




# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class       a					(       _A      ):

     '''simple docstring'''





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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						Optional[int]=0.999	,					__UpperCamelCase      :						Union[str, Any]="cosine"	,					)  ->							Tuple:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__UpperCamelCase      :						List[str]				):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

    elif alpha_transform_type == "exp":

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

    else:
        raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'				)

    UpperCAmelCase_										=					[]
    for i in range(__UpperCamelCase				):
        UpperCAmelCase_										=					i / num_diffusion_timesteps
        UpperCAmelCase_										=					(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(__UpperCamelCase				) / alpha_bar_fn(__UpperCamelCase				)	,					__UpperCamelCase				)				)
    return torch.tensor(__UpperCamelCase	,					dtype=torch.floataa				)



class       a					(       _A     ,       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Any       		=  1
     @register_to_config
     def __init__(     self       :      List[Any] ,   __snake_case       :      int = 10_00 ,   __snake_case       :      float = 0.0_001 ,   __snake_case       :      float = 0.02 ,   __snake_case       :      str = "linear" ,   __snake_case       :      Optional[Union[np.ndarray, List[float]]] = None ,   __snake_case       :      bool = True ,   __snake_case       :      bool = True ,   __snake_case       :      int = 0 ,   __snake_case       :      str = "epsilon" ,   __snake_case       :      float = 1.0 ,   **__snake_case       :      List[Any] ,   ):
         if kwargs.get('''set_alpha_to_one''' ,   __snake_case    ) is not None:
             UpperCAmelCase_										=					(
                 '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
             )
             deprecate('''set_alpha_to_one''' ,   '''1.0.0''' ,   __snake_case ,   standard_warn=__snake_case    )
             UpperCAmelCase_										=					kwargs['''set_alpha_to_one''']
         if trained_betas is not None:
             UpperCAmelCase_										=					torch.tensor(__snake_case ,   dtype=torch.floataa    )
         elif beta_schedule == "linear":
             UpperCAmelCase_										=					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.
             UpperCAmelCase_										=					(
                 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
             UpperCAmelCase_										=					betas_for_alpha_bar(__snake_case    )
         else:
             raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'    )

         UpperCAmelCase_										=					1.0 - self.betas
         UpperCAmelCase_										=					torch.cumprod(self.alphas ,   dim=0    )

         # At every step in inverted ddim, we are looking into the next alphas_cumprod
         # For the final step, there is no next alphas_cumprod, and the index is out of bounds
         # `set_alpha_to_zero` decides whether we set this parameter simply to zero
         # in this case, self.step() just output the predicted noise
         # or whether we use the final alpha of the "non-previous" one.
         UpperCAmelCase_										=					torch.tensor(0.0    ) if set_alpha_to_zero else self.alphas_cumprod[-1]

         # standard deviation of the initial noise distribution
         UpperCAmelCase_										=					1.0

         # setable values
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					torch.from_numpy(np.arange(0 ,   __snake_case    ).copy().astype(np.intaa    )    )
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      torch.FloatTensor ,   __snake_case       :      Optional[int] = None    ):
         return sample
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      int ,   __snake_case       :      Union[str, torch.device] = None    ):

         if num_inference_steps > self.config.num_train_timesteps:
             raise ValueError(
                 F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                 F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                 F' maximal {self.config.num_train_timesteps} timesteps.'    )

         UpperCAmelCase_										=					num_inference_steps
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					(np.arange(0 ,   __snake_case    ) * step_ratio).round().copy().astype(np.intaa    )
         UpperCAmelCase_										=					torch.from_numpy(__snake_case    ).to(__snake_case    )
         self.timesteps += self.config.steps_offset
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      torch.FloatTensor ,   __snake_case       :      int ,   __snake_case       :      torch.FloatTensor ,   __snake_case       :      float = 0.0 ,   __snake_case       :      bool = False ,   __snake_case       :      Optional[torch.FloatTensor] = None ,   __snake_case       :      bool = True ,   ):
         # 1. get previous step value (=t+1)
         UpperCAmelCase_										=					timestep + self.config.num_train_timesteps // self.num_inference_steps

         # 2. compute alphas, betas
         # change original implementation to exactly match noise levels for analogous forward process
         UpperCAmelCase_										=					self.alphas_cumprod[timestep]
         UpperCAmelCase_										=					(
             self.alphas_cumprod[prev_timestep]
             if prev_timestep < self.config.num_train_timesteps
             else self.final_alpha_cumprod
         )

         UpperCAmelCase_										=					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
         if self.config.prediction_type == "epsilon":
             UpperCAmelCase_										=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
             UpperCAmelCase_										=					model_output
         elif self.config.prediction_type == "sample":
             UpperCAmelCase_										=					model_output
             UpperCAmelCase_										=					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
         elif self.config.prediction_type == "v_prediction":
             UpperCAmelCase_										=					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
             UpperCAmelCase_										=					(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
         else:
             raise ValueError(
                 F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                 ''' `v_prediction`'''    )

         # 4. Clip or threshold "predicted x_0"
         if self.config.clip_sample:
             UpperCAmelCase_										=					pred_original_sample.clamp(
                 -self.config.clip_sample_range ,   self.config.clip_sample_range    )

         # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         UpperCAmelCase_										=					(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

         # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         UpperCAmelCase_										=					alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

         if not return_dict:
             return (prev_sample, pred_original_sample)
         return DDIMSchedulerOutput(prev_sample=__snake_case ,   pred_original_sample=__snake_case    )




     def __len__(     self       :      Optional[Any]    ):
         return self.config.num_train_timesteps






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate



@dataclass(frozen=_A      )
class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  field(default='audio-classification'     ,       metadata={'include_in_asdict_even_if_is_default': True}      )
     lowerCAmelCase    :	ClassVar[Features]       		=  Features({'audio': Audio()}      )
     lowerCAmelCase    :	ClassVar[Features]       		=  Features({'labels': ClassLabel}      )
     lowerCAmelCase    :	str       		=  "audio"
     lowerCAmelCase    :	str       		=  "labels"
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      List[Any]    ):
         if self.label_column not in features:
             raise ValueError(F'Column {self.label_column} is not present in features.'    )
         if not isinstance(features[self.label_column] ,   __snake_case    ):
             raise ValueError(F'Column {self.label_column} is not a ClassLabel.'    )
         UpperCAmelCase_										=					copy.deepcopy(self    )
         UpperCAmelCase_										=					self.label_schema.copy()
         UpperCAmelCase_										=					features[self.label_column]
         UpperCAmelCase_										=					label_schema
         return task_template




     @property
     def   lowerCamelCase_       (     self       :      Tuple    ):
         return {
             self.audio_column: "audio",
             self.label_column: "labels",
         }





