






from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__UpperCamelCase					:		int	  =						logging.get_logger(__name__)


class   __SCREAMING_SNAKE_CASE(	_UpperCAmelCase	):
       _UpperCAmelCase     =       ["input_features", "attention_mask"]
       def __init__(     self:      Optional[Any]       ,	UpperCamelCase:      int=80       ,	UpperCamelCase:      Tuple=1_60_00       ,	UpperCamelCase:      int=80       ,	UpperCamelCase:      int=0.0       ,	UpperCamelCase:      Any=True       ,	UpperCamelCase:      int=True       ,	UpperCamelCase:      List[Any]=True       ,	**UpperCamelCase:      Optional[Any]       ,	)		->		Tuple:
           super().__init__(feature_size=lowerCamelCase_       ,	sampling_rate=lowerCamelCase_       ,	padding_value=lowerCamelCase_       ,	**lowerCamelCase_						)
           snake_case__									=     num_mel_bins
           snake_case__									=     do_ceptral_normalize
           snake_case__									=     normalize_means
           snake_case__									=     normalize_vars
           snake_case__									=     True
       def lowerCAmelCase_ (     self:      Union[str, Any]       ,	UpperCamelCase:      np.ndarray       ,	)		->		np.ndarray:
           snake_case__									=     waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
           snake_case__									=     torch.from_numpy(lowerCamelCase_						).unsqueeze(0						)
           snake_case__									=     ta_kaldi.fbank(lowerCamelCase_       ,	num_mel_bins=self.num_mel_bins       ,	sample_frequency=self.sampling_rate						)
           return features.numpy()
       @staticmethod
       def lowerCAmelCase_ (     UpperCamelCase:      np.ndarray       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Optional[bool] = True       ,	UpperCamelCase:      Optional[bool] = True       ,	UpperCamelCase:      float = 0.0       ,	)		->		np.ndarray:




           if normalize_means:
               snake_case__									=     x[:input_length].mean(axis=0						)
               snake_case__									=     np.subtract(lowerCamelCase_       ,	lowerCamelCase_						)
           if normalize_vars:
               snake_case__									=     x[:input_length].std(axis=0						)
               snake_case__									=     np.divide(lowerCamelCase_       ,	lowerCamelCase_						)

           if input_length < x.shape[0]:
               snake_case__									=     padding_value

           # make sure array is in float32
           snake_case__									=     x.astype(np.floataa						)

           return x
       def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      List[np.ndarray]       ,	UpperCamelCase:      Optional[np.ndarray] = None						)		->		List[np.ndarray]:
           snake_case__									=     attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
           return [
               self.utterance_cmvn(lowerCamelCase_       ,	lowerCamelCase_       ,	self.normalize_means       ,	self.normalize_vars       ,	self.padding_value						)
               for x, n in zip(lowerCamelCase_       ,	lowerCamelCase_						)
           ]


       def __call__(     self:      int       ,	UpperCamelCase:      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,	UpperCamelCase:      Union[bool, str, PaddingStrategy] = False       ,	UpperCamelCase:      Optional[int] = None       ,	UpperCamelCase:      bool = False       ,	UpperCamelCase:      Optional[int] = None       ,	UpperCamelCase:      Optional[Union[str, TensorType]] = None       ,	UpperCamelCase:      Optional[int] = None       ,	UpperCamelCase:      Optional[bool] = None       ,	**UpperCamelCase:      str       ,	)		->		BatchFeature:




           if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                       F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
                       F''' {self.sampling_rate} and not {sampling_rate}.'''						)
           else:
               logger.warning(
                   'It is strongly recommended to pass the `sampling_rate` argument to this function. '
                   'Failing to do so can result in silent errors that might be hard to debug.'						)

           snake_case__									=     isinstance(lowerCamelCase_       ,	np.ndarray						) and len(raw_speech.shape						) > 1
           if is_batched_numpy and len(raw_speech.shape						) > 2:
               raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''						)
           snake_case__									=     is_batched_numpy or (
               isinstance(lowerCamelCase_       ,	(list, tuple)						) and (isinstance(raw_speech[0]       ,	(np.ndarray, tuple, list)						))
           )

           if is_batched:
               snake_case__									=     [np.asarray(lowerCamelCase_       ,	dtype=np.floataa						) for speech in raw_speech]
           elif not is_batched and not isinstance(lowerCamelCase_       ,	np.ndarray						):
               snake_case__									=     np.asarray(lowerCamelCase_       ,	dtype=np.floataa						)
           elif isinstance(lowerCamelCase_       ,	np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
               snake_case__									=     raw_speech.astype(np.floataa						)

