





"""simple docstring"""
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


_snake_case     :		str       						= "\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

_snake_case     :		List[str]       						= "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

_snake_case     :		Union[str, Any]       						= "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							return float((preds == labels).mean()       )






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  simple_accuracy(UpperCamelCase					,  UpperCamelCase       )
							A		=  float(fa_score(y_true=UpperCamelCase					,  y_pred=UpperCamelCase       )       )
							return {
							    "accuracy": acc,
							    "f1": fa,
							}






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  float(pearsonr(UpperCamelCase					,  UpperCamelCase       )[0]       )
							A		=  float(spearmanr(UpperCamelCase					,  UpperCamelCase       )[0]       )
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							}






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,							_KWARGS_DESCRIPTION )
class 					_UpperCAmelCase   (	datasets.Metric ):


					def 	lowerCamelCase       (					self			:Union[str, Any]		):
												if self.config_name not in [
												    "sst2",
												    "mnli",
												    "mnli_mismatched",
												    "mnli_matched",
												    "cola",
												    "stsb",
												    "mrpc",
												    "qqp",
												    "qnli",
												    "rte",
												    "wnli",
												    "hans",
												]:
																			raise KeyError(
																			    "You should supply a configuration name selected in "
																			    "[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
																			    "\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"		)
												return datasets.MetricInfo(
												    description=_DESCRIPTION     ,       citation=_CITATION     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
												        {
												            "predictions": datasets.Value("int64" if self.config_name != "stsb" else "float32"		),
												            "references": datasets.Value("int64" if self.config_name != "stsb" else "float32"		),
												        }		)     ,       codebase_urls=[]     ,       reference_urls=[]     ,       format="numpy"     ,       )


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:str		):
												if self.config_name == "cola":
																			return {"matthews_correlation": matthews_corrcoef(A_     ,       A_		)}
												elif self.config_name == "stsb":
																			return pearson_and_spearman(A_     ,       A_		)
												elif self.config_name in ["mrpc", "qqp"]:
																			return acc_and_fa(A_     ,       A_		)
												elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
																			return {"accuracy": simple_accuracy(A_     ,       A_		)}
												else:
																			raise KeyError(
																			    "You should supply a configuration name selected in "
																			    "[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
																			    "\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"		)




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 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     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       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_euler'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe([prompt]				,      generator=A_				,      guidance_scale=9.0				,      num_inference_steps=20				,      output_type='np'       )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     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       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'       )
				UpperCamelCase		=     sd_pipe.to(A_       )
				sd_pipe.set_progress_bar_config(disable=A_       )

				sd_pipe.set_scheduler('sample_dpmpp_2m'       )

				UpperCamelCase		=     'A painting of a squirrel eating a burger'
				UpperCamelCase		=     torch.manual_seed(0       )
				UpperCamelCase		=     sd_pipe(
				    [prompt]				,      generator=A_				,      guidance_scale=7.5				,      num_inference_steps=15				,      output_type='np'				,      use_karras_sigmas=A_				,      )

				UpperCamelCase		=     output.images

				UpperCamelCase		=     image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				UpperCamelCase		=     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
