




"""simple docstring"""


import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class _lowerCamelCase				(     __snake_case						):
   _lowerCamelCase				:Any	      =	(PNDMScheduler,)
   _lowerCamelCase				:int	      =	(("num_inference_steps", 50),)






   def        _lowerCAmelCase    (		self   :		List[Any]    ,	**UpperCamelCase   :		int    ) ->						Tuple:

         """simple docstring"""
         lowerCAmelCase__			: Tuple  					=       {
             '''num_train_timesteps''': 10_00,
             '''beta_start''': 0.0001,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**a_    )
         return config






   def        _lowerCAmelCase    (		self   :		Dict    ,	UpperCamelCase   :		List[Any]=0    ,	**UpperCamelCase   :		Optional[Any]    ) ->						Dict:

         """simple docstring"""
         lowerCAmelCase__			: Dict  					=       dict(self.forward_default_kwargs    )
         lowerCAmelCase__			: str  					=       kwargs.pop("""num_inference_steps"""    ,	a_    )
         lowerCAmelCase__			: str  					=       self.dummy_sample
         lowerCAmelCase__			: Any  					=       0.1 * sample
         lowerCAmelCase__			: List[Any]  					=       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
               lowerCAmelCase__			: Any  					=       self.get_scheduler_config(**a_    )
               lowerCAmelCase__			: int  					=       scheduler_class(**a_    )
               scheduler.set_timesteps(a_    )
               # copy over dummy past residuals
               lowerCAmelCase__			: Tuple  					=       dummy_past_residuals[:]

               with tempfile.TemporaryDirectory() as tmpdirname:
                     scheduler.save_config(a_    )
                     lowerCAmelCase__			: List[Any]  					=       scheduler_class.from_pretrained(a_    )
                     new_scheduler.set_timesteps(a_    )
                     # copy over dummy past residuals
                     lowerCAmelCase__			: Any  					=       dummy_past_residuals[:]

               lowerCAmelCase__			: int  					=       scheduler.step_prk(a_    ,	a_    ,	a_    ,	**a_    ).prev_sample
               lowerCAmelCase__			: Union[str, Any]  					=       new_scheduler.step_prk(a_    ,	a_    ,	a_    ,	**a_    ).prev_sample

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

               lowerCAmelCase__			: Tuple  					=       scheduler.step_plms(a_    ,	a_    ,	a_    ,	**a_    ).prev_sample
               lowerCAmelCase__			: Tuple  					=       new_scheduler.step_plms(a_    ,	a_    ,	a_    ,	**a_    ).prev_sample

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






   def        _lowerCAmelCase    (		self   :		str    ) ->						Union[str, Any]:

         """simple docstring"""
         pass






   def        _lowerCAmelCase    (		self   :		Dict    ,	UpperCamelCase   :		List[Any]=0    ,	**UpperCamelCase   :		str    ) ->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__			: int  					=       dict(self.forward_default_kwargs    )
         lowerCAmelCase__			: Optional[Any]  					=       kwargs.pop("""num_inference_steps"""    ,	a_    )
         lowerCAmelCase__			: List[Any]  					=       self.dummy_sample
         lowerCAmelCase__			: List[Any]  					=       0.1 * sample
         lowerCAmelCase__			: List[str]  					=       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
               lowerCAmelCase__			: str  					=       self.get_scheduler_config()
               lowerCAmelCase__			: int  					=       scheduler_class(**a_    )
               scheduler.set_timesteps(a_    )

               # copy over dummy past residuals (must be after setting timesteps)
               lowerCAmelCase__			: List[str]  					=       dummy_past_residuals[:]

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

                     # copy over dummy past residual (must be after setting timesteps)
                     lowerCAmelCase__			: str  					=       dummy_past_residuals[:]

               lowerCAmelCase__			: Any  					=       scheduler.step_prk(a_    ,	a_    ,	a_    ,	**a_    ).prev_sample
               lowerCAmelCase__			: Dict  					=       new_scheduler.step_prk(a_    ,	a_    ,	a_    ,	**a_    ).prev_sample

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

               lowerCAmelCase__			: List[Any]  					=       scheduler.step_plms(a_    ,	a_    ,	a_    ,	**a_    ).prev_sample
               lowerCAmelCase__			: Union[str, Any]  					=       new_scheduler.step_plms(a_    ,	a_    ,	a_    ,	**a_    ).prev_sample

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






   def        _lowerCAmelCase    (		self   :		List[str]    ,	**UpperCamelCase   :		List[Any]    ) ->						int:

         """simple docstring"""
         lowerCAmelCase__			: List[str]  					=       self.scheduler_classes[0]
         lowerCAmelCase__			: List[Any]  					=       self.get_scheduler_config(**a_    )
         lowerCAmelCase__			: Tuple  					=       scheduler_class(**a_    )

         lowerCAmelCase__			: List[Any]  					=       10
         lowerCAmelCase__			: List[str]  					=       self.dummy_model()
         lowerCAmelCase__			: Any  					=       self.dummy_sample_deter
         scheduler.set_timesteps(a_    )

         for i, t in enumerate(scheduler.prk_timesteps    ):
               lowerCAmelCase__			: int  					=       model(a_    ,	a_    )
               lowerCAmelCase__			: Optional[int]  					=       scheduler.step_prk(a_    ,	a_    ,	a_    ).prev_sample

         for i, t in enumerate(scheduler.plms_timesteps    ):
               lowerCAmelCase__			: int  					=       model(a_    ,	a_    )
               lowerCAmelCase__			: Optional[int]  					=       scheduler.step_plms(a_    ,	a_    ,	a_    ).prev_sample

         return sample






   def        _lowerCAmelCase    (		self   :		Optional[int]    ) ->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__			: List[Any]  					=       dict(self.forward_default_kwargs    )

         lowerCAmelCase__			: Any  					=       kwargs.pop("""num_inference_steps"""    ,	a_    )

         for scheduler_class in self.scheduler_classes:
               lowerCAmelCase__			: Optional[Any]  					=       self.get_scheduler_config()
               lowerCAmelCase__			: Dict  					=       scheduler_class(**a_    )

               lowerCAmelCase__			: List[Any]  					=       self.dummy_sample
               lowerCAmelCase__			: List[Any]  					=       0.1 * sample

               if num_inference_steps is not None and hasattr(a_    ,	"""set_timesteps"""    ):
                     scheduler.set_timesteps(a_    )
               elif num_inference_steps is not None and not hasattr(a_    ,	"""set_timesteps"""    ):
                     lowerCAmelCase__			: List[str]  					=       num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               lowerCAmelCase__			: Union[str, Any]  					=       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
               lowerCAmelCase__			: List[Any]  					=       dummy_past_residuals[:]

               lowerCAmelCase__			: str  					=       scheduler.step_prk(a_    ,	0    ,	a_    ,	**a_    ).prev_sample
               lowerCAmelCase__			: Dict  					=       scheduler.step_prk(a_    ,	1    ,	a_    ,	**a_    ).prev_sample

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

               lowerCAmelCase__			: Tuple  					=       scheduler.step_plms(a_    ,	0    ,	a_    ,	**a_    ).prev_sample
               lowerCAmelCase__			: List[str]  					=       scheduler.step_plms(a_    ,	1    ,	a_    ,	**a_    ).prev_sample

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






   def        _lowerCAmelCase    (		self   :		Optional[Any]    ) ->						int:

         """simple docstring"""

         for timesteps in [1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=a_    )






   def        _lowerCAmelCase    (		self   :		Any    ) ->						Tuple:

         """simple docstring"""

         for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=a_    )

         lowerCAmelCase__			: List[str]  					=       self.scheduler_classes[0]
         lowerCAmelCase__			: Tuple  					=       self.get_scheduler_config(steps_offset=1    )
         lowerCAmelCase__			: int  					=       scheduler_class(**a_    )
         scheduler.set_timesteps(10    )
         assert torch.equal(
             scheduler.timesteps    ,	torch.LongTensor(
                 [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]    )    ,	)






   def        _lowerCAmelCase    (		self   :		List[Any]    ) ->						Tuple:

         """simple docstring"""

         for beta_start, beta_end in zip([0.0001, 0.001]    ,	[0.002, 0.02]    ):
               self.check_over_configs(beta_start=a_    ,	beta_end=a_    )






   def        _lowerCAmelCase    (		self   :		Optional[int]    ) ->						Tuple:

         """simple docstring"""

         for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=a_    )






   def        _lowerCAmelCase    (		self   :		int    ) ->						List[str]:

         """simple docstring"""

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






   def        _lowerCAmelCase    (		self   :		int    ) ->						Optional[int]:

         """simple docstring"""

         for t in [1, 5, 10]:
               self.check_over_forward(time_step=a_    )






   def        _lowerCAmelCase    (		self   :		Dict    ) ->						List[str]:

         """simple docstring"""

         for t, num_inference_steps in zip([1, 5, 10]    ,	[10, 50, 1_00]    ):
               self.check_over_forward(num_inference_steps=a_    )






   def        _lowerCAmelCase    (		self   :		Dict    ) ->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__			: Union[str, Any]  					=       27

         for scheduler_class in self.scheduler_classes:
               lowerCAmelCase__			: List[str]  					=       self.dummy_sample
               lowerCAmelCase__			: Dict  					=       0.1 * sample

               lowerCAmelCase__			: Tuple  					=       self.get_scheduler_config()
               lowerCAmelCase__			: List[str]  					=       scheduler_class(**a_    )

               scheduler.set_timesteps(a_    )

