
"""simple docstring"""




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def lowercase_						(     _snake_case     ,_snake_case=0.999     ,_snake_case="cosine"     ,):
       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

       SCREAMING_SNAKE_CASE__		: int     =     []
       for i in range(a_		):
              SCREAMING_SNAKE_CASE__		: Optional[Any]     =     i / num_diffusion_timesteps
              SCREAMING_SNAKE_CASE__		: int     =     (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(a_		) / alpha_bar_fn(a_		)     ,a_		)		)
       return torch.tensor(a_     ,dtype=torch.floataa		)



class 			lowerCAmelCase_		(_UpperCamelCase				,      _UpperCamelCase						):


    """simple docstring"""



    __UpperCamelCase   :		Dict       =     [e.name for e in KarrasDiffusionSchedulers]
    __UpperCamelCase   :		Dict       =     2



    @register_to_config
    def __init__(self     , SCREAMING_SNAKE_CASE__ = 10_00     , SCREAMING_SNAKE_CASE__ = 0.00085     , SCREAMING_SNAKE_CASE__ = 0.012     , SCREAMING_SNAKE_CASE__ = "linear"     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = "epsilon"     , SCREAMING_SNAKE_CASE__ = "linspace"     , SCREAMING_SNAKE_CASE__ = 0     , )      ->	Tuple:



           """simple docstring"""




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

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

           #  set all values
           self.set_timesteps(_UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase					)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=None					)      ->	Tuple:



           """simple docstring"""




           if schedule_timesteps is None:
                  SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.timesteps

           SCREAMING_SNAKE_CASE__		: List[str]     =     (schedule_timesteps == timestep).nonzero()

           # The sigma index that is taken for the **very** first `step`
           # is always the second index (or the last index if there is only 1)
           # This way we can ensure we don't accidentally skip a sigma in
           # case we start in the middle of the denoising schedule (e.g. for image-to-image)
           if len(self._index_counter					) == 0:
                  SCREAMING_SNAKE_CASE__		: int     =     1 if len(_UpperCAmelCase					) > 1 else 0
           else:
                  SCREAMING_SNAKE_CASE__		: Tuple     =     timestep.cpu().item() if torch.is_tensor(_UpperCAmelCase					) else timestep
                  SCREAMING_SNAKE_CASE__		: List[Any]     =     self._index_counter[timestep_int]

           return indices[pos].item()



    @property
    def 			__magic_name__					(self					)      ->	int:



           """simple docstring"""




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

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



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , )      ->	Optional[int]:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: List[Any]     =     self.index_for_timestep(_UpperCAmelCase					)

           if self.state_in_first_order:
                  SCREAMING_SNAKE_CASE__		: Any     =     self.sigmas[step_index]
           else:
                  SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.sigmas_interpol[step_index]

           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     sample / ((sigma**2 + 1) ** 0.5)
           return sample



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , )      ->	int:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Tuple     =     num_inference_steps

           SCREAMING_SNAKE_CASE__		: List[str]     =     num_train_timesteps or self.config.num_train_timesteps

           # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
           if self.config.timestep_spacing == "linspace":
                  SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     np.linspace(0     , num_train_timesteps - 1     , _UpperCAmelCase     , dtype=_UpperCAmelCase					)[::-1].copy()
           elif self.config.timestep_spacing == "leading":
                  SCREAMING_SNAKE_CASE__		: List[str]     =     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
                  SCREAMING_SNAKE_CASE__		: Optional[int]     =     (np.arange(0     , _UpperCAmelCase					) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase					)
                  timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
                  SCREAMING_SNAKE_CASE__		: List[str]     =     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
                  SCREAMING_SNAKE_CASE__		: Optional[Any]     =     (np.arange(_UpperCAmelCase     , 0     , -step_ratio					)).round().copy().astype(_UpperCAmelCase					)
                  timesteps -= 1
           else:
                  raise ValueError(
                      F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

           SCREAMING_SNAKE_CASE__		: List[str]     =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
           SCREAMING_SNAKE_CASE__		: Dict     =     torch.from_numpy(np.log(_UpperCAmelCase					)					).to(_UpperCAmelCase					)

           SCREAMING_SNAKE_CASE__		: int     =     np.interp(_UpperCAmelCase     , np.arange(0     , len(_UpperCAmelCase					)					)     , _UpperCAmelCase					)
           SCREAMING_SNAKE_CASE__		: Any     =     np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
           SCREAMING_SNAKE_CASE__		: Optional[int]     =     torch.from_numpy(_UpperCAmelCase					).to(device=_UpperCAmelCase					)

           # interpolate sigmas
           SCREAMING_SNAKE_CASE__		: int     =     sigmas.log().lerp(sigmas.roll(1					).log()     , 0.5					).exp()

           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
           SCREAMING_SNAKE_CASE__		: str     =     torch.cat(
               [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)

           if str(_UpperCAmelCase					).startswith("""mps"""					):
                  # mps does not support float64
                  SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.from_numpy(_UpperCAmelCase					).to(_UpperCAmelCase     , dtype=torch.floataa					)
           else:
                  SCREAMING_SNAKE_CASE__		: int     =     torch.from_numpy(_UpperCAmelCase					).to(_UpperCAmelCase					)

