

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

import numpy as np
import torch
from scipy import integrate

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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : torch.FloatTensor
      lowerCamelCase_      : Optional[torch.FloatTensor]  			=   None



def   UpperCamelCase(   lowercase_     ,   lowercase_=0.9_99     ,   lowercase_="cosine"     ,   )      ->							int:





  '''simple docstring'''





  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase_				):
      return math.cos((t + 0.0_08) / 1.0_08 * 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}'''				)

  snake_case_					= []
  for i in range(lowercase_				):
    snake_case_					= i / num_diffusion_timesteps
    snake_case_					= (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 					__lowerCamelCase							(      __snake_case    ,				__snake_case      ):
      lowerCamelCase_      : Optional[Any]  			=   [e.name for e in KarrasDiffusionSchedulers]
      lowerCamelCase_      : Optional[int]  			=   1







      @register_to_config
      def __init__(	self				,    lowerCamelCase = 1000				,    lowerCamelCase = 0.0001				,    lowerCamelCase = 0.02				,    lowerCamelCase = "linear"				,    lowerCamelCase = None				,    lowerCamelCase = False				,    lowerCamelCase = "epsilon"				,    lowerCamelCase = "linspace"				,    lowerCamelCase = 0				,    )		->			Dict:
        if trained_betas is not None:
          snake_case_					= torch.tensor(lowerCamelCase				,    dtype=torch.floataa  )
        elif beta_schedule == "linear":
          snake_case_					= torch.linspace(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    dtype=torch.floataa  )
        elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          snake_case_					= (
              torch.linspace(beta_start**0.5				,    beta_end**0.5				,    lowerCamelCase				,    dtype=torch.floataa  ) ** 2
          )
        elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          snake_case_					= betas_for_alpha_bar(lowerCamelCase  )
        else:
          raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''  )

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

        snake_case_					= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
        snake_case_					= np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
        snake_case_					= torch.from_numpy(lowerCamelCase  )

        # setable values
        snake_case_					= None
        snake_case_					= use_karras_sigmas
        self.set_timesteps(lowerCamelCase				,    lowerCamelCase  )
        snake_case_					= []
        snake_case_					= False







      @property
      def       lowerCAmelCase_       (	self  )		->			int:
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

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







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase  )		->			torch.FloatTensor:
        if isinstance(lowerCamelCase				,    torch.Tensor  ):
          snake_case_					= timestep.to(self.timesteps.device  )
        snake_case_					= (self.timesteps == timestep).nonzero().item()
        snake_case_					= self.sigmas[step_index]
        snake_case_					= sample / ((sigma**2 + 1) ** 0.5)
        snake_case_					= True
        return sample







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Any:

        def lms_derivative(lowerCamelCase  ):
          snake_case_					= 1.0
          for k in range(lowerCamelCase  ):
            if current_order == k:
              continue
            prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
          return prod

        snake_case_					= integrate.quad(lowerCamelCase				,    self.sigmas[t]				,    self.sigmas[t + 1]				,    epsrel=1e-4  )[0]

        return integrated_coeff







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase = None  )		->			Tuple:
        snake_case_					= 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":
          snake_case_					= np.linspace(0				,    self.config.num_train_timesteps - 1				,    lowerCamelCase				,    dtype=lowerCamelCase  )[
              ::-1
          ].copy()
        elif self.config.timestep_spacing == "leading":
          snake_case_					= 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
          snake_case_					= (np.arange(0				,    lowerCamelCase  ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase  )
          timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
          snake_case_					= 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
          snake_case_					= (np.arange(self.config.num_train_timesteps				,    0				,    -step_ratio  )).round().copy().astype(lowerCamelCase  )
          timesteps -= 1
        else:
          raise ValueError(
              f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''  )

        snake_case_					= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
        snake_case_					= np.log(lowerCamelCase  )
        snake_case_					= np.interp(lowerCamelCase				,    np.arange(0				,    len(lowerCamelCase  )  )				,    lowerCamelCase  )

        if self.use_karras_sigmas:
          snake_case_					= self._convert_to_karras(in_sigmas=lowerCamelCase  )
          snake_case_					= np.array([self._sigma_to_t(lowerCamelCase				,    lowerCamelCase  ) for sigma in sigmas]  )

        snake_case_					= np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )

        snake_case_					= torch.from_numpy(lowerCamelCase  ).to(device=lowerCamelCase  )
        if str(lowerCamelCase  ).startswith("""mps"""  ):
          # mps does not support float64
          snake_case_					= torch.from_numpy(lowerCamelCase  ).to(lowerCamelCase				,    dtype=torch.floataa  )
        else:
          snake_case_					= torch.from_numpy(lowerCamelCase  ).to(device=lowerCamelCase  )

        snake_case_					= []







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase  )		->			Dict:
        # get log sigma
        snake_case_					= np.log(lowerCamelCase  )

        # get distribution
        snake_case_					= log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        snake_case_					= np.cumsum((dists >= 0)				,    axis=0  ).argmax(axis=0  ).clip(max=log_sigmas.shape[0] - 2  )
        snake_case_					= low_idx + 1

        snake_case_					= log_sigmas[low_idx]
        snake_case_					= log_sigmas[high_idx]