           # always return batch
           if not is_batched:
               snake_case__									=     [raw_speech]

           # extract fbank features
           snake_case__									=     [self._extract_fbank_features(lowerCamelCase_						) for waveform in raw_speech]

           # convert into correct format for padding
           snake_case__									=     BatchFeature({'input_features': features}						)

           snake_case__									=     self.pad(
               lowerCamelCase_       ,	padding=lowerCamelCase_       ,	max_length=lowerCamelCase_       ,	truncation=lowerCamelCase_       ,	pad_to_multiple_of=lowerCamelCase_       ,	return_attention_mask=lowerCamelCase_       ,	**lowerCamelCase_       ,	)

           # make sure list is in array format
           snake_case__									=     padded_inputs.get('input_features'						)
           if isinstance(input_features[0]       ,	lowerCamelCase_						):
               snake_case__									=     [np.asarray(lowerCamelCase_       ,	dtype=np.floataa						) for feature in input_features]

           snake_case__									=     padded_inputs.get('attention_mask'						)
           if attention_mask is not None:
               snake_case__									=     [np.asarray(lowerCamelCase_       ,	dtype=np.intaa						) for array in attention_mask]

           # Utterance-level cepstral mean and variance normalization
           if self.do_ceptral_normalize:
               snake_case__									=     (
                   np.array(lowerCamelCase_       ,	dtype=np.intaa						)
                   if self._get_padding_strategies(lowerCamelCase_       ,	max_length=lowerCamelCase_						) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               snake_case__									=     self.normalize(
                   padded_inputs['input_features']       ,	attention_mask=lowerCamelCase_						)

           if return_tensors is not None:
               snake_case__									=     padded_inputs.convert_to_tensors(lowerCamelCase_						)

           return padded_inputs












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


__UpperCamelCase					:		Optional[int]	  =						8
def      a_ (      _A      ,   _A=BITS  )			->			List[Any]:







				"""simple docstring"""
				snake_case__									=     x.device

				snake_case__									=     (x * 255).int().clamp(0      ,   255  )

				snake_case__									=     2 ** torch.arange(bits - 1      ,   -1      ,   -1      ,   device=_A  )
				snake_case__									=     rearrange(_A      ,   'd -> d 1 1'  )
				snake_case__									=     rearrange(_A      ,   'b c h w -> b c 1 h w'  )

				snake_case__									=     ((x & mask) != 0).float()
				snake_case__									=     rearrange(_A      ,   'b c d h w -> b (c d) h w'  )
				snake_case__									=     bits * 2 - 1
				return bits
def      a_ (      _A      ,   _A=BITS  )			->			List[str]:







				"""simple docstring"""
				snake_case__									=     x.device

				snake_case__									=     (x > 0).int()
				snake_case__									=     2 ** torch.arange(bits - 1      ,   -1      ,   -1      ,   device=_A      ,   dtype=torch.intaa  )

				snake_case__									=     rearrange(_A      ,   'd -> d 1 1'  )
				snake_case__									=     rearrange(_A      ,   'b (c d) h w -> b c d h w'      ,   d=8  )
				snake_case__									=     reduce(x * mask      ,   'b c d h w -> b c h w'      ,   'sum'  )
				return (dec / 255).clamp(0.0      ,   1.0  )
def      a_ (      self      ,   _A      ,   _A      ,   _A      ,   _A = 0.0      ,   _A = True      ,   _A=None      ,   _A = True      ,   )			->			Union[DDIMSchedulerOutput, Tuple]:







				"""simple docstring"""
				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)
				snake_case__									=     timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 2. compute alphas, betas
				snake_case__									=     self.alphas_cumprod[timestep]
				snake_case__									=     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

				snake_case__									=     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
				snake_case__									=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				snake_case__									=     self.bit_scale
				if self.config.clip_sample:
								snake_case__									=     torch.clamp(_A      ,   -scale      ,   _A  )

