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


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	torch.FloatTensor
       A							:	Optional[torch.FloatTensor]          =	None







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Optional[Any]		,							__SCREAMING_SNAKE_CASE       :					Any=0.9_99		,							__SCREAMING_SNAKE_CASE       :					int="cosine"		,							):







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

        def alpha_bar_fn(__SCREAMING_SNAKE_CASE       :					Dict   ):
            return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2   ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__SCREAMING_SNAKE_CASE       :					Optional[int]   ):
            return math.exp(t * -12.0   )

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

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







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase   ,							__UpperCamelCase    ):
       A							:	Optional[int]          =	[e.name for e in KarrasDiffusionSchedulers]
       A							:	Optional[Any]          =	1

       @register_to_config
       def __init__(							self					:     Tuple			,			_lowerCAmelCase					:     int = 10_00			,			_lowerCAmelCase					:     float = 0.0001			,			_lowerCAmelCase					:     float = 0.02			,			_lowerCAmelCase					:     str = "linear"			,			_lowerCAmelCase					:     Optional[Union[np.ndarray, List[float]]] = None			,			_lowerCAmelCase					:     str = "epsilon"			,			_lowerCAmelCase					:     str = "linspace"			,			_lowerCAmelCase					:     int = 0			,			):
           if trained_betas is not None:
               __snake_case       :				Optional[Any] 			=    torch.tensor(_lowerCAmelCase			,			dtype=torch.floataa			)
           elif beta_schedule == "linear":
               __snake_case       :				Optional[Any] 			=    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       :				Optional[Any] 			=    (
                   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       :				Tuple 			=    betas_for_alpha_bar(_lowerCAmelCase			)
           else:
               raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

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

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

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

       @property
       def 			snake_case__    (							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 			snake_case__    (							self					:     str			,			_lowerCAmelCase					:     torch.FloatTensor			,			_lowerCAmelCase					:     Union[float, torch.FloatTensor]			):
           if isinstance(_lowerCAmelCase			,			torch.Tensor			):
               __snake_case       :				Any 			=    timestep.to(self.timesteps.device			)
           __snake_case       :				Union[str, Any] 			=    (self.timesteps == timestep).nonzero().item()
           __snake_case       :				Union[str, Any] 			=    self.sigmas[step_index]
           __snake_case       :				Optional[int] 			=    sample / ((sigma**2 + 1) ** 0.5)
           __snake_case       :				Tuple 			=    True
           return sample

       def 			snake_case__    (							self					:     str			,			_lowerCAmelCase					:     int			,			_lowerCAmelCase					:     Union[str, torch.device] = None			):
           __snake_case       :				List[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":
               __snake_case       :				Union[str, Any] 			=    np.linspace(0			,			self.config.num_train_timesteps - 1			,			_lowerCAmelCase			,			dtype=_lowerCAmelCase			)[
                   ::-1
               ].copy()
           elif self.config.timestep_spacing == "leading":
               __snake_case       :				Optional[int] 			=    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       :				List[str] 			=    (np.arange(0			,			_lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase			)
               timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
               __snake_case       :				int 			=    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       :				Tuple 			=    (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       :				Any 			=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
           __snake_case       :				int 			=    np.interp(_lowerCAmelCase			,			np.arange(0			,			len(_lowerCAmelCase			)			)			,			_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
           __snake_case       :				Any 			=    torch.from_numpy(_lowerCAmelCase			).to(device=_lowerCAmelCase			)
           if str(_lowerCAmelCase			).startswith("""mps"""			):
               # mps does not support float64
               __snake_case       :				str 			=    torch.from_numpy(_lowerCAmelCase			).to(_lowerCAmelCase			,			dtype=torch.floataa			)
           else:
               __snake_case       :				List[str] 			=    torch.from_numpy(_lowerCAmelCase			).to(device=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Optional[int]			,			_lowerCAmelCase					:     torch.FloatTensor			,			_lowerCAmelCase					:     Union[float, torch.FloatTensor]			,			_lowerCAmelCase					:     torch.FloatTensor			,			_lowerCAmelCase					:     Optional[torch.Generator] = None			,			_lowerCAmelCase					:     bool = True			,			):

           if (
               isinstance(_lowerCAmelCase			,			_lowerCAmelCase			)
               or isinstance(_lowerCAmelCase			,			torch.IntTensor			)
               or isinstance(_lowerCAmelCase			,			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(_lowerCAmelCase			,			torch.Tensor			):
               __snake_case       :				int 			=    timestep.to(self.timesteps.device			)

           __snake_case       :				Tuple 			=    (self.timesteps == timestep).nonzero().item()
           __snake_case       :				Optional[int] 			=    self.sigmas[step_index]

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
               __snake_case       :				List[str] 			=    sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               __snake_case       :				int 			=    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`'''			)

           __snake_case       :				int 			=    self.sigmas[step_index]
           __snake_case       :				str 			=    self.sigmas[step_index + 1]
           __snake_case       :				List[Any] 			=    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
           __snake_case       :				Dict 			=    (sigma_to**2 - sigma_up**2) ** 0.5

           # 2. Convert to an ODE derivative
           __snake_case       :				List[Any] 			=    (sample - pred_original_sample) / sigma

           __snake_case       :				Optional[int] 			=    sigma_down - sigma

           __snake_case       :				Tuple 			=    sample + derivative * dt

           __snake_case       :				Any 			=    model_output.device
           __snake_case       :				Tuple 			=    randn_tensor(model_output.shape			,			dtype=model_output.dtype			,			device=_lowerCAmelCase			,			generator=_lowerCAmelCase			)

