"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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





class  _A						(       lowerCAmelCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            super().__init__()
            self.register_modules(unet=__lowerCAmelCase		,       scheduler=__lowerCAmelCase						)







      @torch.no_grad()
      def __call__(						self		,       __lowerCAmelCase = 1		,       __lowerCAmelCase = 100		,       __lowerCAmelCase = None		,       __lowerCAmelCase = None		,       __lowerCAmelCase = True		,       ):







            """simple docstring"""



            if audio_length_in_s is None:
                  lowercase														=	self.unet.config.sample_size / self.unet.config.sample_rate

            lowercase														=	audio_length_in_s * self.unet.config.sample_rate

            lowercase														=	2 ** len(self.unet.up_blocks						)
            if sample_size < 3 * down_scale_factor:
                  raise ValueError(
                      f'{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'
                      f' {3 * down_scale_factor / self.unet.config.sample_rate}.'						)

            lowercase														=	int(__lowerCAmelCase						)
            if sample_size % down_scale_factor != 0:
                  lowercase														=	(
                      (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
                  ) * down_scale_factor
                  logger.info(
                      f'{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'
                      f' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'
                      """ process."""						)
            lowercase														=	int(__lowerCAmelCase						)

            lowercase														=	next(iter(self.unet.parameters()						)						).dtype
            lowercase														=	(batch_size, self.unet.config.in_channels, sample_size)
            if isinstance(__lowerCAmelCase		,       __lowerCAmelCase						) and len(__lowerCAmelCase						) != batch_size:
                  raise ValueError(
                      f'You have passed a list of generators of length {len(__lowerCAmelCase						)}, but requested an effective batch'
                      f' size of {batch_size}. Make sure the batch size matches the length of the generators.'						)

            lowercase														=	randn_tensor(__lowerCAmelCase		,       generator=__lowerCAmelCase		,       device=self.device		,       dtype=__lowerCAmelCase						)

            # set step values
            self.scheduler.set_timesteps(__lowerCAmelCase		,       device=audio.device						)
            lowercase														=	self.scheduler.timesteps.to(__lowerCAmelCase						)

            for t in self.progress_bar(self.scheduler.timesteps						):
                  # 1. predict noise model_output
                  lowercase														=	self.unet(__lowerCAmelCase		,       __lowerCAmelCase						).sample

                  # 2. compute previous image: x_t -> t_t-1
                  lowercase														=	self.scheduler.step(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						).prev_sample

            lowercase														=	audio.clamp(-1		,       1						).float().cpu().numpy()

            lowercase														=	audio[:, :, :original_sample_size]

            if not return_dict:
                  return (audio,)

            return AudioPipelineOutput(audios=__lowerCAmelCase						)




"""simple docstring"""






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__lowerCAmelCase    :       Union[str, Any]					     =0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__lowerCAmelCase    :       Optional[int]					     =[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	WATERMARK_BITS
            lowercase														=	WatermarkEncoder()

            self.encoder.set_watermark("""bits"""		,       self.watermark						)







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if images.shape[-1] < 256:
                  return images

            lowercase														=	(255 * (images / 2 + 0.5)).cpu().permute(0		,       2		,       3		,       1						).float().numpy()

            lowercase														=	[self.encoder.encode(__lowerCAmelCase		,       """dwtDct"""						) for image in images]

            lowercase														=	torch.from_numpy(np.array(__lowerCAmelCase						)						).permute(0		,       3		,       1		,       2						)

            lowercase														=	torch.clamp(2 * (images / 255 - 0.5)		,       min=-1.0		,       max=1.0						)
            return images



