






import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
	pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase      :    int   		=   logging.get_logger(__name__)

__lowerCamelCase      :    Dict   		=   {
    """kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"align_text_model"



					def __init__(      self  :						Dict							,	__A  :						Optional[Any]=3_0_5_2_2							,	__A  :						Optional[int]=7_6_8							,	__A  :						Dict=1_2							,	__A  :						Any=1_2							,	__A  :						List[str]=3_0_7_2							,	__A  :						Tuple="gelu"							,	__A  :						List[Any]=0.1							,	__A  :						List[str]=0.1							,	__A  :						Optional[Any]=5_1_2							,	__A  :						Optional[int]=2							,	__A  :						int=0.0_2							,	__A  :						Dict=1e-1_2							,	__A  :						Optional[int]=0							,	__A  :						List[Any]="absolute"							,	__A  :						str=True							,	**__A  :						Optional[Any]							,	):
						super().__init__(**__A )

						snake_case__				:   Any     =						vocab_size
						snake_case__				:   str     =						hidden_size
						snake_case__				:   str     =						num_hidden_layers
						snake_case__				:   List[str]     =						num_attention_heads
						snake_case__				:   Optional[int]     =						hidden_act
						snake_case__				:   Tuple     =						intermediate_size
						snake_case__				:   Optional[Any]     =						hidden_dropout_prob
						snake_case__				:   Dict     =						attention_probs_dropout_prob
						snake_case__				:   Optional[Any]     =						max_position_embeddings
						snake_case__				:   List[str]     =						type_vocab_size
						snake_case__				:   Union[str, Any]     =						initializer_range
						snake_case__				:   Dict     =						layer_norm_eps
						snake_case__				:   Optional[int]     =						position_embedding_type
						snake_case__				:   Tuple     =						use_cache
						snake_case__				:   List[str]     =						pad_token_id






					@classmethod
					def  _lowercase  (      cls  :						Tuple							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Optional[int] ):
						cls._set_token_in_kwargs(__A )

						snake_case__,		snake_case__				:   Dict     =						cls.get_config_dict(__A							,	**__A )

						# get the text config dict if we are loading from AlignConfig
						if config_dict.get("model_type" ) == "align":
							snake_case__				:   Any     =						config_dict["text_config"]

						if "model_type" in config_dict and hasattr(cls							,	"model_type" ) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
							    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

						return cls.from_dict(__A							,	**__A )



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"align_vision_model"



					def __init__(      self  :						Union[str, Any]							,	__A  :						int = 3							,	__A  :						int = 6_0_0							,	__A  :						float = 2.0							,	__A  :						float = 3.1							,	__A  :						int = 8							,	__A  :						List[int] = [3, 3, 5, 3, 5, 5, 3]							,	__A  :						List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]							,	__A  :						List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]							,	__A  :						List[int] = []							,	__A  :						List[int] = [1, 2, 2, 2, 1, 2, 1]							,	__A  :						List[int] = [1, 2, 2, 3, 3, 4, 1]							,	__A  :						List[int] = [1, 6, 6, 6, 6, 6, 6]							,	__A  :						float = 0.2_5							,	__A  :						str = "swish"							,	__A  :						int = 2_5_6_0							,	__A  :						str = "mean"							,	__A  :						float = 0.0_2							,	__A  :						float = 0.0_0_1							,	__A  :						float = 0.9_9							,	__A  :						float = 0.2							,	**__A  :						Optional[int]							,	):
						super().__init__(**__A )

						snake_case__				:   Optional[Any]     =						num_channels
						snake_case__				:   str     =						image_size
						snake_case__				:   Optional[Any]     =						width_coefficient
						snake_case__				:   int     =						depth_coefficient
						snake_case__				:   List[str]     =						depth_divisor
						snake_case__				:   int     =						kernel_sizes
						snake_case__				:   str     =						in_channels
						snake_case__				:   Optional[Any]     =						out_channels
						snake_case__				:   Union[str, Any]     =						depthwise_padding
						snake_case__				:   Dict     =						strides
						snake_case__				:   Optional[int]     =						num_block_repeats
						snake_case__				:   Any     =						expand_ratios
						snake_case__				:   List[Any]     =						squeeze_expansion_ratio
						snake_case__				:   List[Any]     =						hidden_act
						snake_case__				:   Dict     =						hidden_dim
						snake_case__				:   str     =						pooling_type
						snake_case__				:   Any     =						initializer_range
						snake_case__				:   List[str]     =						batch_norm_eps
						snake_case__				:   Optional[Any]     =						batch_norm_momentum
						snake_case__				:   Any     =						drop_connect_rate
						snake_case__				:   Optional[int]     =						sum(__A ) * 4






					@classmethod
					def  _lowercase  (      cls  :						List[Any]							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Dict ):
						cls._set_token_in_kwargs(__A )

						snake_case__,		snake_case__				:   Any     =						cls.get_config_dict(__A							,	**__A )

						# get the vision config dict if we are loading from AlignConfig
						if config_dict.get("model_type" ) == "align":
							snake_case__				:   str     =						config_dict["vision_config"]

						if "model_type" in config_dict and hasattr(cls							,	"model_type" ) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
							    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

						return cls.from_dict(__A							,	**__A )



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"align"
					a_			=	True



					def __init__(      self  :						int							,	__A  :						List[str]=None							,	__A  :						Any=None							,	__A  :						Tuple=6_4_0							,	__A  :						int=1.0							,	__A  :						Tuple=0.0_2							,	**__A  :						Tuple							,	):
						super().__init__(**__A )

						if text_config is None:
							snake_case__				:   List[Any]     =						{}
							logger.info("text_config is None. Initializing the AlignTextConfig with default values." )

						if vision_config is None:
							snake_case__				:   Union[str, Any]     =						{}
							logger.info("vision_config is None. Initializing the AlignVisionConfig with default values." )

						snake_case__				:   Any     =						AlignTextConfig(**__A )
						snake_case__				:   List[str]     =						AlignVisionConfig(**__A )

						snake_case__				:   List[str]     =						projection_dim
						snake_case__				:   Union[str, Any]     =						temperature_init_value
						snake_case__				:   int     =						initializer_range



					@classmethod
					def  _lowercase  (      cls  :						Optional[Any]							,	__A  :						AlignTextConfig							,	__A  :						AlignVisionConfig							,	**__A  :						Optional[int] ):
						return cls(text_config=text_config.to_dict()							,	vision_config=vision_config.to_dict()							,	**__A )






					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   Dict     =						copy.deepcopy(self.__dict__ )
						snake_case__				:   List[Any]     =						self.text_config.to_dict()
						snake_case__				:   Dict     =						self.vision_config.to_dict()
						snake_case__				:   Dict     =						self.__class__.model_type
						return output











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



