




"""simple docstring"""


from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = ["vqvae"]


 def __init__(    self:		List[Any],				a_:		AutoencoderKL,				a_:		UNetaDConditionModel,				a_:		Mel,				a_:		Union[DDIMScheduler, DDPMScheduler],				):




   '''simple docstring'''



   super().__init__()
   self.register_modules(unet=a_,				scheduler=a_,				mel=a_,				vqvae=a_		)


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   return 50 if isinstance(self.scheduler,				a_		) else 1_000


 @torch.no_grad()
 def __call__(    self:		str,				a_:		int = 1,				a_:		str = None,				a_:		np.ndarray = None,				a_:		int = 0,				a_:		int = 0,				a_:		int = None,				a_:		torch.Generator = None,				a_:		float = 0,				a_:		float = 0,				a_:		torch.Generator = None,				a_:		float = 0,				a_:		torch.Tensor = None,				a_:		torch.Tensor = None,				a_:		List[Any]=True,				):




   '''simple docstring'''



   _snake_case							:		int								=  steps or self.get_default_steps()
   self.scheduler.set_timesteps(a_		)
   _snake_case							:		Optional[Any]								=  step_generator or generator
   # For backwards compatibility
   if type(self.unet.config.sample_size		) == int:
     _snake_case							:		List[Any]								=  (self.unet.config.sample_size, self.unet.config.sample_size)
   if noise is None:
     _snake_case							:		int								=  randn_tensor(
         (
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size[0],
             self.unet.config.sample_size[1],
         ),				generator=a_,				device=self.device,				)
   _snake_case							:		int								=  noise
   _snake_case							:		Optional[int]								=  None

   if audio_file is not None or raw_audio is not None:
     self.mel.load_audio(a_,				a_		)
     _snake_case							:		int								=  self.mel.audio_slice_to_image(a_		)
     _snake_case							:		Optional[int]								=  np.frombuffer(input_image.tobytes(),				dtype="""uint8"""		).reshape(
         (input_image.height, input_image.width)		)
     _snake_case							:		Optional[Any]								=  (input_image / 255) * 2 - 1
     _snake_case							:		Any								=  torch.tensor(input_image[np.newaxis, :, :],				dtype=torch.float		).to(self.device		)

     if self.vqvae is not None:
       _snake_case							:		List[Any]								=  self.vqvae.encode(torch.unsqueeze(a_,				0		)		).latent_dist.sample(
           generator=a_		)[0]
       _snake_case							:		Union[str, Any]								=  self.vqvae.config.scaling_factor * input_images

     if start_step > 0:
       _snake_case							:		Tuple								=  self.scheduler.add_noise(a_,				a_,				self.scheduler.timesteps[start_step - 1]		)

     _snake_case							:		Optional[Any]								=  (
         self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
     )
     _snake_case							:		Optional[int]								=  int(mask_start_secs * pixels_per_second		)
     _snake_case							:		Tuple								=  int(mask_end_secs * pixels_per_second		)
     _snake_case							:		Optional[Any]								=  self.scheduler.add_noise(a_,				a_,				torch.tensor(self.scheduler.timesteps[start_step:]		)		)

   for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
     if isinstance(self.unet,				a_		):
       _snake_case							:		List[str]								=  self.unet(a_,				a_,				a_		)["""sample"""]
     else:
       _snake_case							:		Tuple								=  self.unet(a_,				a_		)["""sample"""]

     if isinstance(self.scheduler,				a_		):
       _snake_case							:		Optional[int]								=  self.scheduler.step(
           model_output=a_,				timestep=a_,				sample=a_,				eta=a_,				generator=a_,				)["""prev_sample"""]
     else:
       _snake_case							:		Tuple								=  self.scheduler.step(
           model_output=a_,				timestep=a_,				sample=a_,				generator=a_,				)["""prev_sample"""]

     if mask is not None:
       if mask_start > 0:
         _snake_case							:		Any								=  mask[:, step, :, :mask_start]
       if mask_end > 0:
         _snake_case							:		Optional[int]								=  mask[:, step, :, -mask_end:]

   if self.vqvae is not None:
     # 0.18215 was scaling factor used in training to ensure unit variance
     _snake_case							:		Tuple								=  1 / self.vqvae.config.scaling_factor * images
     _snake_case							:		Optional[Any]								=  self.vqvae.decode(a_		)["""sample"""]

