

"""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


A_							:			str											= "\\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"

A_							:			List[str]											= "\\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"

A_							:			List[Any]											= "\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    lowerCamelCase						(datasets.Metric			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						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				:						Tuple    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						int=None    ,				__UpperCAmelCase				:						List[str]=None    ,				__UpperCAmelCase				:						Dict=None    ,				__UpperCAmelCase				:						int=None    ,				__UpperCAmelCase				:						Optional[int]="auto"    ,				__UpperCAmelCase				:						Dict=-1    ,				__UpperCAmelCase				:						Union[str, Any]=0.9    ,				__UpperCAmelCase				:						Any=5    ,				__UpperCAmelCase				:						int=5_0_0    ,				__UpperCAmelCase				:						List[str]="gpt2-large"    ,				__UpperCAmelCase				:						List[Any]=-1    ,				__UpperCAmelCase				:						Optional[Any]=1_0_2_4    ,				__UpperCAmelCase				:						Optional[Any]=2_5    ,				__UpperCAmelCase				:						int=5    ,				__UpperCAmelCase				:						Tuple=True    ,				__UpperCAmelCase				:						str=2_5    ,				)   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      compute_mauve(
						    p_text=lowerCAmelCase_    ,				q_text=lowerCAmelCase_    ,				p_features=lowerCAmelCase_    ,				q_features=lowerCAmelCase_    ,				p_tokens=lowerCAmelCase_    ,				q_tokens=lowerCAmelCase_    ,				num_buckets=lowerCAmelCase_    ,				pca_max_data=lowerCAmelCase_    ,				kmeans_explained_var=lowerCAmelCase_    ,				kmeans_num_redo=lowerCAmelCase_    ,				kmeans_max_iter=lowerCAmelCase_    ,				featurize_model_name=lowerCAmelCase_    ,				device_id=lowerCAmelCase_    ,				max_text_length=lowerCAmelCase_    ,				divergence_curve_discretization_size=lowerCAmelCase_    ,				mauve_scaling_factor=lowerCAmelCase_    ,				verbose=lowerCAmelCase_    ,				seed=lowerCAmelCase_    ,				)
						return out





"""simple docstring"""




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


A_							:			Union[str, Any]											= logging.get_logger(__name__)

A_							:			int											= {
    "caidas/swin2sr-classicalsr-x2-64": (
        "https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
    ),
}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		List[str]							=							'swin2sr'

			lowerCamelCase__   :		Optional[Any]							=							{
			    'hidden_size': 'embed_dim',
			    'num_attention_heads': 'num_heads',
			    'num_hidden_layers': 'num_layers',
			}

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						Optional[int]=6_4    ,				__UpperCAmelCase				:						Tuple=1    ,				__UpperCAmelCase				:						Optional[Any]=3    ,				__UpperCAmelCase				:						List[str]=1_8_0    ,				__UpperCAmelCase				:						List[str]=[6, 6, 6, 6, 6, 6]    ,				__UpperCAmelCase				:						Optional[Any]=[6, 6, 6, 6, 6, 6]    ,				__UpperCAmelCase				:						Tuple=8    ,				__UpperCAmelCase				:						Union[str, Any]=2.0    ,				__UpperCAmelCase				:						int=True    ,				__UpperCAmelCase				:						Optional[Any]=0.0    ,				__UpperCAmelCase				:						str=0.0    ,				__UpperCAmelCase				:						Dict=0.1    ,				__UpperCAmelCase				:						Dict="gelu"    ,				__UpperCAmelCase				:						Tuple=False    ,				__UpperCAmelCase				:						List[str]=0.02    ,				__UpperCAmelCase				:						List[Any]=1e-5    ,				__UpperCAmelCase				:						str=2    ,				__UpperCAmelCase				:						Optional[int]=1.0    ,				__UpperCAmelCase				:						Union[str, Any]="1conv"    ,				__UpperCAmelCase				:						List[Any]="pixelshuffle"    ,				**__UpperCAmelCase				:						Any    ,				)   ->    Optional[int]:
						super().__init__(**__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      patch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      embed_dim
						SCREAMING_SNAKE_CASE__								=      depths
						SCREAMING_SNAKE_CASE__								=      len(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      num_heads
						SCREAMING_SNAKE_CASE__								=      window_size
						SCREAMING_SNAKE_CASE__								=      mlp_ratio
						SCREAMING_SNAKE_CASE__								=      qkv_bias
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      drop_path_rate
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      use_absolute_embeddings
						SCREAMING_SNAKE_CASE__								=      layer_norm_eps
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      upscale
						SCREAMING_SNAKE_CASE__								=      img_range
						SCREAMING_SNAKE_CASE__								=      resi_connection
						SCREAMING_SNAKE_CASE__								=      upsampler


