





'''simple docstring'''



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   (UniPCMultistepScheduler,)
     UpperCAmelCase__       :						Dict      =   (('''num_inference_steps''', 25),)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       _snake_case     		=				{
           "num_train_timesteps": 1_000,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "solver_order": 2,
           "solver_type": "bh2",
       }

       config.update(**lowerCamelCase	)
       return config

     def 	UpperCamelCase(					self						,   lowerCamelCase=0						,   **lowerCamelCase	):
       _snake_case     		=				dict(self.forward_default_kwargs	)
       _snake_case     		=				kwargs.pop("num_inference_steps"						,   lowerCamelCase	)
       _snake_case     		=				self.dummy_sample
       _snake_case     		=				0.1 * sample
       _snake_case     		=				[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
         _snake_case     		=				self.get_scheduler_config(**lowerCamelCase	)
         _snake_case     		=				scheduler_class(**lowerCamelCase	)
         scheduler.set_timesteps(lowerCamelCase	)
         # copy over dummy past residuals
         _snake_case     		=				dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(lowerCamelCase	)
           _snake_case     		=				scheduler_class.from_pretrained(lowerCamelCase	)
           new_scheduler.set_timesteps(lowerCamelCase	)
           # copy over dummy past residuals
           _snake_case     		=				dummy_past_residuals[: new_scheduler.config.solver_order]

         _snake_case		,		_snake_case     		=				sample, sample
         for t in range(lowerCamelCase						,   time_step + scheduler.config.solver_order + 1	):
           _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	).prev_sample
           _snake_case     		=				new_scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	).prev_sample

           assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"

     def 	UpperCamelCase(					self						,   lowerCamelCase=0						,   **lowerCamelCase	):
       _snake_case     		=				dict(self.forward_default_kwargs	)
       _snake_case     		=				kwargs.pop("num_inference_steps"						,   lowerCamelCase	)
       _snake_case     		=				self.dummy_sample
       _snake_case     		=				0.1 * sample
       _snake_case     		=				[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
         _snake_case     		=				self.get_scheduler_config()
         _snake_case     		=				scheduler_class(**lowerCamelCase	)
         scheduler.set_timesteps(lowerCamelCase	)

         # copy over dummy past residuals (must be after setting timesteps)
         _snake_case     		=				dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(lowerCamelCase	)
           _snake_case     		=				scheduler_class.from_pretrained(lowerCamelCase	)
           # copy over dummy past residuals
           new_scheduler.set_timesteps(lowerCamelCase	)

           # copy over dummy past residual (must be after setting timesteps)
           _snake_case     		=				dummy_past_residuals[: new_scheduler.config.solver_order]

         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	).prev_sample
         _snake_case     		=				new_scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	).prev_sample

         assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"

     def 	UpperCamelCase(					self						,   lowerCamelCase=None						,   **lowerCamelCase	):
       if scheduler is None:
         _snake_case     		=				self.scheduler_classes[0]
         _snake_case     		=				self.get_scheduler_config(**lowerCamelCase	)
         _snake_case     		=				scheduler_class(**lowerCamelCase	)

       _snake_case     		=				self.scheduler_classes[0]
       _snake_case     		=				self.get_scheduler_config(**lowerCamelCase	)
       _snake_case     		=				scheduler_class(**lowerCamelCase	)

       _snake_case     		=				10
       _snake_case     		=				self.dummy_model()
       _snake_case     		=				self.dummy_sample_deter
       scheduler.set_timesteps(lowerCamelCase	)

       for i, t in enumerate(scheduler.timesteps	):
         _snake_case     		=				model(lowerCamelCase						,   lowerCamelCase	)
         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	).prev_sample

       return sample

     def 	UpperCamelCase(					self	):
       _snake_case     		=				dict(self.forward_default_kwargs	)

       _snake_case     		=				kwargs.pop("num_inference_steps"						,   lowerCamelCase	)

       for scheduler_class in self.scheduler_classes:
         _snake_case     		=				self.get_scheduler_config()
         _snake_case     		=				scheduler_class(**lowerCamelCase	)

         _snake_case     		=				self.dummy_sample
         _snake_case     		=				0.1 * sample

         if num_inference_steps is not None and hasattr(lowerCamelCase						,   "set_timesteps"	):
           scheduler.set_timesteps(lowerCamelCase	)
         elif num_inference_steps is not None and not hasattr(lowerCamelCase						,   "set_timesteps"	):
           _snake_case     		=				num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         _snake_case     		=				[residual + 0.2, residual + 0.15, residual + 0.10]
         _snake_case     		=				dummy_past_residuals[: scheduler.config.solver_order]