   _snake_case							:		str								=  (images / 2 + 0.5).clamp(0,				1		)
   _snake_case							:		Optional[int]								=  images.cpu().permute(0,				2,				3,				1		).numpy()
   _snake_case							:		Tuple								=  (images * 255).round().astype("""uint8"""		)
   _snake_case							:		Optional[int]								=  list(
       (Image.fromarray(_[:, :, 0]		) for _ in images)
       if images.shape[3] == 1
       else (Image.fromarray(a_,				mode="""RGB"""		).convert("""L"""		) for _ in images)		)

   _snake_case							:		Optional[int]								=  [self.mel.image_to_audio(a_		) for _ in images]
   if not return_dict:
     return images, (self.mel.get_sample_rate(), audios)

   return BaseOutput(**AudioPipelineOutput(np.array(a_		)[:, np.newaxis, :]		),				**ImagePipelineOutput(a_		)		)


 @torch.no_grad()
 def 			UpperCamelCase_       (    self:		Any,				a_:		List[Image.Image],				a_:		int = 50		):




   '''simple docstring'''



   assert isinstance(self.scheduler,				a_		)
   self.scheduler.set_timesteps(a_		)
   _snake_case							:		Dict								=  np.array(
       [np.frombuffer(image.tobytes(),				dtype="""uint8"""		).reshape((1, image.height, image.width)		) for image in images]		)
   _snake_case							:		Optional[Any]								=  (sample / 255) * 2 - 1
   _snake_case							:		Tuple								=  torch.Tensor(a_		).to(self.device		)

   for t in self.progress_bar(torch.flip(self.scheduler.timesteps,				(0,)		)		):
     _snake_case							:		Optional[int]								=  t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
     _snake_case							:		Union[str, Any]								=  self.scheduler.alphas_cumprod[t]
     _snake_case							:		Optional[int]								=  (
         self.scheduler.alphas_cumprod[prev_timestep]
         if prev_timestep >= 0
         else self.scheduler.final_alpha_cumprod
     )
     _snake_case							:		Union[str, Any]								=  1 - alpha_prod_t
     _snake_case							:		Union[str, Any]								=  self.unet(a_,				a_		)["""sample"""]
     _snake_case							:		Optional[Any]								=  (1 - alpha_prod_t_prev) ** 0.5 * model_output
     _snake_case							:		Tuple								=  (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
     _snake_case							:		Dict								=  sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

   return sample


 @staticmethod
 def 			UpperCamelCase_       (    a_:		torch.Tensor,				a_:		torch.Tensor,				a_:		float		):




   '''simple docstring'''



   _snake_case							:		str								=  acos(torch.dot(torch.flatten(a_		),				torch.flatten(a_		)		) / torch.norm(a_		) / torch.norm(a_		)		)
   return sin((1 - alpha) * theta		) * xa / sin(a_		) + sin(alpha * theta		) * xa / sin(a_		)








"""simple docstring"""


import math

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


def  UpperCAmelCase__	(snake_case__							: int = 3   ):
  """simple docstring"""

  if isinstance(snake_case__		,       snake_case__   ):
    raise TypeError("""number of qubits must be a integer."""   )
  if number_of_qubits <= 0:
    raise ValueError("""number of qubits must be > 0."""   )
  if math.floor(snake_case__   ) != number_of_qubits:
    raise ValueError("""number of qubits must be exact integer."""   )
  if number_of_qubits > 10:
    raise ValueError("""number of qubits too large to simulate(>10)."""   )

  _snake_case							:		Optional[int]								=  QuantumRegister(snake_case__		,       """qr"""   )
  _snake_case							:		List[Any]								=  ClassicalRegister(snake_case__		,       """cr"""   )

  _snake_case							:		Optional[int]								=  QuantumCircuit(snake_case__		,       snake_case__   )

  _snake_case							:		Dict								=  number_of_qubits

  for i in range(snake_case__   ):
    quantum_circuit.h(number_of_qubits - i - 1   )
    counter -= 1
    for j in range(snake_case__   ):
      quantum_circuit.cp(np.pi / 2 ** (counter - j)		,       snake_case__		,       snake_case__   )

  for k in range(number_of_qubits // 2   ):
    quantum_circuit.swap(snake_case__		,       number_of_qubits - k - 1   )

  # measure all the qubits
  quantum_circuit.measure(snake_case__		,       snake_case__   )
  # simulate with 10000 shots
  _snake_case							:		Optional[int]								=  Aer.get_backend("""qasm_simulator"""   )
  _snake_case							:		Optional[int]								=  execute(snake_case__		,       snake_case__		,       shots=1_00_00   )

  return job.result().get_counts(snake_case__   )


if __name__ == "__main__":
     print(
         F'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
     )


