

"""simple docstring"""

import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			torch.FloatTensor
       __UpperCamelCase				:			Optional[torch.FloatTensor]          =	None




def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__=0.999				,				lowercase__="cosine"				,				) ->	List[Any]:
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(lowercase__      ):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(lowercase__      ):
   return math.exp(t * -12.0      )

 else:
  raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""      )

 lowerCAmelCase__      :							Union[str, Any]    =							[]
 for i in range(lowercase__      ):
  lowerCAmelCase__      :							Dict    =							i / num_diffusion_timesteps
  lowerCAmelCase__      :							Optional[Any]    =							(i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(lowercase__      ) / alpha_bar_fn(lowercase__      )				,				lowercase__      )      )
 return torch.tensor(lowercase__				,				dtype=torch.floataa      )






class  __a				(	__magic_name__					,  __magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			str          =	[e.name for e in KarrasDiffusionSchedulers]
       __UpperCamelCase				:			str          =	1






       @register_to_config
       def __init__(      self ,				snake_case = 1_000 ,				snake_case = 0.0_001 ,				snake_case = 0.02 ,				snake_case = "linear" ,				snake_case = None ,				snake_case = "epsilon" ,				snake_case = "linspace" ,				snake_case = 0 ,				):





        """simple docstring"""

        if trained_betas is not None:
         lowerCAmelCase__      :							Dict    =							torch.tensor(snake_case ,				dtype=torch.floataa      )
        elif beta_schedule == "linear":
         lowerCAmelCase__      :							Tuple    =							torch.linspace(snake_case ,				snake_case ,				snake_case ,				dtype=torch.floataa      )
        elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         lowerCAmelCase__      :							int    =							(
             torch.linspace(beta_start**0.5 ,				beta_end**0.5 ,				snake_case ,				dtype=torch.floataa      ) ** 2
         )
        elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         lowerCAmelCase__      :							Dict    =							betas_for_alpha_bar(snake_case      )
        else:
         raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""      )

        lowerCAmelCase__      :							Tuple    =							1.0 - self.betas
        lowerCAmelCase__      :							int    =							torch.cumprod(self.alphas ,				dim=0      )

        lowerCAmelCase__      :							List[Any]    =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
        lowerCAmelCase__      :							Any    =							np.concatenate([sigmas[::-1], [0.0]]      ).astype(np.floataa      )
        lowerCAmelCase__      :							int    =							torch.from_numpy(snake_case      )

        # setable values
        lowerCAmelCase__      :							Optional[Any]    =							None
        lowerCAmelCase__      :							List[str]    =							np.linspace(0 ,				num_train_timesteps - 1 ,				snake_case ,				dtype=snake_case      )[::-1].copy()
        lowerCAmelCase__      :							str    =							torch.from_numpy(snake_case      )
        lowerCAmelCase__      :							Optional[Any]    =							False






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        if self.config.timestep_spacing in ["linspace", "trailing"]:
         return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case      ):





        """simple docstring"""

        if isinstance(snake_case ,				torch.Tensor      ):
         lowerCAmelCase__      :							List[str]    =							timestep.to(self.timesteps.device      )
        lowerCAmelCase__      :							Optional[Any]    =							(self.timesteps == timestep).nonzero().item()
        lowerCAmelCase__      :							Optional[Any]    =							self.sigmas[step_index]
        lowerCAmelCase__      :							Optional[Any]    =							sample / ((sigma**2 + 1) ** 0.5)
        lowerCAmelCase__      :							Optional[int]    =							True
        return sample






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case = None      ):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							num_inference_steps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
         lowerCAmelCase__      :							List[Any]    =							np.linspace(0 ,				self.config.num_train_timesteps - 1 ,				snake_case ,				dtype=snake_case      )[
             ::-1
         ].copy()
        elif self.config.timestep_spacing == "leading":
         lowerCAmelCase__      :							Union[str, Any]    =							self.config.num_train_timesteps // self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         lowerCAmelCase__      :							Any    =							(np.arange(0 ,				snake_case      ) * step_ratio).round()[::-1].copy().astype(snake_case      )
         timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
         lowerCAmelCase__      :							str    =							self.config.num_train_timesteps / self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         lowerCAmelCase__      :							Union[str, Any]    =							(np.arange(self.config.num_train_timesteps ,				0 ,				-step_ratio      )).round().copy().astype(snake_case      )
         timesteps -= 1
        else:
         raise ValueError(
             F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""      )