           # interpolate timesteps
           SCREAMING_SNAKE_CASE__		: int     =     self.sigma_to_t(_UpperCAmelCase					).to(_UpperCAmelCase     , dtype=timesteps.dtype					)
           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])     , dim=-1					).flatten()

           SCREAMING_SNAKE_CASE__		: Dict     =     torch.cat([timesteps[:1], interleaved_timesteps]					)

           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     None

           # for exp beta schedules, such as the one for `pipeline_shap_e.py`
           # we need an index counter
           SCREAMING_SNAKE_CASE__		: Tuple     =     defaultdict(_UpperCAmelCase					)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     sigma.log()

           # get distribution
           SCREAMING_SNAKE_CASE__		: List[str]     =     log_sigma - self.log_sigmas[:, None]

           # get sigmas range
           SCREAMING_SNAKE_CASE__		: int     =     dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
           SCREAMING_SNAKE_CASE__		: int     =     low_idx + 1

           SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.log_sigmas[low_idx]
           SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.log_sigmas[high_idx]

           # interpolate sigmas
           SCREAMING_SNAKE_CASE__		: Optional[int]     =     (low - log_sigma) / (low - high)
           SCREAMING_SNAKE_CASE__		: List[str]     =     w.clamp(0     , 1					)

           # transform interpolation to time range
           SCREAMING_SNAKE_CASE__		: List[Any]     =     (1 - w) * low_idx + w * high_idx
           SCREAMING_SNAKE_CASE__		: Any     =     t.view(sigma.shape					)
           return t



    @property
    def 			__magic_name__					(self					)      ->	Any:



           """simple docstring"""




           return self.sample is None



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = True     , )      ->	Optional[int]:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.index_for_timestep(_UpperCAmelCase					)

           # advance index counter by 1
           SCREAMING_SNAKE_CASE__		: int     =     timestep.cpu().item() if torch.is_tensor(_UpperCAmelCase					) else timestep
           self._index_counter[timestep_int] += 1

           if self.state_in_first_order:
                  SCREAMING_SNAKE_CASE__		: str     =     self.sigmas[step_index]
                  SCREAMING_SNAKE_CASE__		: Tuple     =     self.sigmas_interpol[step_index + 1]
                  SCREAMING_SNAKE_CASE__		: Any     =     self.sigmas[step_index + 1]
           else:
                  # 2nd order / KDPM2's method
                  SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.sigmas[step_index - 1]
                  SCREAMING_SNAKE_CASE__		: int     =     self.sigmas_interpol[step_index]
                  SCREAMING_SNAKE_CASE__		: List[Any]     =     self.sigmas[step_index]

           # currently only gamma=0 is supported. This usually works best anyways.
           # We can support gamma in the future but then need to scale the timestep before
           # passing it to the model which requires a change in API
           SCREAMING_SNAKE_CASE__		: List[Any]     =     0
           SCREAMING_SNAKE_CASE__		: List[Any]     =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
                  SCREAMING_SNAKE_CASE__		: Dict     =     sigma_hat if self.state_in_first_order else sigma_interpol
                  SCREAMING_SNAKE_CASE__		: int     =     sample - sigma_input * model_output
           elif self.config.prediction_type == "v_prediction":
                  SCREAMING_SNAKE_CASE__		: List[str]     =     sigma_hat if self.state_in_first_order else sigma_interpol
                  SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                      sample / (sigma_input**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`'''					)

           if self.state_in_first_order:
                  # 2. Convert to an ODE derivative for 1st order
                  SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     (sample - pred_original_sample) / sigma_hat
                  # 3. delta timestep
                  SCREAMING_SNAKE_CASE__		: Optional[int]     =     sigma_interpol - sigma_hat

                  # store for 2nd order step
                  SCREAMING_SNAKE_CASE__		: Dict     =     sample
           else:
                  # DPM-Solver-2
                  # 2. Convert to an ODE derivative for 2nd order
                  SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     (sample - pred_original_sample) / sigma_interpol

                  # 3. delta timestep
                  SCREAMING_SNAKE_CASE__		: List[str]     =     sigma_next - sigma_hat

                  SCREAMING_SNAKE_CASE__		: Tuple     =     self.sample
                  SCREAMING_SNAKE_CASE__		: Tuple     =     None

           SCREAMING_SNAKE_CASE__		: int     =     sample + derivative * dt

           if not return_dict:
                  return (prev_sample,)

           return SchedulerOutput(prev_sample=_UpperCAmelCase					)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , )      ->	Optional[int]:



           """simple docstring"""




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

           SCREAMING_SNAKE_CASE__		: List[Any]     =     [self.index_for_timestep(_UpperCAmelCase     , _UpperCAmelCase					) for t in timesteps]