				# 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)
				snake_case__									=     self._get_variance(_A      ,   _A  )
				snake_case__									=     eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								snake_case__									=     (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
				snake_case__									=     (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
				snake_case__									=     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
								snake_case__									=     model_output.device if torch.is_tensor(_A  ) else 'cpu'
								snake_case__									=     torch.randn(model_output.shape      ,   dtype=model_output.dtype      ,   generator=_A  ).to(_A  )
								snake_case__									=     self._get_variance(_A      ,   _A  ) ** 0.5 * eta * noise

								snake_case__									=     prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=_A      ,   pred_original_sample=_A  )
def      a_ (      self      ,   _A      ,   _A      ,   _A      ,   _A="epsilon"      ,   _A=None      ,   _A = True      ,   )			->			Union[DDPMSchedulerOutput, Tuple]:







				"""simple docstring"""
				snake_case__									=     timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
								snake_case__							,						snake_case__									=     torch.split(_A      ,   sample.shape[1]      ,   dim=1  )
				else:
								snake_case__									=     None

				# 1. compute alphas, betas
				snake_case__									=     self.alphas_cumprod[t]
				snake_case__									=     self.alphas_cumprod[t - 1] if t > 0 else self.one
				snake_case__									=     1 - alpha_prod_t
				snake_case__									=     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":
								snake_case__									=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								snake_case__									=     model_output
				else:
								raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''  )

				# 3. Clip "predicted x_0"
				snake_case__									=     self.bit_scale
				if self.config.clip_sample:
								snake_case__									=     torch.clamp(_A      ,   -scale      ,   _A  )

				# 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
				snake_case__									=     (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				snake_case__									=     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
				snake_case__									=     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				snake_case__									=     0
				if t > 0:
								snake_case__									=     torch.randn(
								    model_output.size()      ,   dtype=model_output.dtype      ,   layout=model_output.layout      ,   generator=_A  ).to(model_output.device  )
								snake_case__									=     (self._get_variance(_A      ,   predicted_variance=_A  ) ** 0.5) * noise

				snake_case__									=     pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=_A      ,   pred_original_sample=_A  )


class   __SCREAMING_SNAKE_CASE(	a_	):
							def __init__(     self:      Dict       ,	UpperCamelCase:      UNetaDConditionModel       ,	UpperCamelCase:      Union[DDIMScheduler, DDPMScheduler]       ,	UpperCamelCase:      Optional[float] = 1.0       ,	)		->		Union[str, Any]:
											super().__init__()
											snake_case__									=     bit_scale
											snake_case__									=     (
											    ddim_bit_scheduler_step if isinstance(UpperCamelCase       ,	UpperCamelCase						) else ddpm_bit_scheduler_step
											)

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


							@torch.no_grad()
							def __call__(     self:      Dict       ,	UpperCamelCase:      Optional[int] = 2_56       ,	UpperCamelCase:      Optional[int] = 2_56       ,	UpperCamelCase:      Optional[int] = 50       ,	UpperCamelCase:      Optional[torch.Generator] = None       ,	UpperCamelCase:      Optional[int] = 1       ,	UpperCamelCase:      Optional[str] = "pil"       ,	UpperCamelCase:      bool = True       ,	**UpperCamelCase:      int       ,	)		->		Union[Tuple, ImagePipelineOutput]:
											snake_case__									=     torch.randn(
											    (batch_size, self.unet.config.in_channels, height, width)       ,	generator=UpperCamelCase       ,	)
											snake_case__									=     decimal_to_bits(UpperCamelCase						) * self.bit_scale
											snake_case__									=     latents.to(self.device						)

											self.scheduler.set_timesteps(UpperCamelCase						)

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

															# compute the previous noisy sample x_t -> x_t-1
															snake_case__									=     self.scheduler.step(UpperCamelCase       ,	UpperCamelCase       ,	UpperCamelCase						).prev_sample

											snake_case__									=     bits_to_decimal(UpperCamelCase						)

											if output_type == "pil":
															snake_case__									=     self.numpy_to_pil(UpperCamelCase						)

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=UpperCamelCase						)




