range
						snake_case__				:   Tuple     =						layer_norm_eps

						# Fine-tuning task hyperparameters
						snake_case__				:   int     =						positive_label_weight
						snake_case__				:   Any     =						num_aggregation_labels
						snake_case__				:   Optional[int]     =						aggregation_loss_weight
						snake_case__				:   Optional[int]     =						use_answer_as_supervision
						snake_case__				:   int     =						answer_loss_importance
						snake_case__				:   Optional[int]     =						use_normalized_answer_loss
						snake_case__				:   Optional[Any]     =						huber_loss_delta
						snake_case__				:   int     =						temperature
						snake_case__				:   Any     =						aggregation_temperature
						snake_case__				:   List[str]     =						use_gumbel_for_cells
						snake_case__				:   Optional[int]     =						use_gumbel_for_aggregation
						snake_case__				:   int     =						average_approximation_function
						snake_case__				:   Optional[int]     =						cell_selection_preference
						snake_case__				:   Any     =						answer_loss_cutoff
						snake_case__				:   int     =						max_num_rows
						snake_case__				:   List[Any]     =						max_num_columns
						snake_case__				:   int     =						average_logits_per_cell
						snake_case__				:   Union[str, Any]     =						select_one_column
						snake_case__				:   str     =						allow_empty_column_selection
						snake_case__				:   List[str]     =						init_cell_selection_weights_to_zero
						snake_case__				:   Optional[Any]     =						reset_position_index_per_cell
						snake_case__				:   Optional[int]     =						disable_per_token_loss

						# Aggregation hyperparameters
						snake_case__				:   Tuple     =						aggregation_labels
						snake_case__				:   Any     =						no_aggregation_label_index

						if isinstance(self.aggregation_labels							,	__A ):
							snake_case__				:   str     =						{int(__A ): v for k, v in aggregation_labels.items()}











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      :    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}
}

"""

__lowerCamelCase      :    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
"""

__lowerCamelCase      :    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 							SCREAMING_SNAKE_CASE__		(       datasets.Metric		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Dict ):
						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  :						Union[str, Any]							,	__A  :						Dict							,	__A  :						List[str]							,	__A  :						int=None							,	__A  :						List[Any]=None							,	__A  :						Optional[int]=None							,	__A  :						List[Any]=None							,	__A  :						Union[str, Any]="auto"							,	__A  :						Optional[Any]=-1							,	__A  :						Optional[Any]=0.9							,	__A  :						Any=5							,	__A  :						List[Any]=5_0_0							,	__A  :						Tuple="gpt2-large"							,	__A  :						Optional[Any]=-1							,	__A  :						str=1_0_2_4							,	__A  :						Tuple=2_5							,	__A  :						str=5							,	__A  :						Optional[int]=True							,	__A  :						Any=2_5							,	):
						snake_case__				:   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



