


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						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   42
       _UpperCamelCase     		=   None







def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=0.999			,  __lowerCamelCase="cosine"			,  )		->Tuple:


 '''simple docstring'''
 if alpha_transform_type == "cosine":

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

 elif alpha_transform_type == "exp":

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

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

 snake_case :			int							=   []
 for i in range(__lowerCamelCase							):
  snake_case :			str							=   i / num_diffusion_timesteps
  snake_case :			Dict							=   (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(__lowerCamelCase							) / alpha_bar_fn(__lowerCamelCase							)			,  __lowerCamelCase							)							)
 return torch.tensor(__lowerCamelCase			,  dtype=torch.floataa							)





class   lowerCamelCase						(	SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   [e.name for e in KarrasDiffusionSchedulers]
       _UpperCamelCase     		=   1

       @register_to_config
       def __init__(      self						,    a__ = 10_00						,    a__ = 0.0001						,    a__ = 0.02						,    a__ = "linear"						,    a__ = None						,    a__ = False						,    a__ = "epsilon"						,    a__ = "linspace"						,    a__ = 0						,    ):






        '''simple docstring'''






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

        snake_case :			List[Any]							=   1.0 - self.betas
        snake_case :			Union[str, Any]							=   torch.cumprod(self.alphas						,    dim=0       )

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

        # setable values
        snake_case :			Tuple							=   None
        snake_case :			str							=   use_karras_sigmas
        self.set_timesteps(a__						,    a__       )
        snake_case :			Any							=   []
        snake_case :			str							=   False

       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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

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

       def 					UpperCAmelCase					(      self						,    a__						,    a__       ):






        '''simple docstring'''






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

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__       ):






        '''simple docstring'''






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

        snake_case :			str							=   integrate.quad(a__						,    self.sigmas[t]						,    self.sigmas[t + 1]						,    epsrel=1e-4       )[0]

        return integrated_coeff

       def 					UpperCAmelCase					(      self						,    a__						,    a__ = None       ):






        '''simple docstring'''






        snake_case :			List[str]							=   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 :			Union[str, Any]							=   np.linspace(0						,    self.config.num_train_timesteps - 1						,    a__						,    dtype=a__       )[
             ::-1
         ].copy()
        elif self.config.timestep_spacing == "leading":
         snake_case :			Tuple							=   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 :			str							=   (np.arange(0						,    a__       ) * step_ratio).round()[::-1].copy().astype(a__       )
         timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
         snake_case :			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
         snake_case :			str							=   (np.arange(self.config.num_train_timesteps						,    0						,    -step_ratio       )).round().copy().astype(a__       )
         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 :			Optional[int]							=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
        snake_case :			Union[str, Any]							=   np.log(a__       )
        snake_case :			Optional[Any]							=   np.interp(a__						,    np.arange(0						,    len(a__       )       )						,    a__       )

        if self.use_karras_sigmas:
         snake_case :			List[str]							=   self._convert_to_karras(in_sigmas=a__       )
         snake_case :			int							=   np.array([self._sigma_to_t(a__						,    a__       ) for sigma in sigmas]       )

        snake_case :			str							=   np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )

        snake_case :			List[Any]							=   torch.from_numpy(a__       ).to(device=a__       )
        if str(a__       ).startswith('''mps'''       ):
         # mps does not support float64
         snake_case :			Any							=   torch.from_numpy(a__       ).to(a__						,    dtype=torch.floataa       )
        else:
         snake_case :			str							=   torch.from_numpy(a__       ).to(device=a__       )

        snake_case :			Union[str, Any]							=   []

       def 					UpperCAmelCase					(      self						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			int							=   np.log(a__       )

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

        # get sigmas range
        snake_case :			Optional[Any]							=   np.cumsum((dists >= 0)						,    axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
        snake_case :			Union[str, Any]							=   low_idx + 1

        snake_case :			str							=   log_sigmas[low_idx]
        snake_case :			Optional[int]							=   log_sigmas[high_idx]

        # interpolate sigmas
        snake_case :			int							=   (low - log_sigma) / (low - high)
        snake_case :			str							=   np.clip(a__						,    0						,    1       )

        # transform interpolation to time range
        snake_case :			List[str]							=   (1 - w) * low_idx + w * high_idx
        snake_case :			Union[str, Any]							=   t.reshape(sigma.shape       )
        return t

       def 					UpperCAmelCase					(      self						,    a__       ):






        '''simple docstring'''






        snake_case :			float							=   in_sigmas[-1].item()
        snake_case :			float							=   in_sigmas[0].item()

        snake_case :			Union[str, Any]							=   7.0  # 7.0 is the value used in the paper
        snake_case :			Tuple							=   np.linspace(0						,    1						,    self.num_inference_steps       )
        snake_case :			List[Any]							=   sigma_min ** (1 / rho)
        snake_case :			Optional[Any]							=   sigma_max ** (1 / rho)
        snake_case :			Optional[int]							=   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    a__ = 4						,    a__ = True						,    ):






