


'''simple docstring'''






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 	a__       (							_snake_case						):





							'''simple docstring'''






							lowercase__    :     int					=   (PNDMScheduler,)
							lowercase__    :     List[Any]					=   (("""num_inference_steps""", 5_0),)







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					**lowerCamelCase_     ) ->							Any:
														lowerCAmelCase__      =			{
														    "num_train_timesteps": 10_00,
														    "beta_start": 0.0_001,
														    "beta_end": 0.02,
														    "beta_schedule": "linear",
														}

														config.update(**snake_case_     )
														return config







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_=0      ,					**lowerCamelCase_     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			dict(self.forward_default_kwargs     )
														lowerCAmelCase__      =			kwargs.pop('''num_inference_steps'''      ,					snake_case_     )
														lowerCAmelCase__      =			self.dummy_sample
														lowerCAmelCase__      =			0.1 * sample
														lowerCAmelCase__      =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

																					lowerCAmelCase__      =			scheduler.step_prk(snake_case_      ,					snake_case_      ,					snake_case_      ,					**snake_case_     ).prev_sample
																					lowerCAmelCase__      =			new_scheduler.step_prk(snake_case_      ,					snake_case_      ,					snake_case_      ,					**snake_case_     ).prev_sample

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

																					lowerCAmelCase__      =			scheduler.step_plms(snake_case_      ,					snake_case_      ,					snake_case_      ,					**snake_case_     ).prev_sample
																					lowerCAmelCase__      =			new_scheduler.step_plms(snake_case_      ,					snake_case_      ,					snake_case_      ,					**snake_case_     ).prev_sample

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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														pass







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_=0      ,					**lowerCamelCase_     ) ->							Tuple:
														lowerCAmelCase__      =			dict(self.forward_default_kwargs     )
														lowerCAmelCase__      =			kwargs.pop('''num_inference_steps'''      ,					snake_case_     )
														lowerCAmelCase__      =			self.dummy_sample
														lowerCAmelCase__      =			0.1 * sample
														lowerCAmelCase__      =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

														for scheduler_class in self.scheduler_classes:
																					lowerCAmelCase__      =			self.get_scheduler_config()
																					lowerCAmelCase__      =			scheduler_class(**snake_case_     )
																					scheduler.set_timesteps(snake_case_     )

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

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

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

																					lowerCAmelCase__      =			scheduler.step_prk(snake_case_      ,					snake_case_      ,					snake_case_      ,					**snake_case_     ).prev_sample
																					lowerCAmelCase__      =			new_scheduler.step_prk(snake_case_      ,					snake_case_      ,					snake_case_      ,					**snake_case_     ).prev_sample

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

																					lowerCAmelCase__      =			scheduler.step_plms(snake_case_      ,					snake_case_      ,					snake_case_      ,					**snake_case_     ).prev_sample
																					lowerCAmelCase__      =			new_scheduler.step_plms(snake_case_      ,					snake_case_      ,					snake_case_      ,					**snake_case_     ).prev_sample

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







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					**lowerCamelCase_     ) ->							List[str]:
														lowerCAmelCase__      =			self.scheduler_classes[0]
														lowerCAmelCase__      =			self.get_scheduler_config(**snake_case_     )
														lowerCAmelCase__      =			scheduler_class(**snake_case_     )

														lowerCAmelCase__      =			10
														lowerCAmelCase__      =			self.dummy_model()
														lowerCAmelCase__      =			self.dummy_sample_deter
														scheduler.set_timesteps(snake_case_     )

														for i, t in enumerate(scheduler.prk_timesteps     ):
																					lowerCAmelCase__      =			model(snake_case_      ,					snake_case_     )
																					lowerCAmelCase__      =			scheduler.step_prk(snake_case_      ,					snake_case_      ,					snake_case_     ).prev_sample

