




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__ :   Tuple						=							'''\\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'''

UpperCamelCase__ :   List[Any]						=							'''\\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'''

UpperCamelCase__ :   Optional[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 		snake_case	(			self ):
							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 		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__=1024      ,snake_case__=25      ,snake_case__=5      ,snake_case__=True      ,snake_case__=25      ,):
							SCREAMING_SNAKE_CASE_   : Union[str, 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






import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






@slow
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_euler"						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							sd_pipe([prompt]   ,   generator=a   ,   guidance_scale=9.0   ,   num_inference_steps=20   ,   output_type="np"						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							output.images

				SCREAMING_SNAKE_CASE :   Any					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_euler"						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe([prompt]   ,   generator=a   ,   guidance_scale=9.0   ,   num_inference_steps=20   ,   output_type="np"						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							output.images

				SCREAMING_SNAKE_CASE :   Tuple					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-1






			def 				__UpperCamelCase			(      self				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_dpmpp_2m"						)

				SCREAMING_SNAKE_CASE :   str					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=7.5   ,   num_inference_steps=15   ,   output_type="np"   ,   use_karras_sigmas=a   ,   )

				SCREAMING_SNAKE_CASE :   str					  =							output.images

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]						)

				assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2