        '''simple docstring'''






        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(a__						,    torch.Tensor       ):
         snake_case :			Optional[int]							=   timestep.to(self.timesteps.device       )
        snake_case :			Any							=   (self.timesteps == timestep).nonzero().item()
        snake_case :			Tuple							=   self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
         snake_case :			Union[str, Any]							=   sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         snake_case :			Tuple							=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        elif self.config.prediction_type == "sample":
         snake_case :			int							=   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 :			int							=   (sample - pred_original_sample) / sigma
        self.derivatives.append(a__       )
        if len(self.derivatives       ) > order:
         self.derivatives.pop(0       )

        # 3. Compute linear multistep coefficients
        snake_case :			Any							=   min(step_index + 1						,    a__       )
        snake_case :			List[Any]							=   [self.get_lms_coefficient(a__						,    a__						,    a__       ) for curr_order in range(a__       )]

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

        if not return_dict:
         return (prev_sample,)

        return LMSDiscreteSchedulerOutput(prev_sample=a__						,    pred_original_sample=a__       )

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    ):






        '''simple docstring'''






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

        snake_case :			Union[str, Any]							=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

        snake_case :			Tuple							=   original_samples + noise * sigma
        return noisy_samples



       def __len__(      self       ):






        '''simple docstring'''






        return self.config.num_train_timesteps










import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a					:				Dict       =      logging.get_logger(__name__)

a					:				str       =      {
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hubert"""

       def __init__(      self						,    a__=32						,    a__=7_68						,    a__=12						,    a__=12						,    a__=30_72						,    a__="gelu"						,    a__=0.1						,    a__=0.1						,    a__=0.1						,    a__=True						,    a__=0.0						,    a__=0.1						,    a__=0.1						,    a__=0.02						,    a__=1e-5						,    a__="group"						,    a__="gelu"						,    a__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)						,    a__=(5, 2, 2, 2, 2, 2, 2)						,    a__=(10, 3, 3, 3, 3, 2, 2)						,    a__=False						,    a__=1_28						,    a__=16						,    a__=False						,    a__=True						,    a__=0.05						,    a__=10						,    a__=2						,    a__=0.0						,    a__=10						,    a__=0						,    a__="sum"						,    a__=False						,    a__=False						,    a__=2_56						,    a__=0						,    a__=1						,    a__=2						,    **a__						,    ):






        '''simple docstring'''






        super().__init__(**a__						,    pad_token_id=a__						,    bos_token_id=a__						,    eos_token_id=a__       )
        snake_case :			List[str]							=   hidden_size
        snake_case :			List[str]							=   feat_extract_norm
        snake_case :			List[str]							=   feat_extract_activation
        snake_case :			Dict							=   list(a__       )
        snake_case :			Tuple							=   list(a__       )
        snake_case :			Dict							=   list(a__       )
        snake_case :			str							=   conv_bias
        snake_case :			Dict							=   num_conv_pos_embeddings
        snake_case :			Union[str, Any]							=   num_conv_pos_embedding_groups
        snake_case :			Optional[int]							=   len(self.conv_dim       )
        snake_case :			Tuple							=   num_hidden_layers
        snake_case :			Any							=   intermediate_size
        snake_case :			Optional[Any]							=   hidden_act
        snake_case :			str							=   num_attention_heads
        snake_case :			List[Any]							=   hidden_dropout
        snake_case :			Union[str, Any]							=   attention_dropout
        snake_case :			Dict							=   activation_dropout
        snake_case :			List[str]							=   feat_proj_layer_norm
        snake_case :			List[str]							=   feat_proj_dropout
        snake_case :			Union[str, Any]							=   final_dropout
        snake_case :			List[str]							=   layerdrop
        snake_case :			str							=   layer_norm_eps
        snake_case :			Tuple							=   initializer_range
        snake_case :			str							=   vocab_size
        snake_case :			Tuple							=   do_stable_layer_norm
        snake_case :			Dict							=   use_weighted_layer_sum
        snake_case :			Optional[Any]							=   classifier_proj_size

        if (
            (len(self.conv_stride       ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
            or (len(self.conv_dim       ) != self.num_feat_extract_layers)
        ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
             ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
             f''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
             f''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        snake_case :			Optional[int]							=   apply_spec_augment
        snake_case :			int							=   mask_time_prob
        snake_case :			Dict							=   mask_time_length
        snake_case :			Any							=   mask_time_min_masks
        snake_case :			Any							=   mask_feature_prob
        snake_case :			Union[str, Any]							=   mask_feature_length
        snake_case :			List[Any]							=   mask_feature_min_masks

        # ctc loss
        snake_case :			List[str]							=   ctc_loss_reduction
        snake_case :			Dict							=   ctc_zero_infinity



       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        return functools.reduce(operator.mul						,    self.conv_stride						,    1       )






