



'''simple docstring'''




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

import datasets


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

lowerCamelCase							:Optional[int]       = '''\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
'''

lowerCamelCase							:str       = '''
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "pearson": Pearson Correlation
    "spearmanr": Spearman Correlation
    "matthews_correlation": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
    {\'pearson\': 1.0, \'spearmanr\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			return float((preds == labels).mean()   )



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Union[str, Any]     =					simple_accuracy(lowerCamelCase__						,				lowerCamelCase__   )
			A_     :						Optional[int]     =					float(fa_score(y_true=lowerCamelCase__						,				y_pred=lowerCamelCase__   )   )
			return {
			    "accuracy": acc,
			    "f1": fa,
			}



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						int     =					float(pearsonr(lowerCamelCase__						,				lowerCamelCase__   )[0]   )
			A_     :						Any     =					float(spearmanr(lowerCamelCase__						,				lowerCamelCase__   )[0]   )
			return {
			    "pearson": pearson_corr,
			    "spearmanr": spearman_corr,
			}







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


	def    _a     (self							):
				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    _a     (self	,				lowercase	,				lowercase							):
				if self.config_name == "cola":
							return {"matthews_correlation": matthews_corrcoef(lowercase	,				lowercase							)}
				elif self.config_name == "stsb":
							return pearson_and_spearman(lowercase	,				lowercase							)
				elif self.config_name in ["mrpc", "qqp"]:
							return acc_and_fa(lowercase	,				lowercase							)
				elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
							return {"accuracy": simple_accuracy(lowercase	,				lowercase							)}
				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 transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference







class      _lowerCAmelCase							(      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = ShapEPipeline
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = ['prompt']
	__SCREAMING_SNAKE_CASE       :		str		      = ['prompt']
	__SCREAMING_SNAKE_CASE       :		Dict		      = [
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = False


	@property
	def    _a     (self							):
				return 32


	@property
	def    _a     (self							):
				return 32


	@property
	def    _a     (self							):
				return self.time_input_dim * 4


	@property
	def    _a     (self							):
				return 8


	@property
	def    _a     (self							):
				A_     :						Dict     =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
				return tokenizer


	@property
	def    _a     (self							):
				torch.manual_seed(0							)
				A_     :						Any     =					CLIPTextConfig(
				    bos_token_id=0	,				eos_token_id=2	,				hidden_size=self.text_embedder_hidden_size	,				projection_dim=self.text_embedder_hidden_size	,				intermediate_size=37	,				layer_norm_eps=1E-05	,				num_attention_heads=4	,				num_hidden_layers=5	,				pad_token_id=1	,				vocab_size=1000	,				)
				return CLIPTextModelWithProjection(lowercase							)


	@property
	def    _a     (self							):
				torch.manual_seed(0							)

				A_     :						Any     =					{
				    """num_attention_heads""": 2,
				    """attention_head_dim""": 16,
				    """embedding_dim""": self.time_input_dim,
				    """num_embeddings""": 32,
				    """embedding_proj_dim""": self.text_embedder_hidden_size,
				    """time_embed_dim""": self.time_embed_dim,
				    """num_layers""": 1,
				    """clip_embed_dim""": self.time_input_dim * 2,
				    """additional_embeddings""": 0,
				    """time_embed_act_fn""": """gelu""",
				    """norm_in_type""": """layer""",
				    """encoder_hid_proj_type""": None,
				    """added_emb_type""": None,
				}

				A_     :						List[str]     =					PriorTransformer(**lowercase							)
				return model


	@property
	def    _a     (self							):
				torch.manual_seed(0							)