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

           SCREAMING_SNAKE_CASE__		: List[Any]     =     original_samples + noise * sigma
           return noisy_samples



    def __len__(self					)      ->	Optional[Any]:



           """simple docstring"""




           return self.config.num_train_timesteps






"""simple docstring"""




import functools
import operator

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


UpperCAmelCase__   :					str												=				logging.get_logger(__name__)

UpperCAmelCase__   :					Dict												=				{
    'microsoft/unispeech-large-1500h-cv': (
        'https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[str]       =     '''unispeech'''



				def __init__(self     , SCREAMING_SNAKE_CASE__=32     , SCREAMING_SNAKE_CASE__=7_68     , SCREAMING_SNAKE_CASE__=12     , SCREAMING_SNAKE_CASE__=12     , SCREAMING_SNAKE_CASE__=30_72     , SCREAMING_SNAKE_CASE__="gelu"     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.02     , SCREAMING_SNAKE_CASE__=1E-5     , SCREAMING_SNAKE_CASE__="group"     , SCREAMING_SNAKE_CASE__="gelu"     , SCREAMING_SNAKE_CASE__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)     , SCREAMING_SNAKE_CASE__=(5, 2, 2, 2, 2, 2, 2)     , SCREAMING_SNAKE_CASE__=(10, 3, 3, 3, 3, 2, 2)     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=1_28     , SCREAMING_SNAKE_CASE__=16     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=0.05     , SCREAMING_SNAKE_CASE__=10     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=10     , SCREAMING_SNAKE_CASE__=0     , SCREAMING_SNAKE_CASE__=3_20     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=1_00     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__="mean"     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=80     , SCREAMING_SNAKE_CASE__=0     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.5     , **SCREAMING_SNAKE_CASE__     , )      ->	List[Any]:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__     , pad_token_id=SCREAMING_SNAKE_CASE__     , bos_token_id=SCREAMING_SNAKE_CASE__     , eos_token_id=SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     hidden_size
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     feat_extract_norm
											SCREAMING_SNAKE_CASE__		: Any     =     feat_extract_activation
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: str     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     list(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     conv_bias
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     num_conv_pos_embeddings
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     num_conv_pos_embedding_groups
											SCREAMING_SNAKE_CASE__		: Dict     =     len(self.conv_dim					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     num_hidden_layers
											SCREAMING_SNAKE_CASE__		: str     =     intermediate_size
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     hidden_act
											SCREAMING_SNAKE_CASE__		: Any     =     num_attention_heads
											SCREAMING_SNAKE_CASE__		: Dict     =     hidden_dropout
											SCREAMING_SNAKE_CASE__		: List[str]     =     attention_dropout
											SCREAMING_SNAKE_CASE__		: Any     =     activation_dropout
											SCREAMING_SNAKE_CASE__		: int     =     feat_proj_dropout
											SCREAMING_SNAKE_CASE__		: List[Any]     =     final_dropout
											SCREAMING_SNAKE_CASE__		: str     =     layerdrop
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     layer_norm_eps
											SCREAMING_SNAKE_CASE__		: Dict     =     initializer_range
											SCREAMING_SNAKE_CASE__		: Dict     =     num_ctc_classes
											SCREAMING_SNAKE_CASE__		: List[Any]     =     vocab_size
											SCREAMING_SNAKE_CASE__		: str     =     do_stable_layer_norm
											SCREAMING_SNAKE_CASE__		: Dict     =     use_weighted_layer_sum
											SCREAMING_SNAKE_CASE__		: Union[str, 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
											SCREAMING_SNAKE_CASE__		: Tuple     =     apply_spec_augment
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     mask_time_prob
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     mask_time_length
											SCREAMING_SNAKE_CASE__		: List[str]     =     mask_time_min_masks
											SCREAMING_SNAKE_CASE__		: Tuple     =     mask_feature_prob
											SCREAMING_SNAKE_CASE__		: Any     =     mask_feature_length
											SCREAMING_SNAKE_CASE__		: int     =     mask_feature_min_masks

											# parameters for pretraining with codevector quantized representations
											SCREAMING_SNAKE_CASE__		: Dict     =     num_codevectors_per_group
											SCREAMING_SNAKE_CASE__		: Any     =     num_codevector_groups
											SCREAMING_SNAKE_CASE__		: str     =     contrastive_logits_temperature
											SCREAMING_SNAKE_CASE__		: str     =     feat_quantizer_dropout
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     num_negatives
											SCREAMING_SNAKE_CASE__		: Tuple     =     codevector_dim
											SCREAMING_SNAKE_CASE__		: List[Any]     =     proj_codevector_dim
											SCREAMING_SNAKE_CASE__		: Tuple     =     diversity_loss_weight

											# ctc loss
											SCREAMING_SNAKE_CASE__		: int     =     ctc_loss_reduction
											SCREAMING_SNAKE_CASE__		: str     =     ctc_zero_infinity

											# pretraining loss
											SCREAMING_SNAKE_CASE__		: List[str]     =     replace_prob



				@property
				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




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




