





'''simple docstring'''







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

import datasets


snake_case_     :       List[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_     :       Optional[Any]						=  "\\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_     :       List[str]						=  "\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        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			Optional[int]   ) ->	Optional[Any]:
						return float((preds == labels).mean()   )





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Union[str, Any],			SCREAMING_SNAKE_CASE__      :			Dict   ) ->	Optional[int]:
						UpperCAmelCase_			:      Union[str, Any]     						=  simple_accuracy(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Union[str, Any]     						=  float(fa_score(y_true=SCREAMING_SNAKE_CASE__,			y_pred=SCREAMING_SNAKE_CASE__   )   )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			Optional[int]   ) ->	Tuple:
						UpperCAmelCase_			:      Optional[Any]     						=  float(pearsonr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )[0]   )
						UpperCAmelCase_			:      Any     						=  float(spearmanr(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )[0]   )
						return {
						    "pearson": pearson_corr,
						    "spearmanr": spearman_corr,
						}


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

							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       int:







													"""simple docstring"""
													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     UpperCAmelCase__	(							self							:     Union[str, Any]       ,     __magic_name__							:     Union[str, Any]       ,     __magic_name__							:     Optional[Any]   )					->       Any:







													"""simple docstring"""
													if self.config_name == "cola":
																			return {"matthews_correlation": matthews_corrcoef(__magic_name__       ,     __magic_name__   )}
													elif self.config_name == "stsb":
																			return pearson_and_spearman(__magic_name__       ,     __magic_name__   )
													elif self.config_name in ["mrpc", "qqp"]:
																			return acc_and_fa(__magic_name__       ,     __magic_name__   )
													elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
																			return {"accuracy": simple_accuracy(__magic_name__       ,     __magic_name__   )}
													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"]'''   )












'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


snake_case_     :       List[str]						=  False


class 					__a      (unittest.TestCase     ):
							pass





@nightly
@require_torch_gpu
class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     int   )					->       str:







													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,     torch_dtype=torch.floataa   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      List[Any]     						=  load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''   )

													UpperCAmelCase_			:      Optional[int]     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Union[str, Any]     						=  pipe.dual_guided(
													    prompt='''first prompt'''       ,     image=__magic_name__       ,     text_to_image_strength=0.7_5       ,     generator=__magic_name__       ,     guidance_scale=7.5       ,     num_inference_steps=2       ,     output_type='''numpy'''       ,     ).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(__magic_name__   )
																			UpperCAmelCase_			:      Optional[int]     						=  VersatileDiffusionPipeline.from_pretrained(__magic_name__       ,     torch_dtype=torch.floataa   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Any     						=  generator.manual_seed(0   )
													UpperCAmelCase_			:      Dict     						=  pipe.dual_guided(
													    prompt='''first prompt'''       ,     image=__magic_name__       ,     text_to_image_strength=0.7_5       ,     generator=__magic_name__       ,     guidance_scale=7.5       ,     num_inference_steps=2       ,     output_type='''numpy'''       ,     ).images

													assert np.abs(image - new_image   ).sum() < 1E-5, "Models don't have the same forward pass"



							def     UpperCAmelCase__	(							self							:     str   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,     torch_dtype=torch.floataa   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Union[str, Any]     						=  '''cyberpunk 2077'''
													UpperCAmelCase_			:      Union[str, Any]     						=  load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''   )
													UpperCAmelCase_			:      Tuple     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  pipe.dual_guided(
													    prompt=__magic_name__       ,     image=__magic_name__       ,     text_to_image_strength=0.7_5       ,     generator=__magic_name__       ,     guidance_scale=7.5       ,     num_inference_steps=50       ,     output_type='''numpy'''       ,     ).images

													UpperCAmelCase_			:      List[str]     						=  image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_			:      Union[str, Any]     						=  np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]   )

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

													UpperCAmelCase_			:      Tuple     						=  '''A painting of a squirrel eating a burger '''
													UpperCAmelCase_			:      Optional[int]     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      List[Any]     						=  pipe.text_to_image(
													    prompt=__magic_name__       ,     generator=__magic_name__       ,     guidance_scale=7.5       ,     num_inference_steps=50       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Tuple     						=  image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_			:      Any     						=  np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]   )

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

													UpperCAmelCase_			:      Tuple     						=  pipe.image_variation(__magic_name__       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Optional[Any]     						=  image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_			:      List[str]     						=  np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]   )

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





