

"""simple docstring"""




import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
      from scipy.stats import pearsonr, spearmanr
      from sklearn.metrics import fa_score, matthews_corrcoef


A_							:			Optional[int]											= (
    "This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate "
    "library. You can have a look at this example script for pointers: "
    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
)

def 	A       (     snake_case__		,				snake_case__    ):




   '''simple docstring'''


   warnings.warn(__A		,				__A    )
   requires_backends(__A		,				"""sklearn"""    )
   return (preds == labels).mean()

def 	A       (     snake_case__		,				snake_case__    ):




   '''simple docstring'''


   warnings.warn(__A		,				__A    )
   requires_backends(__A		,				"""sklearn"""    )
   SCREAMING_SNAKE_CASE__								=      simple_accuracy(__A		,				__A    )
   SCREAMING_SNAKE_CASE__								=      fa_score(y_true=__A		,				y_pred=__A    )
   return {
       "acc": acc,
       "f1": fa,
       "acc_and_f1": (acc + fa) / 2,
   }

def 	A       (     snake_case__		,				snake_case__    ):




   '''simple docstring'''


   warnings.warn(__A		,				__A    )
   requires_backends(__A		,				"""sklearn"""    )
   SCREAMING_SNAKE_CASE__								=      pearsonr(__A		,				__A    )[0]
   SCREAMING_SNAKE_CASE__								=      spearmanr(__A		,				__A    )[0]
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
       "corr": (pearson_corr + spearman_corr) / 2,
   }

def 	A       (     snake_case__		,				snake_case__		,				snake_case__    ):




   '''simple docstring'''


   warnings.warn(__A		,				__A    )
   requires_backends(__A		,				"""sklearn"""    )
   assert len(__A    ) == len(__A    ), f"""Predictions and labels have mismatched lengths {len(__A    )} and {len(__A    )}"""
   if task_name == "cola":
      return {"mcc": matthews_corrcoef(__A		,				__A    )}
   elif task_name == "sst-2":
      return {"acc": simple_accuracy(__A		,				__A    )}
   elif task_name == "mrpc":
      return acc_and_fa(__A		,				__A    )
   elif task_name == "sts-b":
      return pearson_and_spearman(__A		,				__A    )
   elif task_name == "qqp":
      return acc_and_fa(__A		,				__A    )
   elif task_name == "mnli":
      return {"mnli/acc": simple_accuracy(__A		,				__A    )}
   elif task_name == "mnli-mm":
      return {"mnli-mm/acc": simple_accuracy(__A		,				__A    )}
   elif task_name == "qnli":
      return {"acc": simple_accuracy(__A		,				__A    )}
   elif task_name == "rte":
      return {"acc": simple_accuracy(__A		,				__A    )}
   elif task_name == "wnli":
      return {"acc": simple_accuracy(__A		,				__A    )}
   elif task_name == "hans":
      return {"acc": simple_accuracy(__A		,				__A    )}
   else:
      raise KeyError(__A    )




def 	A       (     snake_case__		,				snake_case__		,				snake_case__    ):




   '''simple docstring'''


   warnings.warn(__A		,				__A    )
   requires_backends(__A		,				"""sklearn"""    )
   if len(__A    ) != len(__A    ):
      raise ValueError(f"""Predictions and labels have mismatched lengths {len(__A    )} and {len(__A    )}"""    )
   if task_name == "xnli":
      return {"acc": simple_accuracy(__A		,				__A    )}
   else:
      raise KeyError(__A    )





"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
						import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    str:
						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      ort.SessionOptions()
						SCREAMING_SNAKE_CASE__								=      False
						return options





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""       )
						SCREAMING_SNAKE_CASE__								=      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""       )

						# using the PNDM scheduler by default
						SCREAMING_SNAKE_CASE__								=      OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    """CompVis/stable-diffusion-v1-4"""    ,				revision="""onnx"""    ,				safety_checker=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				provider=self.gpu_provider    ,				sess_options=self.gpu_options    ,				)
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      """A red cat sitting on a park bench"""

						SCREAMING_SNAKE_CASE__								=      np.random.RandomState(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(
						    prompt=__UpperCAmelCase    ,				image=__UpperCAmelCase    ,				mask_image=__UpperCAmelCase    ,				strength=0.75    ,				guidance_scale=7.5    ,				num_inference_steps=1_5    ,				generator=__UpperCAmelCase    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image       ).max() < 1e-2


