
"""simple docstring"""




from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_A     :			Dict  						=			8


def     __magic_name__      ( __snake_case							:			List[str]	,					__snake_case							:			Union[str, Any]=BITS    )					->							List[Any]:
       lowercase       :							Optional[Any]									=				x.device

       lowercase       :							Optional[int]									=				(x * 255).int().clamp(0	,					255    )

       lowercase       :							int									=				2 ** torch.arange(bits - 1	,					-1	,					-1	,					device=UpperCamelCase__    )
       lowercase       :							Union[str, Any]									=				rearrange(UpperCamelCase__	,					"d -> d 1 1"    )
       lowercase       :							str									=				rearrange(UpperCamelCase__	,					"b c h w -> b c 1 h w"    )

       lowercase       :							Dict									=				((x & mask) != 0).float()
       lowercase       :							Optional[Any]									=				rearrange(UpperCamelCase__	,					"b c d h w -> b (c d) h w"    )
       lowercase       :							List[str]									=				bits * 2 - 1
       return bits


def     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			Tuple=BITS    )					->							Union[str, Any]:
       lowercase       :							Dict									=				x.device

       lowercase       :							Tuple									=				(x > 0).int()
       lowercase       :							Tuple									=				2 ** torch.arange(bits - 1	,					-1	,					-1	,					device=UpperCamelCase__	,					dtype=torch.intaa    )

       lowercase       :							Optional[Any]									=				rearrange(UpperCamelCase__	,					"d -> d 1 1"    )
       lowercase       :							str									=				rearrange(UpperCamelCase__	,					"b (c d) h w -> b c d h w"	,					d=8    )
       lowercase       :							str									=				reduce(x * mask	,					"b c d h w -> b c h w"	,					"sum"    )
       return (dec / 255).clamp(0.0	,					1.0    )


def     __magic_name__      ( self							:			List[Any]	,					__snake_case							:			torch.FloatTensor	,					__snake_case							:			int	,					__snake_case							:			torch.FloatTensor	,					__snake_case							:			float = 0.0	,					__snake_case							:			bool = True	,					__snake_case							:			str=None	,					__snake_case							:			bool = True	,					)					->							Dict:
       if self.num_inference_steps is None:
              raise ValueError(
                  "Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler"    )

       # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
       # Ideally, read DDIM paper in-detail understanding

       # Notation (<variable name> -> <name in paper>
       # - pred_noise_t -> e_theta(x_t, t)
       # - pred_original_sample -> f_theta(x_t, t) or x_0
       # - std_dev_t -> sigma_t
       # - eta -> η
       # - pred_sample_direction -> "direction pointing to x_t"
       # - pred_prev_sample -> "x_t-1"

       # 1. get previous step value (=t-1)
       lowercase       :							Any									=				timestep - self.config.num_train_timesteps // self.num_inference_steps

       # 2. compute alphas, betas
       lowercase       :							int									=				self.alphas_cumprod[timestep]
       lowercase       :							Optional[Any]									=				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

       lowercase       :							List[str]									=				1 - alpha_prod_t

       # 3. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       lowercase       :							List[str]									=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

       # 4. Clip "predicted x_0"
       lowercase       :							Optional[Any]									=				self.bit_scale
       if self.config.clip_sample:
              lowercase       :							Optional[Any]									=				torch.clamp(UpperCamelCase__	,					-scale	,					UpperCamelCase__    )

       # 5. compute variance: "sigma_t(η)" -> see formula (16)
       # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
       lowercase       :							int									=				self._get_variance(UpperCamelCase__	,					UpperCamelCase__    )
       lowercase       :							Optional[int]									=				eta * variance ** 0.5

       if use_clipped_model_output:
              # the model_output is always re-derived from the clipped x_0 in Glide
              lowercase       :							Dict									=				(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

       # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       lowercase       :							Any									=				(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

       # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       lowercase       :							Optional[Any]									=				alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if eta > 0:
              # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
              lowercase       :							Optional[int]									=				model_output.device if torch.is_tensor(UpperCamelCase__    ) else "cpu"
              lowercase       :							List[Any]									=				torch.randn(model_output.shape	,					dtype=model_output.dtype	,					generator=UpperCamelCase__    ).to(UpperCamelCase__    )
              lowercase       :							Tuple									=				self._get_variance(UpperCamelCase__	,					UpperCamelCase__    ) ** 0.5 * eta * noise

              lowercase       :							List[Any]									=				prev_sample + variance

       if not return_dict:
              return (prev_sample,)

       return DDIMSchedulerOutput(prev_sample=UpperCamelCase__	,					pred_original_sample=UpperCamelCase__    )


def     __magic_name__      ( self							:			Optional[Any]	,					__snake_case							:			torch.FloatTensor	,					__snake_case							:			int	,					__snake_case							:			torch.FloatTensor	,					__snake_case							:			Tuple="epsilon"	,					__snake_case							:			int=None	,					__snake_case							:			bool = True	,					)					->							Optional[Any]:
       lowercase       :							Optional[int]									=				timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
              lowercase						,       lowercase       :							List[str]									=				torch.split(UpperCamelCase__	,					sample.shape[1]	,					dim=1    )
       else:
              lowercase       :							Optional[Any]									=				None