														for i, t in enumerate(scheduler.plms_timesteps     ):
																					lowerCAmelCase__      =			model(snake_case_      ,					snake_case_     )
																					lowerCAmelCase__      =			scheduler.step_plms(snake_case_      ,					snake_case_      ,					snake_case_     ).prev_sample

														return sample







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														lowerCAmelCase__      =			dict(self.forward_default_kwargs     )

														lowerCAmelCase__      =			kwargs.pop('''num_inference_steps'''      ,					snake_case_     )

														for scheduler_class in self.scheduler_classes:
																					lowerCAmelCase__      =			self.get_scheduler_config()
																					lowerCAmelCase__      =			scheduler_class(**snake_case_     )

																					lowerCAmelCase__      =			self.dummy_sample
																					lowerCAmelCase__      =			0.1 * sample

																					if num_inference_steps is not None and hasattr(snake_case_      ,					'''set_timesteps'''     ):
																												scheduler.set_timesteps(snake_case_     )
																					elif num_inference_steps is not None and not hasattr(snake_case_      ,					'''set_timesteps'''     ):
																												lowerCAmelCase__      =			num_inference_steps

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

																					lowerCAmelCase__      =			scheduler.step_prk(snake_case_      ,					0      ,					snake_case_      ,					**snake_case_     ).prev_sample
																					lowerCAmelCase__      =			scheduler.step_prk(snake_case_      ,					1      ,					snake_case_      ,					**snake_case_     ).prev_sample

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

																					lowerCAmelCase__      =			scheduler.step_plms(snake_case_      ,					0      ,					snake_case_      ,					**snake_case_     ).prev_sample
																					lowerCAmelCase__      =			scheduler.step_plms(snake_case_      ,					1      ,					snake_case_      ,					**snake_case_     ).prev_sample

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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														for timesteps in [1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=snake_case_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														for steps_offset in [0, 1]:
																					self.check_over_configs(steps_offset=snake_case_     )

														lowerCAmelCase__      =			self.scheduler_classes[0]
														lowerCAmelCase__      =			self.get_scheduler_config(steps_offset=1     )
														lowerCAmelCase__      =			scheduler_class(**snake_case_     )
														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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														for beta_start, beta_end in zip([0.0_001, 0.001]      ,					[0.002, 0.02]     ):
																					self.check_over_configs(beta_start=snake_case_      ,					beta_end=snake_case_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=snake_case_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														for prediction_type in ["epsilon", "v_prediction"]:
																					self.check_over_configs(prediction_type=snake_case_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														for t in [1, 5, 10]:
																					self.check_over_forward(time_step=snake_case_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														for t, num_inference_steps in zip([1, 5, 10]      ,					[10, 50, 1_00]     ):
																					self.check_over_forward(num_inference_steps=snake_case_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Union[str, Any]:
														# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
														lowerCAmelCase__      =			27

														for scheduler_class in self.scheduler_classes:
																					lowerCAmelCase__      =			self.dummy_sample
																					lowerCAmelCase__      =			0.1 * sample

																					lowerCAmelCase__      =			self.get_scheduler_config()
																					lowerCAmelCase__      =			scheduler_class(**snake_case_     )

																					scheduler.set_timesteps(snake_case_     )

																					# 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__      =			scheduler.step_prk(snake_case_      ,					snake_case_      ,					snake_case_     ).prev_sample







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														with self.assertRaises(snake_case_     ):
																					lowerCAmelCase__      =			self.scheduler_classes[0]
																					lowerCAmelCase__      =			self.get_scheduler_config()
																					lowerCAmelCase__      =			scheduler_class(**snake_case_     )

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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			self.full_loop()
														lowerCAmelCase__      =			torch.sum(torch.abs(snake_case_     )     )
														lowerCAmelCase__      =			torch.mean(torch.abs(snake_case_     )     )

														assert abs(result_sum.item() - 198.1_318     ) < 1e-2
														assert abs(result_mean.item() - 0.2_580     ) < 1e-3