        lowerCAmelCase__      :							Union[str, Any]    =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
        lowerCAmelCase__      :							Union[str, Any]    =							np.interp(snake_case ,				np.arange(0 ,				len(snake_case      )      ) ,				snake_case      )
        lowerCAmelCase__      :							Union[str, Any]    =							np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
        lowerCAmelCase__      :							Any    =							torch.from_numpy(snake_case      ).to(device=snake_case      )
        if str(snake_case      ).startswith("mps"      ):
         # mps does not support float64
         lowerCAmelCase__      :							int    =							torch.from_numpy(snake_case      ).to(snake_case ,				dtype=torch.floataa      )
        else:
         lowerCAmelCase__      :							List[Any]    =							torch.from_numpy(snake_case      ).to(device=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case = None ,				snake_case = True ,				):





        """simple docstring"""

        if (
            isinstance(snake_case ,				snake_case      )
            or isinstance(snake_case ,				torch.IntTensor      )
            or isinstance(snake_case ,				torch.LongTensor      )
        ):
         raise ValueError(
             (
                 "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                 " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                 " one of the `scheduler.timesteps` as a timestep."
             ) ,				)

        if not self.is_scale_input_called:
         logger.warning(
             "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
             "See `StableDiffusionPipeline` for a usage example."      )

        if isinstance(snake_case ,				torch.Tensor      ):
         lowerCAmelCase__      :							Dict    =							timestep.to(self.timesteps.device      )

        lowerCAmelCase__      :							List[Any]    =							(self.timesteps == timestep).nonzero().item()
        lowerCAmelCase__      :							Tuple    =							self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
         lowerCAmelCase__      :							Optional[Any]    =							sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         lowerCAmelCase__      :							Union[str, Any]    =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        elif self.config.prediction_type == "sample":
         raise NotImplementedError("prediction_type not implemented yet: sample"      )
        else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""      )

        lowerCAmelCase__      :							Tuple    =							self.sigmas[step_index]
        lowerCAmelCase__      :							Union[str, Any]    =							self.sigmas[step_index + 1]
        lowerCAmelCase__      :							List[str]    =							(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
        lowerCAmelCase__      :							Tuple    =							(sigma_to**2 - sigma_up**2) ** 0.5

        # 2. Convert to an ODE derivative
        lowerCAmelCase__      :							str    =							(sample - pred_original_sample) / sigma

        lowerCAmelCase__      :							Tuple    =							sigma_down - sigma

        lowerCAmelCase__      :							str    =							sample + derivative * dt

        lowerCAmelCase__      :							Dict    =							model_output.device
        lowerCAmelCase__      :							List[Any]    =							randn_tensor(model_output.shape ,				dtype=model_output.dtype ,				device=snake_case ,				generator=snake_case      )

        lowerCAmelCase__      :							List[Any]    =							prev_sample + noise * sigma_up

        if not return_dict:
         return (prev_sample,)

        return EulerAncestralDiscreteSchedulerOutput(
            prev_sample=snake_case ,				pred_original_sample=snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							self.sigmas.to(device=original_samples.device ,				dtype=original_samples.dtype      )
        if original_samples.device.type == "mps" and torch.is_floating_point(snake_case      ):
         # mps does not support float64
         lowerCAmelCase__      :							Optional[int]    =							self.timesteps.to(original_samples.device ,				dtype=torch.floataa      )
         lowerCAmelCase__      :							Any    =							timesteps.to(original_samples.device ,				dtype=torch.floataa      )
        else:
         lowerCAmelCase__      :							Optional[Any]    =							self.timesteps.to(original_samples.device      )
         lowerCAmelCase__      :							Tuple    =							timesteps.to(original_samples.device      )

        lowerCAmelCase__      :							List[str]    =							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

        lowerCAmelCase__      :							Any    =							sigmas[step_indices].flatten()
        while len(sigma.shape      ) < len(original_samples.shape      ):
         lowerCAmelCase__      :							Optional[Any]    =							sigma.unsqueeze(-1      )

        lowerCAmelCase__      :							Optional[Any]    =							original_samples + noise * sigma
        return noisy_samples






       def __len__(      self      ):