				A_     :						str     =					{
				    """param_shapes""": (
				        (self.renderer_dim, 93),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				    ),
				    """d_latent""": self.time_input_dim,
				    """d_hidden""": self.renderer_dim,
				    """n_output""": 12,
				    """background""": (
				        0.1,
				        0.1,
				        0.1,
				    ),
				}
				A_     :						Dict     =					ShapERenderer(**lowercase							)
				return model


	def    _a     (self							):
				A_     :						Optional[int]     =					self.dummy_prior
				A_     :						Optional[int]     =					self.dummy_text_encoder
				A_     :						int     =					self.dummy_tokenizer
				A_     :						Dict     =					self.dummy_renderer

				A_     :						Tuple     =					HeunDiscreteScheduler(
				    beta_schedule="""exp"""	,				num_train_timesteps=1024	,				prediction_type="""sample"""	,				use_karras_sigmas=lowercase	,				clip_sample=lowercase	,				clip_sample_range=1.0	,				)
				A_     :						Union[str, Any]     =					{
				    """prior""": prior,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """renderer""": renderer,
				    """scheduler""": scheduler,
				}

				return components


	def    _a     (self	,				lowercase	,				lowercase=0							):
				if str(lowercase							).startswith("""mps"""							):
							A_     :						Any     =					torch.manual_seed(lowercase							)
				else:
							A_     :						str     =					torch.Generator(device=lowercase							).manual_seed(lowercase							)
				A_     :						List[str]     =					{
				    """prompt""": """horse""",
				    """generator""": generator,
				    """num_inference_steps""": 1,
				    """frame_size""": 32,
				    """output_type""": """np""",
				}
				return inputs


	def    _a     (self							):
				A_     :						str     =					"""cpu"""

				A_     :						Union[str, Any]     =					self.get_dummy_components()

				A_     :						Optional[int]     =					self.pipeline_class(**lowercase							)
				A_     :						str     =					pipe.to(lowercase							)

				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						Dict     =					pipe(**self.get_dummy_inputs(lowercase							)							)
				A_     :						Dict     =					output.images[0]
				A_     :						int     =					image[0, -3:, -3:, -1]

				assert image.shape == (20, 32, 32, 3)

				A_     :						Tuple     =					np.array(
				    [
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				        0.00_03_92_16,
				    ]							)

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


	def    _a     (self							):
				# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
				self._test_inference_batch_consistent(batch_sizes=[1, 2]							)


	def    _a     (self							):
				A_     :						Tuple     =					torch_device == """cpu"""
				A_     :						Union[str, Any]     =					True

				self._test_inference_batch_single_identical(
				    batch_size=2	,				test_max_difference=lowercase	,				relax_max_difference=lowercase	,				)


	def    _a     (self							):
				A_     :						List[Any]     =					self.get_dummy_components()
				A_     :						Any     =					self.pipeline_class(**lowercase							)
				A_     :						Any     =					pipe.to(lowercase							)
				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						int     =					1
				A_     :						Union[str, Any]     =					2

				A_     :						Dict     =					self.get_dummy_inputs(lowercase							)

				for key in inputs.keys():
							if key in self.batch_params:
										A_     :						Optional[Any]     =					batch_size * [inputs[key]]

				A_     :						List[Any]     =					pipe(**lowercase	,				num_images_per_prompt=lowercase							)[0]

				assert images.shape[0] == batch_size * num_images_per_prompt







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


	def    _a     (self							):
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()


	def    _a     (self							):
				A_     :						Union[str, Any]     =					load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/shap_e/test_shap_e_np_out.npy"""							)
				A_     :						Tuple     =					ShapEPipeline.from_pretrained("""openai/shap-e"""							)
				A_     :						int     =					pipe.to(lowercase							)
				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						int     =					torch.Generator(device=lowercase							).manual_seed(0							)

				A_     :						List[str]     =					pipe(
				    """a shark"""	,				generator=lowercase	,				guidance_scale=15.0	,				num_inference_steps=64	,				frame_size=64	,				output_type="""np"""	,				).images[0]

				assert images.shape == (20, 64, 64, 3)

				assert_mean_pixel_difference(lowercase	,				lowercase							)