



"""simple docstring"""

import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format='%(message)s')
def  __snake_case							(	__A      :						np.ndarray     )     ->       np.ndarray:






				'''simple docstring'''
				return input_array.reshape((input_array.size, 1)     )
def  __snake_case							(	__A      :						np.ndarray							,		__A      :						np.ndarray							,		__A      :						int     )     ->       np.ndarray:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     np.nan
				for i in range(__A     ):
								SCREAMING_SNAKE_CASE						:							Dict							=     features[:, labels == i]
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     data.mean(1     )
								# Centralize the data of class i
								SCREAMING_SNAKE_CASE						:							Any							=     data - column_reshape(__A     )
								if i > 0:
												# If covariance_sum is not None
												covariance_sum += np.dot(__A							,		centered_data.T     )
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     np.dot(__A							,		centered_data.T     )

				return covariance_sum / features.shape[1]
def  __snake_case							(	__A      :						np.ndarray							,		__A      :						np.ndarray							,		__A      :						int     )     ->       np.ndarray:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Optional[Any]							=     features.mean(1     )
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     np.nan
				for i in range(__A     ):
								SCREAMING_SNAKE_CASE						:							Dict							=     features[:, labels == i]
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     data.shape[1]
								SCREAMING_SNAKE_CASE						:							Any							=     data.mean(1     )
								if i > 0:
												# If covariance_sum is not None
												covariance_sum += device_data * np.dot(
												    column_reshape(__A     ) - column_reshape(__A     )							,		(column_reshape(__A     ) - column_reshape(__A     )).T							,		)
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												SCREAMING_SNAKE_CASE						:							Optional[Any]							=     device_data * np.dot(
												    column_reshape(__A     ) - column_reshape(__A     )							,		(column_reshape(__A     ) - column_reshape(__A     )).T							,		)

				return covariance_sum / features.shape[1]
def  __snake_case							(	__A      :						np.ndarray							,		__A      :						int     )     ->       np.ndarray:






				'''simple docstring'''

				# Check if the features have been loaded
				if features.any():
								SCREAMING_SNAKE_CASE						:							Tuple							=     features.mean(1     )
								# Center the dataset
								SCREAMING_SNAKE_CASE						:							str							=     features - np.reshape(__A							,		(data_mean.size, 1)     )
								SCREAMING_SNAKE_CASE						:							Any							=     np.dot(__A							,		centered_data.T     ) / features.shape[1]
								SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[Any]							=     np.linalg.eigh(__A     )
								# Take all the columns in the reverse order (-1), and then takes only the first
								SCREAMING_SNAKE_CASE						:							int							=     eigenvectors[:, ::-1][:, 0:dimensions]
								# Project the database on the new space
								SCREAMING_SNAKE_CASE						:							List[str]							=     np.dot(filtered_eigenvectors.T							,		__A     )
								logging.info('Principal Component Analysis computed'     )

								return projected_data
				else:
								logging.basicConfig(level=logging.ERROR							,		format='%(message)s'							,		force=__A     )
								logging.error('Dataset empty'     )
								raise AssertionError
def  __snake_case							(	__A      :						np.ndarray							,		__A      :						np.ndarray							,		__A      :						int							,		__A      :						int     )     ->       np.ndarray:






				'''simple docstring'''
				assert classes > dimensions

				# Check if features have been already loaded
				if features.any:
								SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Tuple							=     eigh(
								    covariance_between_classes(__A							,		__A							,		__A     )							,		covariance_within_classes(__A							,		__A							,		__A     )							,		)
								SCREAMING_SNAKE_CASE						:							List[str]							=     eigenvectors[:, ::-1][:, :dimensions]
								SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     np.linalg.svd(__A     )
								SCREAMING_SNAKE_CASE						:							Any							=     svd_matrix[:, 0:dimensions]
								SCREAMING_SNAKE_CASE						:							Tuple							=     np.dot(filtered_svd_matrix.T							,		__A     )
								logging.info('Linear Discriminant Analysis computed'     )

								return projected_data
				else:
								logging.basicConfig(level=logging.ERROR							,		format='%(message)s'							,		force=__A     )
								logging.error('Dataset empty'     )
								raise AssertionError
def  __snake_case							(	)     ->       None:






