

"""simple docstring"""

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


_UpperCamelCase		     =    """\
@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}
}

"""

_UpperCamelCase		     =    """\
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
"""

_UpperCamelCase		     =    """
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 			__a					(						datasets.Metric       ):





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case=None			,					snake_case=None			,					snake_case=None			,					snake_case=None			,					snake_case="auto"			,					snake_case=-1			,					snake_case=0.9			,					snake_case=5			,					snake_case=500			,					snake_case="gpt2-large"			,					snake_case=-1			,					snake_case=1_024			,					snake_case=25			,					snake_case=5			,					snake_case=True			,					snake_case=25			,					):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				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









"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}






class 			__a					(						__magic_name__       ,	__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						'focalnet'






       def __init__(   self			,					snake_case=224			,					snake_case=4			,					snake_case=3			,					snake_case=96			,					snake_case=False			,					snake_case=[192, 384, 768, 768]			,					snake_case=[2, 2, 6, 2]			,					snake_case=[2, 2, 2, 2]			,					snake_case=[3, 3, 3, 3]			,					snake_case="gelu"			,					snake_case=4.0			,					snake_case=0.0			,					snake_case=0.1			,					snake_case=False			,					snake_case=1e-4			,					snake_case=False			,					snake_case=False			,					snake_case=False			,					snake_case=0.02			,					snake_case=1e-5			,					snake_case=32			,					snake_case=None			,					snake_case=None			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(**snake_case   )

        lowerCAmelCase__   :				Optional[Any] 			=				image_size
        lowerCAmelCase__   :				Union[str, Any] 			=				patch_size
        lowerCAmelCase__   :				List[str] 			=				num_channels
        lowerCAmelCase__   :				List[str] 			=				embed_dim
        lowerCAmelCase__   :				List[Any] 			=				use_conv_embed
        lowerCAmelCase__   :				List[str] 			=				hidden_sizes
        lowerCAmelCase__   :				List[Any] 			=				depths
        lowerCAmelCase__   :				Union[str, Any] 			=				focal_levels
        lowerCAmelCase__   :				Union[str, Any] 			=				focal_windows
        lowerCAmelCase__   :				int 			=				hidden_act
        lowerCAmelCase__   :				List[Any] 			=				mlp_ratio
        lowerCAmelCase__   :				str 			=				hidden_dropout_prob
        lowerCAmelCase__   :				Union[str, Any] 			=				drop_path_rate
        lowerCAmelCase__   :				Tuple 			=				use_layerscale
        lowerCAmelCase__   :				Tuple 			=				layerscale_value
        lowerCAmelCase__   :				str 			=				use_post_layernorm
        lowerCAmelCase__   :				str 			=				use_post_layernorm_in_modulation
        lowerCAmelCase__   :				Union[str, Any] 			=				normalize_modulator
        lowerCAmelCase__   :				Optional[int] 			=				initializer_range
        lowerCAmelCase__   :				List[Any] 			=				layer_norm_eps
        lowerCAmelCase__   :				str 			=				encoder_stride
        lowerCAmelCase__   :				Union[str, Any] 			=				["stem"] + [F"""stage{idx}""" for idx in range(1			,					len(self.depths   ) + 1   )]
        lowerCAmelCase__			,						lowerCAmelCase__   :				Union[str, Any] 			=				get_aligned_output_features_output_indices(
            out_features=snake_case			,					out_indices=snake_case			,					stage_names=self.stage_names   )