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														lowerCAmelCase__      =			self.full_loop(prediction_type='''v_prediction'''     )
														lowerCAmelCase__      =			torch.sum(torch.abs(snake_case_     )     )
														lowerCAmelCase__      =			torch.mean(torch.abs(snake_case_     )     )

														assert abs(result_sum.item() - 67.3_986     ) < 1e-2
														assert abs(result_mean.item() - 0.0_878     ) < 1e-3







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							int:
														# We specify different beta, so that the first alpha is 0.99
														lowerCAmelCase__      =			self.full_loop(set_alpha_to_one=snake_case_      ,					beta_start=0.01     )
														lowerCAmelCase__      =			torch.sum(torch.abs(snake_case_     )     )
														lowerCAmelCase__      =			torch.mean(torch.abs(snake_case_     )     )

														assert abs(result_sum.item() - 230.0_399     ) < 1e-2
														assert abs(result_mean.item() - 0.2_995     ) < 1e-3







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														# We specify different beta, so that the first alpha is 0.99
														lowerCAmelCase__      =			self.full_loop(set_alpha_to_one=snake_case_      ,					beta_start=0.01     )
														lowerCAmelCase__      =			torch.sum(torch.abs(snake_case_     )     )
														lowerCAmelCase__      =			torch.mean(torch.abs(snake_case_     )     )

														assert abs(result_sum.item() - 186.9_482     ) < 1e-2
														assert abs(result_mean.item() - 0.2_434     ) < 1e-3



'''simple docstring'''






import numpy as np
import qiskit


def       _snake_case     (  A = 8				,					A = None )		->					str:
							lowerCAmelCase__      =			np.random.default_rng(seed=A )

							# Roughly 25% of the qubits will contribute to the key.
							# So we take more than we need.
							lowerCAmelCase__      =			6 * key_len
							# Measurement basis for Alice's qubits.
							lowerCAmelCase__      =			rng.integers(2				,					size=A )
							# The set of states Alice will prepare.
							lowerCAmelCase__      =			rng.integers(2				,					size=A )
							# Measurement basis for Bob's qubits.
							lowerCAmelCase__      =			rng.integers(2				,					size=A )

							# Quantum Circuit to simulate BB84
							lowerCAmelCase__      =			qiskit.QuantumCircuit(A				,					name='''BB84''' )

							# Alice prepares her qubits according to rules above.
							for index, _ in enumerate(A ):
														if alice_state[index] == 1:
																					bbaa_circ.x(A )
														if alice_basis[index] == 1:
																					bbaa_circ.h(A )
							bbaa_circ.barrier()

							# Bob measures the received qubits according to rules above.
							for index, _ in enumerate(A ):
														if bob_basis[index] == 1:
																					bbaa_circ.h(A )

							bbaa_circ.barrier()
							bbaa_circ.measure_all()

							# Simulate the quantum circuit.
							lowerCAmelCase__      =			qiskit.Aer.get_backend('''aer_simulator''' )
							# We only need to run one shot because the key is unique.
							# Multiple shots will produce the same key.
							lowerCAmelCase__      =			qiskit.execute(A				,					A				,					shots=1				,					seed_simulator=A )
							# Returns the result of measurement.
							lowerCAmelCase__      =			job.result().get_counts(A ).most_frequent()

							# Extracting the generated key from the simulation results.
							# Only keep measurement results where Alice and Bob chose the same basis.
							lowerCAmelCase__      =			''''''.join(
							    [
							        result_bit
							        for alice_basis_bit, bob_basis_bit, result_bit in zip(
							            A				,					A				,					A )
							        if alice_basis_bit == bob_basis_bit
							    ] )

							# Get final key. Pad with 0 if too short, otherwise truncate.
							lowerCAmelCase__      =			gen_key[:key_len] if len(A ) >= key_len else gen_key.ljust(A				,					'''0''' )
							return key


if __name__ == "__main__":
		print(f"""The generated key is : {bbaa(8, seed=0)}""")
		from doctest import testmod

		testmod()