               # before power of 3 fix, would error on first step, so we only need to do two
               for i, t in enumerate(scheduler.prk_timesteps[:2]    ):
                     lowerCAmelCase__			: Optional[Any]  					=       scheduler.step_prk(a_    ,	a_    ,	a_    ).prev_sample






   def        _lowerCAmelCase    (		self   :		List[str]    ) ->						int:

         """simple docstring"""

         with self.assertRaises(a_    ):
               lowerCAmelCase__			: List[Any]  					=       self.scheduler_classes[0]
               lowerCAmelCase__			: Optional[int]  					=       self.get_scheduler_config()
               lowerCAmelCase__			: Tuple  					=       scheduler_class(**a_    )

               scheduler.step_plms(self.dummy_sample    ,	1    ,	self.dummy_sample    ).prev_sample






   def        _lowerCAmelCase    (		self   :		Union[str, Any]    ) ->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__			: str  					=       self.full_loop()
         lowerCAmelCase__			: Any  					=       torch.sum(torch.abs(a_    )    )
         lowerCAmelCase__			: Dict  					=       torch.mean(torch.abs(a_    )    )

         assert abs(result_sum.item() - 198.1318    ) < 1E-2
         assert abs(result_mean.item() - 0.2580    ) < 1E-3






   def        _lowerCAmelCase    (		self   :		Optional[Any]    ) ->						List[str]:

         """simple docstring"""
         lowerCAmelCase__			: int  					=       self.full_loop(prediction_type="""v_prediction"""    )
         lowerCAmelCase__			: str  					=       torch.sum(torch.abs(a_    )    )
         lowerCAmelCase__			: int  					=       torch.mean(torch.abs(a_    )    )

         assert abs(result_sum.item() - 67.3986    ) < 1E-2
         assert abs(result_mean.item() - 0.0878    ) < 1E-3






   def        _lowerCAmelCase    (		self   :		Tuple    ) ->						str:

         """simple docstring"""
         lowerCAmelCase__			: int  					=       self.full_loop(set_alpha_to_one=a_    ,	beta_start=0.01    )
         lowerCAmelCase__			: Any  					=       torch.sum(torch.abs(a_    )    )
         lowerCAmelCase__			: int  					=       torch.mean(torch.abs(a_    )    )

         assert abs(result_sum.item() - 230.0399    ) < 1E-2
         assert abs(result_mean.item() - 0.2995    ) < 1E-3






   def        _lowerCAmelCase    (		self   :		Any    ) ->						Tuple:

         """simple docstring"""
         lowerCAmelCase__			: Optional[Any]  					=       self.full_loop(set_alpha_to_one=a_    ,	beta_start=0.01    )
         lowerCAmelCase__			: Any  					=       torch.sum(torch.abs(a_    )    )
         lowerCAmelCase__			: List[str]  					=       torch.mean(torch.abs(a_    )    )

         assert abs(result_sum.item() - 186.9482    ) < 1E-2
         assert abs(result_mean.item() - 0.2434    ) < 1E-3









"""simple docstring"""
from sklearn.metrics import recall_score

import datasets


SCREAMING_SNAKE_CASE :    Dict									=				"""
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


SCREAMING_SNAKE_CASE :    Any									=				"""
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


SCREAMING_SNAKE_CASE :    Tuple									=				"""
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""



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






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return datasets.MetricInfo(
			    description=_DESCRIPTION				,       citation=_CITATION				,       inputs_description=_KWARGS_DESCRIPTION				,       features=datasets.Features(
			        {
			            '''predictions''': datasets.Sequence(datasets.Value('''int32'''					)					),
			            '''references''': datasets.Sequence(datasets.Value('''int32'''					)					),
			        }
			        if self.config_name == '''multilabel'''
			        else {
			            '''predictions''': datasets.Value('''int32'''					),
			            '''references''': datasets.Value('''int32'''					),
			        }					)				,       reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html''']				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_=None				,       a_=1				,       a_="binary"				,       a_=None				,       a_="warn"				,       ):





			'''simple docstring'''

			__snake_case	:						Any						=				recall_score(
			    a_				,       a_				,       labels=a_				,       pos_label=a_				,       average=a_				,       sample_weight=a_				,       zero_division=a_				,       )
			return {"recall": float(a_					) if score.size == 1 else score}





