




'''simple docstring'''





from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


_SCREAMING_SNAKE_CASE							:     Tuple   	=				logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
class 				_snake_case	(			lowercase_							):
			lowerCAmelCase_  :   torch.FloatTensor




class 				_snake_case	(			lowercase_		,		lowercase_							):
			lowerCAmelCase_  :   int		 =					1




			@register_to_config
			def __init__(      self       ,							a__ = 40       ,							a__ = 0.0_0_2       ,							a__ = 8_0.0       ,							a__ = 0.5       ,							a__ = 1.0       ,							a__ = 7.0       ,							a__ = True       ,							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   sigma_max

										snake_case_			      =   np.linspace(0       ,							1       ,							a__							)
										snake_case_			      =   self._convert_to_karras(a__							)
										snake_case_			      =   self.sigma_to_t(a__							)

										# setable values
										snake_case_			      =   None
										snake_case_			      =   torch.from_numpy(a__							)
										snake_case_			      =   torch.from_numpy(a__							)
										snake_case_			      =   False
										snake_case_			      =   False




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__=None							)       ->       Any:

										'''simple docstring'''




										if schedule_timesteps is None:
																	snake_case_			      =   self.timesteps

										snake_case_			      =   (schedule_timesteps == timestep).nonzero()
										return indices.item()




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__							)       ->       torch.FloatTensor:

										'''simple docstring'''




										if isinstance(a__       ,							torch.Tensor							):
																	snake_case_			      =   timestep.to(self.timesteps.device							)
										snake_case_			      =   self.index_for_timestep(a__							)
										snake_case_			      =   self.sigmas[step_idx]

										snake_case_			      =   sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

										snake_case_			      =   True
										return sample




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       List[str]:

										'''simple docstring'''




										if not isinstance(a__       ,							np.ndarray							):
																	snake_case_			      =   np.array(a__       ,							dtype=np.floataa							)

										snake_case_			      =   1_000 * 0.2_5 * np.log(sigmas + 1e-44							)

										return timesteps




			def      lowerCAmelCase__       (      self       ,							a__ = None       ,							a__ = None       ,							a__ = None       ,							)       ->       Dict:

										'''simple docstring'''




										if num_inference_steps is None and timesteps is None:
																	raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."							)

										if num_inference_steps is not None and timesteps is not None:
																	raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."							)

										# Follow DDPMScheduler custom timesteps logic
										if timesteps is not None:
																	for i in range(1       ,							len(a__							)							):
																								if timesteps[i] >= timesteps[i - 1]:
																															raise ValueError("`timesteps` must be in descending order."							)

																	if timesteps[0] >= self.config.num_train_timesteps:
																								raise ValueError(
																								    F'`timesteps` must start before `self.config.train_timesteps`:'
																								    F' {self.config.num_train_timesteps}.'							)

																	snake_case_			      =   np.array(a__       ,							dtype=np.intaa							)
																	snake_case_			      =   True
										else:
																	if num_inference_steps > self.config.num_train_timesteps:
																								raise ValueError(
																								    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
																								    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
																								    F' maximal {self.config.num_train_timesteps} timesteps.'							)

																	snake_case_			      =   num_inference_steps

																	snake_case_			      =   self.config.num_train_timesteps // self.num_inference_steps
																	snake_case_			      =   (np.arange(0       ,							a__							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
																	snake_case_			      =   False

										# Map timesteps to Karras sigmas directly for multistep sampling
										# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
										snake_case_			      =   self.config.num_train_timesteps
										snake_case_			      =   timesteps[::-1].copy()
										snake_case_			      =   ramp / (num_train_timesteps - 1)
										snake_case_			      =   self._convert_to_karras(a__							)
										snake_case_			      =   self.sigma_to_t(a__							)

										snake_case_			      =   np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
										snake_case_			      =   torch.from_numpy(a__							).to(device=a__							)

										if str(a__							).startswith("mps"							):
																	# mps does not support float64
																	snake_case_			      =   torch.from_numpy(a__							).to(a__       ,							dtype=torch.floataa							)
										else:
																	snake_case_			      =   torch.from_numpy(a__							).to(device=a__							)




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       str:

										'''simple docstring'''




										snake_case_			      =   self.config.sigma_min
										snake_case_			      =   self.config.sigma_max

										snake_case_			      =   self.config.rho
										snake_case_			      =   sigma_min ** (1 / rho)
										snake_case_			      =   sigma_max ** (1 / rho)
										snake_case_			      =   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   self.config.sigma_data

										snake_case_			      =   sigma_data**2 / (sigma**2 + sigma_data**2)
										snake_case_			      =   sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   self.config.sigma_min
										snake_case_			      =   self.config.sigma_data

										snake_case_			      =   sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
										snake_case_			      =   (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__       ,							a__       ,							a__ = None       ,							a__ = True       ,							)       ->       Union[CMStochasticIterativeSchedulerOutput, Tuple]:

										'''simple docstring'''




										if (
										    isinstance(a__       ,							a__							)
										    or isinstance(a__       ,							torch.IntTensor							)
										    or isinstance(a__       ,							torch.LongTensor							)
										):
																	raise ValueError(
																	    (
																	        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
																	        F' `{self.__class__}.step()` is not supported. Make sure to pass'
																	        " one of the `scheduler.timesteps` as a timestep."
																	    )       ,							)

										if not self.is_scale_input_called:
																	logger.warning(
																	    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
																	    "See `StableDiffusionPipeline` for a usage example."							)

										if isinstance(a__       ,							torch.Tensor							):
																	snake_case_			      =   timestep.to(self.timesteps.device							)

										snake_case_			      =   self.config.sigma_min
										snake_case_			      =   self.config.sigma_max

										snake_case_			      =   self.index_for_timestep(a__							)