				'''simple docstring'''
				# Create dummy dataset with 2 classes and 3 features
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]     )
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     np.array([0, 0, 0, 1, 1]     )
				SCREAMING_SNAKE_CASE						:							str							=     2
				SCREAMING_SNAKE_CASE						:							str							=     2

				# Assert that the function raises an AssertionError if dimensions > classes
				with pytest.raises(__A     ) as error_info:
								SCREAMING_SNAKE_CASE						:							Dict							=     linear_discriminant_analysis(
								    __A							,		__A							,		__A							,		__A     )
								if isinstance(__A							,		np.ndarray     ):
												raise AssertionError(
												    'Did not raise AssertionError for dimensions > classes'     )
								assert error_info.type is AssertionError
def  __snake_case							(	)     ->       None:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							List[str]							=     np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     )
				SCREAMING_SNAKE_CASE						:							Any							=     2
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]     )

				with pytest.raises(__A     ) as error_info:
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     principal_component_analysis(__A							,		__A     )
								if not np.allclose(__A							,		__A     ):
												raise AssertionError
								assert error_info.type is AssertionError


if __name__ == "__main__":
							import doctest

							doctest.testmod()










"""simple docstring"""

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= (DEISMultistepScheduler,)
	_SCREAMING_SNAKE_CASE	:		str  						= (('''num_inference_steps''', 25),)





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       **_SCREAMING_SNAKE_CASE				:       int    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     {
					    'num_train_timesteps': 1_000,
					    'beta_start': 0.0_0_0_1,
					    'beta_end': 0.0_2,
					    'beta_schedule': 'linear',
					    'solver_order': 2,
					}

					config.update(**_SCREAMING_SNAKE_CASE    )
					return config





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       int=0     ,       **_SCREAMING_SNAKE_CASE				:       Optional[int]    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     dict(self.forward_default_kwargs    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.dummy_sample
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     0.1 * sample
					SCREAMING_SNAKE_CASE						:							Any							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE						:							List[str]							=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
									# copy over dummy past residuals
									SCREAMING_SNAKE_CASE						:							Dict							=     dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE    )
													new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
													# copy over dummy past residuals
													SCREAMING_SNAKE_CASE						:							List[str]							=     dummy_past_residuals[: new_scheduler.config.solver_order]

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

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





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





					"""simple docstring"""


					pass





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     dict(self.forward_default_kwargs    )
					SCREAMING_SNAKE_CASE						:							Dict							=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.dummy_sample
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     0.1 * sample
					SCREAMING_SNAKE_CASE						:							int							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE						:							Any							=     self.get_scheduler_config()
									SCREAMING_SNAKE_CASE						:							List[str]							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

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

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

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

									SCREAMING_SNAKE_CASE						:							Optional[int]							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									SCREAMING_SNAKE_CASE						:							str							=     new_scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       **_SCREAMING_SNAKE_CASE				:       List[str]    )						->	Any:





					"""simple docstring"""


					if scheduler is None:
									SCREAMING_SNAKE_CASE						:							int							=     self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							List[str]							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     10
					SCREAMING_SNAKE_CASE						:							Any							=     self.dummy_model()
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.dummy_sample_deter
					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

					for i, t in enumerate(scheduler.timesteps    ):
									SCREAMING_SNAKE_CASE						:							int							=     model(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Tuple							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ).prev_sample

					return sample





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     dict(self.forward_default_kwargs    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     kwargs.pop('num_inference_steps'     ,       _SCREAMING_SNAKE_CASE    )

					for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE						:							int							=     self.get_scheduler_config()
									SCREAMING_SNAKE_CASE						:							Dict							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.dummy_sample
									SCREAMING_SNAKE_CASE						:							Tuple							=     0.1 * sample

									if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE     ,       'set_timesteps'    ):
													scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
									elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE     ,       'set_timesteps'    ):
													SCREAMING_SNAKE_CASE						:							List[str]							=     num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									SCREAMING_SNAKE_CASE						:							Tuple							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     dummy_past_residuals[: scheduler.config.solver_order]

