


'''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


UpperCAmelCase_		:   Union[str, Any]							   =		(
    '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 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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








'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()
											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = 1    ,					__snake_case = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.scheduler.sigmas[i] * torch.ones(shape[0]    ,					device=self.device  )

															# correction step
															for _ in range(self.scheduler.config.correct_steps  ):
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.unet(__snake_case    ,					__snake_case  ).sample
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.scheduler.step_correct(__snake_case    ,					__snake_case    ,					generator=__snake_case  ).prev_sample

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