										# sigma_next corresponds to next_t in original implementation
										snake_case_			      =   self.sigmas[step_index]
										if step_index + 1 < self.config.num_train_timesteps:
																	snake_case_			      =   self.sigmas[step_index + 1]
										else:
																	# Set sigma_next to sigma_min
																	snake_case_			      =   self.sigmas[-1]

										# Get scalings for boundary conditions
										snake_case_      ,   snake_case_			      =   self.get_scalings_for_boundary_condition(a__							)

										# 1. Denoise model output using boundary conditions
										snake_case_			      =   c_out * model_output + c_skip * sample
										if self.config.clip_denoised:
																	snake_case_			      =   denoised.clamp(-1       ,							1							)

										# 2. Sample z ~ N(0, s_noise^2 * I)
										# Noise is not used for onestep sampling.
										if len(self.timesteps							) > 1:
																	snake_case_			      =   randn_tensor(
																	    model_output.shape       ,							dtype=model_output.dtype       ,							device=model_output.device       ,							generator=a__							)
										else:
																	snake_case_			      =   torch.zeros_like(a__							)
										snake_case_			      =   noise * self.config.s_noise

										snake_case_			      =   sigma_next.clamp(min=a__       ,							max=a__							)

										# 3. Return noisy sample
										# tau = sigma_hat, eps = sigma_min
										snake_case_			      =   denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

										if not return_dict:
																	return (prev_sample,)

										return CMStochasticIterativeSchedulerOutput(prev_sample=a__							)




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__       ,							a__       ,							)       ->       torch.FloatTensor:

										'''simple docstring'''




										snake_case_			      =   self.sigmas.to(device=original_samples.device       ,							dtype=original_samples.dtype							)
										if original_samples.device.type == "mps" and torch.is_floating_point(a__							):
																	# mps does not support float64
																	snake_case_			      =   self.timesteps.to(original_samples.device       ,							dtype=torch.floataa							)
																	snake_case_			      =   timesteps.to(original_samples.device       ,							dtype=torch.floataa							)
										else:
																	snake_case_			      =   self.timesteps.to(original_samples.device							)
																	snake_case_			      =   timesteps.to(original_samples.device							)

										snake_case_			      =   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

										snake_case_			      =   sigmas[step_indices].flatten()
										while len(sigma.shape							) < len(original_samples.shape							):
																	snake_case_			      =   sigma.unsqueeze(-1							)

										snake_case_			      =   original_samples + noise * sigma
										return noisy_samples




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

										'''simple docstring'''




										return self.config.num_train_timesteps










'''simple docstring'''





import qiskit
from qiskit.providers import Backend


def   UpperCamelCase_(			snake_case							: int							,       snake_case							: int  ):

							'''simple docstring'''






							snake_case_      ,   snake_case_			      =   bin(snake_case  )[2:], bin(snake_case  )[2:]  # Remove leading '0b'

							# Ensure that both strings are of the same length
							if len(snake_case  ) > len(snake_case  ):
														snake_case_			      =   y.zfill(len(snake_case  )  )
							else:
														snake_case_			      =   x.zfill(len(snake_case  )  )

							# We need (3 * number of bits in the larger number)+1 qBits
							# The second parameter is the number of classical registers, to measure the result
							snake_case_			      =   qiskit.QuantumCircuit((len(snake_case  ) * 3) + 1							,       len(snake_case  ) + 1  )

							# We are essentially "not-ing" the bits that are 1
							# Reversed because it's easier to perform ops on more significant bits
							for i in range(len(snake_case  )  ):
														if x[::-1][i] == "1":
																					circuit.x(snake_case  )
							for j in range(len(snake_case  )  ):
														if y[::-1][j] == "1":
																					circuit.x(len(snake_case  ) + j  )

							return circuit, x, y


def   UpperCamelCase_(			snake_case							: qiskit.QuantumCircuit							,       snake_case							: int							,       snake_case							: int							,       snake_case							: int							,       snake_case							: int							,       ):

							'''simple docstring'''






							circuit.ccx(snake_case							,       snake_case							,       snake_case  )
							circuit.cx(snake_case							,       snake_case  )
							circuit.ccx(snake_case							,       snake_case							,       snake_case  )
							circuit.cx(snake_case							,       snake_case  )
							circuit.cx(snake_case							,       snake_case  )


def   UpperCamelCase_(			snake_case							: int							,       snake_case							: int							,       snake_case							: Backend = qiskit.Aer.get_backend("aer_simulator"  )							,       ):

							'''simple docstring'''






							if vala < 0 or vala < 0:
														raise ValueError("Both Integers must be positive!"  )

							# Store the Integers
							snake_case_      ,   snake_case_      ,   snake_case_			      =   store_two_classics(snake_case							,       snake_case  )
							for i in range(len(snake_case  )  ):
														full_adder(snake_case							,       snake_case							,       len(snake_case  ) + i							,       len(snake_case  ) + len(snake_case  ) + i							,       len(snake_case  ) + len(snake_case  ) + i + 1  )
														circuit.barrier()  # Optional, just for aesthetics

							# Measure the resultant qBits
							for i in range(len(snake_case  ) + 1  ):
														circuit.measure([(len(snake_case  ) * 2) + i]							,       [i]  )

							snake_case_			      =   qiskit.execute(snake_case							,       snake_case							,       shots=1  ).result()

							# The result is in binary. Convert it back to int
							return int(list(res.get_counts()  )[0]							,       2  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