									SCREAMING_SNAKE_CASE						:							Tuple							=     scheduler.timesteps[5]
									SCREAMING_SNAKE_CASE						:							Tuple							=     scheduler.timesteps[6]

									SCREAMING_SNAKE_CASE						:							str							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample
									SCREAMING_SNAKE_CASE						:							List[Any]							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    ).prev_sample

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





	def 			_lowerCAmelCase     (      self				:       Dict    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     DEISMultistepScheduler(**self.get_scheduler_config()    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

					assert abs(result_mean.item() - 0.2_3_9_1_6    ) < 1E-3

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     DPMSolverSinglestepScheduler.from_config(scheduler.config    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     DPMSolverMultistepScheduler.from_config(scheduler.config    )
					SCREAMING_SNAKE_CASE						:							Any							=     UniPCMultistepScheduler.from_config(scheduler.config    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     DEISMultistepScheduler.from_config(scheduler.config    )

					SCREAMING_SNAKE_CASE						:							str							=     self.full_loop(scheduler=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

					assert abs(result_mean.item() - 0.2_3_9_1_6    ) < 1E-3





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





					"""simple docstring"""


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





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





					"""simple docstring"""


					self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE    )
					for order in [1, 2, 3]:
									for solver_type in ["logrho"]:
													for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(
																					    thresholding=_SCREAMING_SNAKE_CASE     ,       prediction_type=_SCREAMING_SNAKE_CASE     ,       sample_max_value=_SCREAMING_SNAKE_CASE     ,       algorithm_type='deis'     ,       solver_order=_SCREAMING_SNAKE_CASE     ,       solver_type=_SCREAMING_SNAKE_CASE     ,       )





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





					"""simple docstring"""


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





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





					"""simple docstring"""


					for algorithm_type in ["deis"]:
									for solver_type in ["logrho"]:
													for order in [1, 2, 3]:
																	for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(
																					    solver_order=_SCREAMING_SNAKE_CASE     ,       solver_type=_SCREAMING_SNAKE_CASE     ,       prediction_type=_SCREAMING_SNAKE_CASE     ,       algorithm_type=_SCREAMING_SNAKE_CASE     ,       )
																					SCREAMING_SNAKE_CASE						:							Dict							=     self.full_loop(
																					    solver_order=_SCREAMING_SNAKE_CASE     ,       solver_type=_SCREAMING_SNAKE_CASE     ,       prediction_type=_SCREAMING_SNAKE_CASE     ,       algorithm_type=_SCREAMING_SNAKE_CASE     ,       )
																					assert not torch.isnan(_SCREAMING_SNAKE_CASE    ).any(), "Samples have nan numbers"





	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	str:





					"""simple docstring"""


					self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )
					self.check_over_configs(lower_order_final=_SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       int    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.full_loop()
					SCREAMING_SNAKE_CASE						:							Tuple							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

					assert abs(result_mean.item() - 0.2_3_9_1_6    ) < 1E-3





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     self.full_loop(prediction_type='v_prediction'    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

					assert abs(result_mean.item() - 0.0_9_1    ) < 1E-3







	def 			_lowerCAmelCase     (      self				:       str    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE						:							Tuple							=     self.get_scheduler_config(thresholding=_SCREAMING_SNAKE_CASE     ,       dynamic_thresholding_ratio=0    )
					SCREAMING_SNAKE_CASE						:							Any							=     scheduler_class(**_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     10
					SCREAMING_SNAKE_CASE						:							Dict							=     self.dummy_model()
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.dummy_sample_deter.half()
					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

					for i, t in enumerate(scheduler.timesteps    ):
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     model(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							int							=     scheduler.step(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ).prev_sample

					assert sample.dtype == torch.floataa





