'''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       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   ["vqvae"]

       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       )  ->  Optional[Any]:
        super().__init__()
        self.register_modules(unet=__lowerCAmelCase     ,       scheduler=__lowerCAmelCase     ,       mel=__lowerCAmelCase     ,       vqvae=__lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  int:
        return 50 if isinstance(self.scheduler     ,       __lowerCAmelCase			) else 1000

       @torch.no_grad()
       def __call__(						self     ,       __lowerCAmelCase = 1     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = 0     ,       __lowerCAmelCase = 0     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = 0     ,       __lowerCAmelCase = 0     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = 0     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase=True     ,       )  ->  Union[
           Union[AudioPipelineOutput, ImagePipelineOutput],
           Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
       ]:
        lowercase__    :   Union[str, Any]						= steps or self.get_default_steps()
        self.scheduler.set_timesteps(__lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= step_generator or generator
        # For backwards compatibility
        if type(self.unet.config.sample_size			) == int:
         lowercase__    :   Optional[int]						= (self.unet.config.sample_size, self.unet.config.sample_size)
        if noise is None:
         lowercase__    :   Optional[int]						= randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )     ,       generator=__lowerCAmelCase     ,       device=self.device     ,       )
        lowercase__    :   Optional[int]						= noise
        lowercase__    :   Optional[Any]						= None

        if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(__lowerCAmelCase     ,       __lowerCAmelCase			)
         lowercase__    :   Union[str, Any]						= self.mel.audio_slice_to_image(__lowerCAmelCase			)
         lowercase__    :   str						= np.frombuffer(input_image.tobytes()     ,       dtype='''uint8'''			).reshape(
             (input_image.height, input_image.width)			)
         lowercase__    :   List[str]						= (input_image / 255) * 2 - 1
         lowercase__    :   Dict						= torch.tensor(input_image[np.newaxis, :, :]     ,       dtype=torch.float			).to(self.device			)

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

         if start_step > 0:
          lowercase__    :   Dict						= self.scheduler.add_noise(__lowerCAmelCase     ,       __lowerCAmelCase     ,       self.scheduler.timesteps[start_step - 1]			)

         lowercase__    :   List[str]						= (
             self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
         )
         lowercase__    :   List[Any]						= int(mask_start_secs * pixels_per_second			)
         lowercase__    :   Dict						= int(mask_end_secs * pixels_per_second			)
         lowercase__    :   Optional[Any]						= self.scheduler.add_noise(__lowerCAmelCase     ,       __lowerCAmelCase     ,       torch.tensor(self.scheduler.timesteps[start_step:]			)			)

        for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]			)			):
         if isinstance(self.unet     ,       __lowerCAmelCase			):
          lowercase__    :   int						= self.unet(__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)['''sample''']
         else:
          lowercase__    :   str						= self.unet(__lowerCAmelCase     ,       __lowerCAmelCase			)['''sample''']

         if isinstance(self.scheduler     ,       __lowerCAmelCase			):
          lowercase__    :   List[str]						= self.scheduler.step(
              model_output=__lowerCAmelCase     ,       timestep=__lowerCAmelCase     ,       sample=__lowerCAmelCase     ,       eta=__lowerCAmelCase     ,       generator=__lowerCAmelCase     ,       )['''prev_sample''']
         else:
          lowercase__    :   Optional[int]						= self.scheduler.step(
              model_output=__lowerCAmelCase     ,       timestep=__lowerCAmelCase     ,       sample=__lowerCAmelCase     ,       generator=__lowerCAmelCase     ,       )['''prev_sample''']

         if mask is not None:
          if mask_start > 0:
           lowercase__    :   Union[str, Any]						= mask[:, step, :, :mask_start]
          if mask_end > 0:
           lowercase__    :   Optional[Any]						= mask[:, step, :, -mask_end:]

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

        lowercase__    :   Dict						= (images / 2 + 0.5).clamp(0     ,       1			)
        lowercase__    :   Any						= images.cpu().permute(0     ,       2     ,       3     ,       1			).numpy()
        lowercase__    :   Tuple						= (images * 255).round().astype('''uint8'''			)
        lowercase__    :   List[Any]						= list(
            (Image.fromarray(_[:, :, 0]			) for _ in images)
            if images.shape[3] == 1
            else (Image.fromarray(__lowerCAmelCase     ,       mode='''RGB'''			).convert('''L'''			) for _ in images)			)

        lowercase__    :   str						= [self.mel.image_to_audio(__lowerCAmelCase			) for _ in images]
        if not return_dict:
         return images, (self.mel.get_sample_rate(), audios)

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

       @torch.no_grad()
       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = 50			)  ->  np.ndarray:
        assert isinstance(self.scheduler     ,       __lowerCAmelCase			)
        self.scheduler.set_timesteps(__lowerCAmelCase			)
        lowercase__    :   Dict						= np.array(
            [np.frombuffer(image.tobytes()     ,       dtype='''uint8'''			).reshape((1, image.height, image.width)			) for image in images]			)
        lowercase__    :   List[str]						= (sample / 255) * 2 - 1
        lowercase__    :   List[Any]						= torch.Tensor(__lowerCAmelCase			).to(self.device			)

        for t in self.progress_bar(torch.flip(self.scheduler.timesteps     ,       (0,)			)			):
         lowercase__    :   Dict						= t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         lowercase__    :   List[str]						= self.scheduler.alphas_cumprod[t]
         lowercase__    :   Dict						= (
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         lowercase__    :   Optional[int]						= 1 - alpha_prod_t
         lowercase__    :   str						= self.unet(__lowerCAmelCase     ,       __lowerCAmelCase			)['''sample''']
         lowercase__    :   str						= (1 - alpha_prod_t_prev) ** 0.5 * model_output
         lowercase__    :   Optional[Any]						= (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         lowercase__    :   int						= sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

        return sample


       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  torch.Tensor:
        lowercase__    :   int						= acos(torch.dot(torch.flatten(__lowerCAmelCase			)     ,       torch.flatten(__lowerCAmelCase			)			) / torch.norm(__lowerCAmelCase			) / torch.norm(__lowerCAmelCase			)			)
        return sin((1 - alpha) * theta			) * xa / sin(__lowerCAmelCase			) + sin(alpha * theta			) * xa / sin(__lowerCAmelCase			)


'''simple docstring'''

import qiskit
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= qiskit.Aer.get_backend('''aer_simulator'''					)

 # Create a Quantum Circuit acting on the q register
 lowercase__    :   Any						= qiskit.QuantumCircuit(UpperCAmelCase		,					UpperCAmelCase					)

 # Map the quantum measurement to the classical bits
 circuit.measure([0]		,					[0]					)

 # Execute the circuit on the simulator
 lowercase__    :   Any						= qiskit.execute(UpperCAmelCase		,					UpperCAmelCase		,					shots=1000					)

 # Return the histogram data of the results of the experiment.
 return job.result().get_counts(UpperCAmelCase					)


if __name__ == "__main__":
  print(F'Total count for various states are: {single_qubit_measure(1, 1)}')