        # interpolate sigmas
        snake_case_					= (low - log_sigma) / (low - high)
        snake_case_					= np.clip(lowerCamelCase				,    0				,    1  )

        # transform interpolation to time range
        snake_case_					= (1 - w) * low_idx + w * high_idx
        snake_case_					= t.reshape(sigma.shape  )
        return t







      def       lowerCAmelCase_       (	self				,    lowerCamelCase  )		->			torch.FloatTensor:
        snake_case_					= in_sigmas[-1].item()
        snake_case_					= in_sigmas[0].item()

        snake_case_					= 7.0  # 7.0 is the value used in the paper
        snake_case_					= np.linspace(0				,    1				,    self.num_inference_steps  )
        snake_case_					= sigma_min ** (1 / rho)
        snake_case_					= sigma_max ** (1 / rho)
        snake_case_					= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = 4				,    lowerCamelCase = True				,    )		->			Union[LMSDiscreteSchedulerOutput, Tuple]:
        if not self.is_scale_input_called:
          warnings.warn(
              """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
              """See `StableDiffusionPipeline` for a usage example."""  )

        if isinstance(lowerCamelCase				,    torch.Tensor  ):
          snake_case_					= timestep.to(self.timesteps.device  )
        snake_case_					= (self.timesteps == timestep).nonzero().item()
        snake_case_					= self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          snake_case_					= sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          snake_case_					= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        elif self.config.prediction_type == "sample":
          snake_case_					= model_output
        else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

        # 2. Convert to an ODE derivative
        snake_case_					= (sample - pred_original_sample) / sigma
        self.derivatives.append(lowerCamelCase  )
        if len(self.derivatives  ) > order:
          self.derivatives.pop(0  )

        # 3. Compute linear multistep coefficients
        snake_case_					= min(step_index + 1				,    lowerCamelCase  )
        snake_case_					= [self.get_lms_coefficient(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  ) for curr_order in range(lowerCamelCase  )]

        # 4. Compute previous sample based on the derivatives path
        snake_case_					= sample + sum(
            coeff * derivative for coeff, derivative in zip(lowerCamelCase				,    reversed(self.derivatives  )  )  )

        if not return_dict:
          return (prev_sample,)

        return LMSDiscreteSchedulerOutput(prev_sample=lowerCamelCase				,    pred_original_sample=lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    )		->			torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        snake_case_					= self.sigmas.to(device=original_samples.device				,    dtype=original_samples.dtype  )
        if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase  ):
          # mps does not support float64
          snake_case_					= self.timesteps.to(original_samples.device				,    dtype=torch.floataa  )
          snake_case_					= timesteps.to(original_samples.device				,    dtype=torch.floataa  )
        else:
          snake_case_					= self.timesteps.to(original_samples.device  )
          snake_case_					= timesteps.to(original_samples.device  )

        snake_case_					= [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

        snake_case_					= original_samples + noise * sigma
        return noisy_samples







      def __len__(	self  )		->			Tuple:
        return self.config.num_train_timesteps


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : Dict  			=   'AutoFeatureExtractor'
      lowerCamelCase_      : Optional[int]  			=   'Speech2Text2Tokenizer'







      def __init__(	self				,    lowerCamelCase				,    lowerCamelCase  )		->			List[str]:
        super().__init__(lowerCamelCase				,    lowerCamelCase  )
        snake_case_					= self.feature_extractor
        snake_case_					= False







      def __call__(	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			Dict:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor(*lowerCamelCase				,    **lowerCamelCase  )

        if "raw_speech" in kwargs:
          warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""  )
          snake_case_					= kwargs.pop("""raw_speech"""  )
        else:
          snake_case_					= kwargs.pop("""audio"""				,    lowerCamelCase  )
        snake_case_					= kwargs.pop("""sampling_rate"""				,    lowerCamelCase  )
        snake_case_					= kwargs.pop("""text"""				,    lowerCamelCase  )
        if len(lowerCamelCase  ) > 0:
          snake_case_					= args[0]
          snake_case_					= args[1:]

        if audio is None and text is None:
          raise ValueError("""You need to specify either an `audio` or `text` input to process."""  )

        if audio is not None:
          snake_case_					= self.feature_extractor(lowerCamelCase				,    *lowerCamelCase				,    sampling_rate=lowerCamelCase				,    **lowerCamelCase  )
        if text is not None:
          snake_case_					= self.tokenizer(lowerCamelCase				,    **lowerCamelCase  )

        if text is None:
          return inputs
        elif audio is None:
          return encodings
        else:
          snake_case_					= encodings["""input_ids"""]
          return inputs







      def       lowerCAmelCase_       (	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			int:
        return self.tokenizer.batch_decode(*lowerCamelCase				,    **lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    *lowerCamelCase				,    **lowerCamelCase  )		->			Union[str, Any]:
        return self.tokenizer.decode(*lowerCamelCase				,    **lowerCamelCase  )







      @contextmanager
      def       lowerCAmelCase_       (	self  )		->			Any:
        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your audio inputs, or in a separate call."""  )
        snake_case_					= True
        snake_case_					= self.tokenizer
        yield
        snake_case_					= self.feature_extractor
        snake_case_					= False