         _snake_case     		=				scheduler.timesteps[5]
         _snake_case     		=				scheduler.timesteps[6]

         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	).prev_sample
         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   **lowerCamelCase	).prev_sample

         self.assertEqual(output_a.shape						,   sample.shape	)
         self.assertEqual(output_a.shape						,   output_a.shape	)

     def 	UpperCamelCase(					self	):
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       _snake_case     		=				UniPCMultistepScheduler(**self.get_scheduler_config()	)
       _snake_case     		=				self.full_loop(scheduler=lowerCamelCase	)
       _snake_case     		=				torch.mean(torch.abs(lowerCamelCase	)	)

       assert abs(result_mean.item() - 0.2464	) < 1e-3

       _snake_case     		=				DPMSolverSinglestepScheduler.from_config(scheduler.config	)
       _snake_case     		=				DEISMultistepScheduler.from_config(scheduler.config	)
       _snake_case     		=				DPMSolverMultistepScheduler.from_config(scheduler.config	)
       _snake_case     		=				UniPCMultistepScheduler.from_config(scheduler.config	)

       _snake_case     		=				self.full_loop(scheduler=lowerCamelCase	)
       _snake_case     		=				torch.mean(torch.abs(lowerCamelCase	)	)

       assert abs(result_mean.item() - 0.2464	) < 1e-3

     def 	UpperCamelCase(					self	):
       for timesteps in [25, 50, 100, 999, 1_000]:
         self.check_over_configs(num_train_timesteps=lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       self.check_over_configs(thresholding=lowerCamelCase	)
       for order in [1, 2, 3]:
         for solver_type in ["bh1", "bh2"]:
           for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample"]:
               self.check_over_configs(
                   thresholding=lowerCamelCase						,   prediction_type=lowerCamelCase						,   sample_max_value=lowerCamelCase						,   solver_order=lowerCamelCase						,   solver_type=lowerCamelCase						,   )

     def 	UpperCamelCase(					self	):
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       for solver_type in ["bh1", "bh2"]:
         for order in [1, 2, 3]:
           for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(
                 solver_order=lowerCamelCase						,   solver_type=lowerCamelCase						,   prediction_type=lowerCamelCase						,   )
             _snake_case     		=				self.full_loop(
                 solver_order=lowerCamelCase						,   solver_type=lowerCamelCase						,   prediction_type=lowerCamelCase						,   )
             assert not torch.isnan(lowerCamelCase	).any(), "Samples have nan numbers"

     def 	UpperCamelCase(					self	):
       self.check_over_configs(lower_order_final=lowerCamelCase	)
       self.check_over_configs(lower_order_final=lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
         self.check_over_forward(num_inference_steps=lowerCamelCase						,   time_step=0	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.full_loop()
       _snake_case     		=				torch.mean(torch.abs(lowerCamelCase	)	)

       assert abs(result_mean.item() - 0.2464	) < 1e-3

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.full_loop(prediction_type="v_prediction"	)
       _snake_case     		=				torch.mean(torch.abs(lowerCamelCase	)	)

       assert abs(result_mean.item() - 0.1014	) < 1e-3

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.scheduler_classes[0]
       _snake_case     		=				self.get_scheduler_config(thresholding=lowerCamelCase						,   dynamic_thresholding_ratio=0	)
       _snake_case     		=				scheduler_class(**lowerCamelCase	)

       _snake_case     		=				10
       _snake_case     		=				self.dummy_model()
       _snake_case     		=				self.dummy_sample_deter.half()
       scheduler.set_timesteps(lowerCamelCase	)

       for i, t in enumerate(scheduler.timesteps	):
         _snake_case     		=				model(lowerCamelCase						,   lowerCamelCase	)
         _snake_case     		=				scheduler.step(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	).prev_sample

       assert sample.dtype == torch.floataa






     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       for scheduler_class in self.scheduler_classes:
         _snake_case     		=				self.get_scheduler_config(**lowerCamelCase	)
         _snake_case     		=				scheduler_class(**lowerCamelCase	)

         scheduler.set_timesteps(scheduler.config.num_train_timesteps	)
         assert len(scheduler.timesteps.unique()	) == scheduler.num_inference_steps









'''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


__magic_name__   :						List[str]    =			(
    """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"       )
  _snake_case     		=				simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				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"       )
  _snake_case     		=				pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  _snake_case     		=				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__       )


