
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


lowercase__							:							List[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 	A_ (							snake_case							:   Optional[int] ,      snake_case							:   Tuple )      ->    Optional[Any]:



						'''simple docstring'''

						warnings.warn(snake_case ,      snake_case )
						requires_backends(snake_case ,      '''sklearn''' )
						return (preds == labels).mean()




def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   int )      ->    int:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   int ,      snake_case							:   int )      ->    List[str]:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Tuple ,      snake_case							:   Optional[int] ,      snake_case							:   Any )      ->    Any:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   int ,      snake_case							:   Tuple ,      snake_case							:   Union[str, Any] )      ->    Dict:



						'''simple docstring'''

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






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													super().__init__()

													# make sure scheduler can always be converted to DDIM
													__UpperCamelCase       =			DDIMScheduler.from_config(scheduler.config			)

													self.register_modules(unet=SCREAMING_SNAKE_CASE_     ,	scheduler=SCREAMING_SNAKE_CASE_			)



							@torch.no_grad()
							def __call__(  self     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 0.0     ,	SCREAMING_SNAKE_CASE_ = 50     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "pil"     ,	SCREAMING_SNAKE_CASE_ = True     ,	)->   Union[ImagePipelineOutput, Tuple]:





													'''simple docstring'''






													if isinstance(self.unet.config.sample_size     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			(
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			__UpperCamelCase       =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			) and len(SCREAMING_SNAKE_CASE_			) != batch_size:
																			raise ValueError(
																			    F"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_			)}, but requested an effective batch"
																			    F" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

													__UpperCamelCase       =			randn_tensor(SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	device=self.device     ,	dtype=self.unet.dtype			)

													# set step values
													self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_			)

													for t in self.progress_bar(self.scheduler.timesteps			):
																			# 1. predict noise model_output
																			__UpperCamelCase       =			self.unet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			).sample

																			# 2. predict previous mean of image x_t-1 and add variance depending on eta
																			# eta corresponds to η in paper and should be between [0, 1]
																			# do x_t -> x_t-1
																			__UpperCamelCase       =			self.scheduler.step(
																			    SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	eta=SCREAMING_SNAKE_CASE_     ,	use_clipped_model_output=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_			).prev_sample

													__UpperCamelCase       =			(image / 2 + 0.5).clamp(0     ,	1			)
													__UpperCamelCase       =			image.cpu().permute(0     ,	2     ,	3     ,	1			).numpy()
													if output_type == "pil":
																			__UpperCamelCase       =			self.numpy_to_pil(SCREAMING_SNAKE_CASE_			)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_			)