       # 1. compute alphas, betas
       lowercase       :							List[str]									=				self.alphas_cumprod[t]
       lowercase       :							Optional[Any]									=				self.alphas_cumprod[t - 1] if t > 0 else self.one
       lowercase       :							int									=				1 - alpha_prod_t
       lowercase       :							Union[str, Any]									=				1 - alpha_prod_t_prev

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       if prediction_type == "epsilon":
              lowercase       :							Any									=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif prediction_type == "sample":
              lowercase       :							int									=				model_output
       else:
              raise ValueError(f"""Unsupported prediction_type {prediction_type}."""    )

       # 3. Clip "predicted x_0"
       lowercase       :							int									=				self.bit_scale
       if self.config.clip_sample:
              lowercase       :							List[Any]									=				torch.clamp(UpperCamelCase__	,					-scale	,					UpperCamelCase__    )

       # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       lowercase       :							Union[str, Any]									=				(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
       lowercase       :							List[str]									=				self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

       # 5. Compute predicted previous sample µ_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       lowercase       :							Any									=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       lowercase       :							List[Any]									=				0
       if t > 0:
              lowercase       :							Tuple									=				torch.randn(
                  model_output.size()	,					dtype=model_output.dtype	,					layout=model_output.layout	,					generator=UpperCamelCase__    ).to(model_output.device    )
              lowercase       :							List[str]									=				(self._get_variance(UpperCamelCase__	,					predicted_variance=UpperCamelCase__    ) ** 0.5) * noise

       lowercase       :							str									=				pred_prev_sample + variance

       if not return_dict:
              return (pred_prev_sample,)

       return DDPMSchedulerOutput(prev_sample=UpperCamelCase__	,					pred_original_sample=UpperCamelCase__    )

class 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a      ,       _a = 1.0      ,       ):
          super().__init__()
          lowercase       :							Dict									=				bit_scale
          lowercase       :							Any									=				(
              ddim_bit_scheduler_step if isinstance(__a      ,       __a							) else ddpm_bit_scheduler_step
          )

          self.register_modules(unet=__a      ,       scheduler=__a							)

   @torch.no_grad()
   def __call__(     self      ,       _a = 256      ,       _a = 256      ,       _a = 50      ,       _a = None      ,       _a = 1      ,       _a = "pil"      ,       _a = True      ,       **_a      ,       ):
          lowercase       :							Optional[int]									=				torch.randn(
              (batch_size, self.unet.config.in_channels, height, width)      ,       generator=__a      ,       )
          lowercase       :							Optional[int]									=				decimal_to_bits(__a							) * self.bit_scale
          lowercase       :							List[Any]									=				latents.to(self.device							)

          self.scheduler.set_timesteps(__a							)

          for t in self.progress_bar(self.scheduler.timesteps							):
                 # predict the noise residual
                 lowercase       :							Tuple									=				self.unet(__a      ,       __a							).sample

                 # compute the previous noisy sample x_t -> x_t-1
                 lowercase       :							str									=				self.scheduler.step(__a      ,       __a      ,       __a							).prev_sample

          lowercase       :							Optional[Any]									=				bits_to_decimal(__a							)

          if output_type == "pil":
                 lowercase       :							Any									=				self.numpy_to_pil(__a							)

          if not return_dict:
                 return (image,)

          return ImagePipelineOutput(images=__a							)



'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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


__lowerCAmelCase						:							List[Any]    							=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class     A ( UpperCAmelCase ):





							def __init__(   self					:				Dict  ,		__a					:				List[Any]  ,		__a					:				Optional[Any]						)				->						Dict:
								super().__init__()
								self.register_modules(unet=__a  ,		scheduler=__a						)






							@torch.no_grad()
							def __call__(   self					:				Tuple  ,		__a					:				int = 1  ,		__a					:				int = 1_0_0  ,		__a					:				Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,		__a					:				Optional[float] = None  ,		__a					:				bool = True  ,		)				->						Union[AudioPipelineOutput, Tuple]:

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

								__UpperCAmelCase      					=      audio_length_in_s * self.unet.config.sample_rate

								__UpperCAmelCase      					=      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}."""						)

								__UpperCAmelCase      					=      int(__a						)
								if sample_size % down_scale_factor != 0:
									__UpperCAmelCase      					=      (
									    (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.'''						)
								__UpperCAmelCase      					=      int(__a						)

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

								__UpperCAmelCase      					=      randn_tensor(__a  ,		generator=__a  ,		device=self.device  ,		dtype=__a						)

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

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

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

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

								__UpperCAmelCase      					=      audio[:, :, :original_sample_size]

								if not return_dict:
									return (audio,)

								return AudioPipelineOutput(audios=__a						)
