
"""simple docstring"""
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class  _snake_case	(    unittest.TestCase							):
							# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
							SCREAMING_SNAKE_CASE		  =    (ScoreSdeVeScheduler,)
							SCREAMING_SNAKE_CASE		  =    ()






							@property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												__a:    Any		     =       4
												__a:    Any		     =       3
												__a:    Union[str, Any]		     =       8
												__a:    Any		     =       8

												__a:    Union[str, Any]		     =       torch.rand((batch_size, num_channels, height, width)     )

												return sample






							@property
							def 				__lowercase	(       self    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    str		     =       4
												__a:    int		     =       3
												__a:    List[str]		     =       8
												__a:    Optional[int]		     =       8

												__a:    Optional[int]		     =       batch_size * num_channels * height * width
												__a:    Optional[Any]		     =       torch.arange(__lowerCamelCase     )
												__a:    Optional[int]		     =       sample.reshape(__lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase     )
												__a:    int		     =       sample / num_elems
												__a:    Optional[int]		     =       sample.permute(3    ,			0    ,			1    ,			2     )

												return sample






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												def model(snake_case__    :      Tuple    ,			snake_case__    :      Any    ,			*snake_case__    :      List[str]     ):
																	return sample * t / (t + 1)

												return model






							def 				__lowercase	(       self    :      Dict    ,			**snake_case__    :      Tuple     ):






												"""simple docstring"""



												__a:    Dict		     =       {
												    """num_train_timesteps""": 2_0_0_0,
												    """snr""": 0.15,
												    """sigma_min""": 0.01,
												    """sigma_max""": 1_3_4_8,
												    """sampling_eps""": 1e-5,
												}

												config.update(**__lowerCamelCase     )
												return config






							def 				__lowercase	(       self    :      Union[str, Any]    ,			snake_case__    :      Any=0    ,			**snake_case__    :      Optional[Any]     ):






												"""simple docstring"""



												__a:    Tuple		     =       dict(self.forward_default_kwargs     )

												for scheduler_class in self.scheduler_classes:
																	__a:    Union[str, Any]		     =       self.dummy_sample
																	__a:    List[Any]		     =       0.1 * sample

																	__a:    List[Any]		     =       self.get_scheduler_config(**__lowerCamelCase     )
																	__a:    Any		     =       scheduler_class(**__lowerCamelCase     )

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(__lowerCamelCase     )
																						__a:    Tuple		     =       scheduler_class.from_pretrained(__lowerCamelCase     )

																	__a:    Optional[int]		     =       scheduler.step_pred(
																	    __lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase    ,			generator=torch.manual_seed(0     )    ,			**__lowerCamelCase     ).prev_sample
																	__a:    Optional[Any]		     =       new_scheduler.step_pred(
																	    __lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase    ,			generator=torch.manual_seed(0     )    ,			**__lowerCamelCase     ).prev_sample

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

																	__a:    List[str]		     =       scheduler.step_correct(__lowerCamelCase    ,			__lowerCamelCase    ,			generator=torch.manual_seed(0     )    ,			**__lowerCamelCase     ).prev_sample
																	__a:    Union[str, Any]		     =       new_scheduler.step_correct(
																	    __lowerCamelCase    ,			__lowerCamelCase    ,			generator=torch.manual_seed(0     )    ,			**__lowerCamelCase     ).prev_sample

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






							def 				__lowercase	(       self    :      Tuple    ,			snake_case__    :      Tuple=0    ,			**snake_case__    :      int     ):






												"""simple docstring"""



												__a:    Any		     =       dict(self.forward_default_kwargs     )
												kwargs.update(__lowerCamelCase     )

												for scheduler_class in self.scheduler_classes:
																	__a:    Tuple		     =       self.dummy_sample
																	__a:    Dict		     =       0.1 * sample

																	__a:    Optional[int]		     =       self.get_scheduler_config()
																	__a:    Optional[int]		     =       scheduler_class(**__lowerCamelCase     )

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(__lowerCamelCase     )
																						__a:    Dict		     =       scheduler_class.from_pretrained(__lowerCamelCase     )

																	__a:    Optional[int]		     =       scheduler.step_pred(
																	    __lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase    ,			generator=torch.manual_seed(0     )    ,			**__lowerCamelCase     ).prev_sample
																	__a:    Union[str, Any]		     =       new_scheduler.step_pred(
																	    __lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase    ,			generator=torch.manual_seed(0     )    ,			**__lowerCamelCase     ).prev_sample

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

																	__a:    int		     =       scheduler.step_correct(__lowerCamelCase    ,			__lowerCamelCase    ,			generator=torch.manual_seed(0     )    ,			**__lowerCamelCase     ).prev_sample
																	__a:    int		     =       new_scheduler.step_correct(
																	    __lowerCamelCase    ,			__lowerCamelCase    ,			generator=torch.manual_seed(0     )    ,			**__lowerCamelCase     ).prev_sample

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






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												for timesteps in [1_0, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=__lowerCamelCase     )