        """simple docstring"""

        return self.config.num_train_timesteps









"""simple docstring"""

from typing import List, Optional, Union

import numpy as np

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


_UpperCamelCase		    =						logging.get_logger(__name__)






class  __a				(	__magic_name__					):





       """simple docstring"""


       __UpperCamelCase				:			Tuple          =	['input_values', 'attention_mask']






       def __init__(      self ,				snake_case=1 ,				snake_case=16_000 ,				snake_case=0.0 ,				snake_case=False ,				snake_case=True ,				**snake_case ,				):





        """simple docstring"""

        super().__init__(feature_size=snake_case ,				sampling_rate=snake_case ,				padding_value=snake_case ,				**snake_case      )
        lowerCAmelCase__      :							Dict    =							return_attention_mask
        lowerCAmelCase__      :							List[str]    =							do_normalize






       @staticmethod
       def  SCREAMING_SNAKE_CASE_  (      snake_case ,				snake_case ,				snake_case = 0.0      ):





        """simple docstring"""

        if attention_mask is not None:
         lowerCAmelCase__      :							List[str]    =							np.array(snake_case ,				np.intaa      )
         lowerCAmelCase__      :							str    =							[]

         for vector, length in zip(snake_case ,				attention_mask.sum(-1      )      ):
          lowerCAmelCase__      :							Any    =							(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7      )
          if length < normed_slice.shape[0]:
           lowerCAmelCase__      :							List[Any]    =							padding_value

          normed_input_values.append(snake_case      )
        else:
         lowerCAmelCase__      :							List[Any]    =							[(x - x.mean()) / np.sqrt(x.var() + 1e-7      ) for x in input_values]

        return normed_input_values






       def __call__(      self ,				snake_case ,				snake_case = False ,				snake_case = None ,				snake_case = False ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				snake_case = None ,				**snake_case ,				):





        """simple docstring"""

        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."      )

        lowerCAmelCase__      :							List[str]    =							isinstance(snake_case ,				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}"""      )
        lowerCAmelCase__      :							Tuple    =							is_batched_numpy or (
            isinstance(snake_case ,				(list, tuple)      ) and (isinstance(raw_speech[0] ,				(np.ndarray, tuple, list)      ))
        )

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

        # convert into correct format for padding
        lowerCAmelCase__      :							Tuple    =							BatchFeature({"input_values": raw_speech}      )

        lowerCAmelCase__      :							Any    =							self.pad(
            snake_case ,				padding=snake_case ,				max_length=snake_case ,				truncation=snake_case ,				pad_to_multiple_of=snake_case ,				return_attention_mask=snake_case ,				)

        # convert input values to correct format
        lowerCAmelCase__      :							List[Any]    =							padded_inputs["input_values"]
        if not isinstance(input_values[0] ,				np.ndarray      ):
         lowerCAmelCase__      :							Any    =							[np.asarray(snake_case ,				dtype=np.floataa      ) for array in input_values]
        elif (
            not isinstance(snake_case ,				np.ndarray      )
            and isinstance(input_values[0] ,				np.ndarray      )
            and input_values[0].dtype is np.dtype(np.floataa      )
        ):
         lowerCAmelCase__      :							str    =							[array.astype(np.floataa      ) for array in input_values]
        elif isinstance(snake_case ,				np.ndarray      ) and input_values.dtype is np.dtype(np.floataa      ):
         lowerCAmelCase__      :							List[Any]    =							input_values.astype(np.floataa      )

        # convert attention_mask to correct format
        lowerCAmelCase__      :							int    =							padded_inputs.get("attention_mask"      )
        if attention_mask is not None:
         lowerCAmelCase__      :							Tuple    =							[np.asarray(snake_case ,				dtype=np.intaa      ) for array in attention_mask]

        # zero-mean and unit-variance normalization
        if self.do_normalize:
         lowerCAmelCase__      :							List[Any]    =							(
             attention_mask
             if self._get_padding_strategies(snake_case ,				max_length=snake_case      ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         lowerCAmelCase__      :							Union[str, Any]    =							self.zero_mean_unit_var_norm(
             padded_inputs["input_values"] ,				attention_mask=snake_case ,				padding_value=self.padding_value      )

        if return_tensors is not None:
         lowerCAmelCase__      :							Optional[Any]    =							padded_inputs.convert_to_tensors(snake_case      )

        return padded_inputs






