






'''simple docstring'''







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

import datasets


__lowerCAmelCase       		=      '\\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'

__lowerCAmelCase       		=      '\\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'

__lowerCAmelCase       		=      '\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 		_UpperCAmelCase   (							__A							:   List[Any]						,		__A							:   Any      ):
				return float((preds == labels).mean()      )





def 		_UpperCAmelCase   (							__A							:   List[Any]						,		__A							:   List[str]      ):
				a_		:  Union[str, Any]											=      simple_accuracy(__A						,		__A      )
				a_		:  List[Any]											=      float(fa_score(y_true=__A						,		y_pred=__A      )      )
				return {
				    "accuracy": acc,
				    "f1": fa,
				}





def 		_UpperCAmelCase   (							__A							:   List[str]						,		__A							:   List[Any]      ):
				a_		:  Optional[int]											=      float(pearsonr(__A						,		__A      )[0]      )
				a_		:  Any											=      float(spearmanr(__A						,		__A      )[0]      )
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				}





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






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			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 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			int					)			->			Tuple:
							if self.config_name == "cola":
											return {"matthews_correlation": matthews_corrcoef(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)}
							elif self.config_name == "stsb":
											return pearson_and_spearman(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							elif self.config_name in ["mrpc", "qqp"]:
											return acc_and_fa(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
							elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
											return {"accuracy": simple_accuracy(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)}
							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 unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			List[Any]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			List[Any]:
							torch.manual_seed(0					)
							a_		:  Tuple											=      UNetaDModel(
							    sample_size=(32, 64)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')  ,						up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')  ,						)
							return model






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			List[Any]:
							torch.manual_seed(0					)
							a_		:  Any											=      UNetaDConditionModel(
							    sample_size=(64, 32)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')  ,						up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')  ,						cross_attention_dim=10  ,						)
							return model






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							torch.manual_seed(0					)
							a_		:  List[Any]											=      AutoencoderKL(
							    sample_size=(128, 64)  ,						in_channels=1  ,						out_channels=1  ,						latent_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')  ,						up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')  ,						)
							a_		:  List[Any]											=      UNetaDModel(
							    sample_size=(64, 32)  ,						in_channels=1  ,						out_channels=1  ,						layers_per_block=2  ,						block_out_channels=(128, 128)  ,						down_block_types=('''AttnDownBlock2D''', '''DownBlock2D''')  ,						up_block_types=('''UpBlock2D''', '''AttnUpBlock2D''')  ,						)
							return vqvae, unet







			@slow
			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Union[str, Any]:
							a_		:  Dict											=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							a_		:  Union[str, Any]											=      Mel(
							    x_res=self.dummy_unet.config.sample_size[1]  ,						y_res=self.dummy_unet.config.sample_size[0]  ,						)

							a_		:  Any											=      DDPMScheduler()
							a_		:  str											=      AudioDiffusionPipeline(vqvae=__SCREAMING_SNAKE_CASE  ,						unet=self.dummy_unet  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  List[Any]											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[int]											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  List[str]											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						steps=4					)
							a_		:  List[Any]											=      output.audios[0]
							a_		:  Dict											=      output.images[0]

							a_		:  Dict											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  Optional[Any]											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						steps=4  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output[0][0]

							assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
							assert (
							    image.height == self.dummy_unet.config.sample_size[0]
							    and image.width == self.dummy_unet.config.sample_size[1]
							)
							a_		:  Dict											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  str											=      np.frombuffer(image_from_tuple.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() == 0

							a_		:  str											=      Mel(
							    x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]  ,						y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]  ,						)

							a_		:  int											=      DDIMScheduler()
							a_		:  Dict											=      self.dummy_vqvae_and_unet
							a_		:  List[str]											=      AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=dummy_vqvae_and_unet[1]  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							np.random.seed(0					)
							a_		:  List[str]											=      np.random.uniform(-1  ,						1  ,						((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)					)
							a_		:  int											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  int											=      pipe(raw_audio=__SCREAMING_SNAKE_CASE  ,						generator=__SCREAMING_SNAKE_CASE  ,						start_step=5  ,						steps=10					)
							a_		:  List[str]											=      output.images[0]

							assert (
							    image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
							    and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
							)
							a_		:  Optional[Any]											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0

							a_		:  List[str]											=      self.dummy_unet_condition
							a_		:  Dict											=      AudioDiffusionPipeline(
							    vqvae=self.dummy_vqvae_and_unet[0]  ,						unet=__SCREAMING_SNAKE_CASE  ,						mel=__SCREAMING_SNAKE_CASE  ,						scheduler=__SCREAMING_SNAKE_CASE					)
							a_		:  int											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							np.random.seed(0					)
							a_		:  Any											=      torch.rand((1, 1, 10)					)
							a_		:  Tuple											=      pipe(generator=__SCREAMING_SNAKE_CASE  ,						encoding=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output.images[0]
							a_		:  Dict											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  List[str]											=      np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0





@slow
@require_torch_gpu
class 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Any:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Any:
							a_		:  Any											=      torch_device

							a_		:  Optional[int]											=      DiffusionPipeline.from_pretrained('''teticio/audio-diffusion-ddim-256'''					)
							a_		:  Dict											=      pipe.to(__SCREAMING_SNAKE_CASE					)
							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

							a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(42					)
							a_		:  List[str]											=      pipe(generator=__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      output.audios[0]
							a_		:  Tuple											=      output.images[0]

							assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
							assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
							a_		:  str											=      np.frombuffer(image.tobytes()  ,						dtype='''uint8'''					)[:10]
							a_		:  Tuple											=      np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() == 0