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]    ,			[1, 1_0_0, 1_0_0_0]     ):
																	self.check_over_configs(sigma_min=__lowerCamelCase    ,			sigma_max=__lowerCamelCase     )






							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												for t in [0.1, 0.5, 0.75]:
																	self.check_over_forward(time_step=__lowerCamelCase     )






							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												__a:    Any		     =       dict(self.forward_default_kwargs     )

												__a:    Dict		     =       self.scheduler_classes[0]
												__a:    List[str]		     =       self.get_scheduler_config()
												__a:    List[Any]		     =       scheduler_class(**__lowerCamelCase     )

												__a:    str		     =       3

												__a:    int		     =       self.dummy_model()
												__a:    Any		     =       self.dummy_sample_deter

												scheduler.set_sigmas(__lowerCamelCase     )
												scheduler.set_timesteps(__lowerCamelCase     )
												__a:    int		     =       torch.manual_seed(0     )

												for i, t in enumerate(scheduler.timesteps     ):
																	__a:    Union[str, Any]		     =       scheduler.sigmas[i]

																	for _ in range(scheduler.config.correct_steps     ):
																						with torch.no_grad():
																											__a:    int		     =       model(__lowerCamelCase    ,			__lowerCamelCase     )
																						__a:    int		     =       scheduler.step_correct(__lowerCamelCase    ,			__lowerCamelCase    ,			generator=__lowerCamelCase    ,			**__lowerCamelCase     ).prev_sample

																	with torch.no_grad():
																						__a:    Optional[Any]		     =       model(__lowerCamelCase    ,			__lowerCamelCase     )

																	__a:    Dict		     =       scheduler.step_pred(__lowerCamelCase    ,			__lowerCamelCase    ,			__lowerCamelCase    ,			generator=__lowerCamelCase    ,			**__lowerCamelCase     )
																	__a:    Optional[Any]		     =       output.prev_sample, output.prev_sample_mean

												__a:    Optional[int]		     =       torch.sum(torch.abs(__lowerCamelCase     )     )
												__a:    Union[str, Any]		     =       torch.mean(torch.abs(__lowerCamelCase     )     )

												assert np.isclose(result_sum.item()    ,			14_372_758_528.0     )
												assert np.isclose(result_mean.item()    ,			18_714_530.0     )






							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												__a:    Tuple		     =       dict(self.forward_default_kwargs     )

												__a:    Optional[Any]		     =       kwargs.pop("""num_inference_steps"""    ,			__lowerCamelCase     )

												for scheduler_class in self.scheduler_classes:
																	__a:    str		     =       self.get_scheduler_config()
																	__a:    Dict		     =       scheduler_class(**__lowerCamelCase     )

																	__a:    Optional[Any]		     =       self.dummy_sample
																	__a:    Dict		     =       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"""     ):
																						__a:    List[str]		     =       num_inference_steps

																	__a:    List[str]		     =       scheduler.step_pred(__lowerCamelCase    ,			0    ,			__lowerCamelCase    ,			generator=torch.manual_seed(0     )    ,			**__lowerCamelCase     ).prev_sample
																	__a:    List[str]		     =       scheduler.step_pred(__lowerCamelCase    ,			1    ,			__lowerCamelCase    ,			generator=torch.manual_seed(0     )    ,			**__lowerCamelCase     ).prev_sample

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








import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    float = np.pi / 2      ,     __magic_name__   :    float = np.pi / 2      ,     __magic_name__   :    float = np.pi / 2   )						->     qiskit.result.counts.Counts:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      QuantumRegister(3      ,     """qr"""   )  # Define the number of quantum bits
	UpperCamelCase	:Optional[Any]		=      ClassicalRegister(1      ,     """cr"""   )  # Define the number of classical bits

	UpperCamelCase	:List[Any]		=      QuantumCircuit(__magic_name__      ,     __magic_name__   )  # Define the quantum circuit.

	# Build the circuit
	quantum_circuit.u(__magic_name__      ,     __magic_name__      ,     __magic_name__      ,     0   )  # Quantum State to teleport
	quantum_circuit.h(1   )  # add hadamard gate
	quantum_circuit.cx(
	    1      ,     2   )  # add control gate with qubit 1 as control and 2 as target.
	quantum_circuit.cx(0      ,     1   )
	quantum_circuit.h(0   )
	quantum_circuit.cz(0      ,     2   )  # add control z gate.
	quantum_circuit.cx(1      ,     2   )

	quantum_circuit.measure([2]      ,     [0]   )  # measure the qubit.

	# Simulate the circuit using qasm simulator
	UpperCamelCase	:Union[str, Any]		=      Aer.get_backend("""aer_simulator"""   )
	UpperCamelCase	:Dict		=      execute(__magic_name__      ,     __magic_name__      ,     shots=1000   )

	return job.result().get_counts(__magic_name__   )


if __name__ == "__main__":
	print(
	    '''Total count for teleported state is: '''
	    F'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
	)