           __snake_case       :				Any 			=    prev_sample + noise * sigma_up

           if not return_dict:
               return (prev_sample,)

           return EulerAncestralDiscreteSchedulerOutput(
               prev_sample=_lowerCAmelCase			,			pred_original_sample=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Union[str, Any]			,			_lowerCAmelCase					:     torch.FloatTensor			,			_lowerCAmelCase					:     torch.FloatTensor			,			_lowerCAmelCase					:     torch.FloatTensor			,			):
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           __snake_case       :				Optional[Any] 			=    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       :				Union[str, Any] 			=    self.timesteps.to(original_samples.device			,			dtype=torch.floataa			)
               __snake_case       :				List[Any] 			=    timesteps.to(original_samples.device			,			dtype=torch.floataa			)
           else:
               __snake_case       :				Tuple 			=    self.timesteps.to(original_samples.device			)
               __snake_case       :				List[Any] 			=    timesteps.to(original_samples.device			)

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

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

           __snake_case       :				Optional[int] 			=    original_samples + noise * sigma
           return noisy_samples





       def __len__(							self					:     int			):
           return self.config.num_train_timesteps




import functools
import operator

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


lowercase_  							=			logging.get_logger(__name__)

lowercase_  							=			{
    "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      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	List[str]          =	"hubert"

       def __init__(							self					:     List[str]			,			_lowerCAmelCase					:     Union[str, Any]=32			,			_lowerCAmelCase					:     Any=7_68			,			_lowerCAmelCase					:     Optional[Any]=12			,			_lowerCAmelCase					:     Optional[int]=12			,			_lowerCAmelCase					:     int=30_72			,			_lowerCAmelCase					:     Optional[Any]="gelu"			,			_lowerCAmelCase					:     List[str]=0.1			,			_lowerCAmelCase					:     Any=0.1			,			_lowerCAmelCase					:     List[str]=0.1			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Dict=0.0			,			_lowerCAmelCase					:     List[str]=0.1			,			_lowerCAmelCase					:     Optional[int]=0.1			,			_lowerCAmelCase					:     Dict=0.02			,			_lowerCAmelCase					:     Any=1e-5			,			_lowerCAmelCase					:     str="group"			,			_lowerCAmelCase					:     Any="gelu"			,			_lowerCAmelCase					:     List[str]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)			,			_lowerCAmelCase					:     Any=(5, 2, 2, 2, 2, 2, 2)			,			_lowerCAmelCase					:     Dict=(10, 3, 3, 3, 3, 2, 2)			,			_lowerCAmelCase					:     List[str]=False			,			_lowerCAmelCase					:     Optional[Any]=1_28			,			_lowerCAmelCase					:     int=16			,			_lowerCAmelCase					:     Tuple=False			,			_lowerCAmelCase					:     int=True			,			_lowerCAmelCase					:     int=0.05			,			_lowerCAmelCase					:     Any=10			,			_lowerCAmelCase					:     Dict=2			,			_lowerCAmelCase					:     Dict=0.0			,			_lowerCAmelCase					:     Optional[int]=10			,			_lowerCAmelCase					:     Union[str, Any]=0			,			_lowerCAmelCase					:     int="sum"			,			_lowerCAmelCase					:     Optional[int]=False			,			_lowerCAmelCase					:     str=False			,			_lowerCAmelCase					:     Optional[Any]=2_56			,			_lowerCAmelCase					:     Any=0			,			_lowerCAmelCase					:     List[Any]=1			,			_lowerCAmelCase					:     List[str]=2			,			**_lowerCAmelCase					:     Optional[int]			,			):
           super().__init__(**_lowerCAmelCase			,			pad_token_id=_lowerCAmelCase			,			bos_token_id=_lowerCAmelCase			,			eos_token_id=_lowerCAmelCase			)
           __snake_case       :				Optional[int] 			=    hidden_size
           __snake_case       :				Optional[Any] 			=    feat_extract_norm
           __snake_case       :				Optional[int] 			=    feat_extract_activation
           __snake_case       :				List[str] 			=    list(_lowerCAmelCase			)
           __snake_case       :				Any 			=    list(_lowerCAmelCase			)
           __snake_case       :				List[Any] 			=    list(_lowerCAmelCase			)
           __snake_case       :				Any 			=    conv_bias
           __snake_case       :				Tuple 			=    num_conv_pos_embeddings
           __snake_case       :				List[Any] 			=    num_conv_pos_embedding_groups
           __snake_case       :				Union[str, Any] 			=    len(self.conv_dim			)
           __snake_case       :				List[Any] 			=    num_hidden_layers
           __snake_case       :				Tuple 			=    intermediate_size
           __snake_case       :				Optional[int] 			=    hidden_act
           __snake_case       :				Union[str, Any] 			=    num_attention_heads
           __snake_case       :				str 			=    hidden_dropout
           __snake_case       :				Union[str, Any] 			=    attention_dropout
           __snake_case       :				Dict 			=    activation_dropout
           __snake_case       :				List[Any] 			=    feat_proj_layer_norm
           __snake_case       :				List[str] 			=    feat_proj_dropout
           __snake_case       :				List[Any] 			=    final_dropout
           __snake_case       :				str 			=    layerdrop
           __snake_case       :				List[str] 			=    layer_norm_eps
           __snake_case       :				Optional[Any] 			=    initializer_range
           __snake_case       :				Optional[int] 			=    vocab_size
           __snake_case       :				Dict 			=    do_stable_layer_norm
           __snake_case       :				Tuple 			=    use_weighted_layer_sum
           __snake_case       :				List[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       :				Union[str, Any] 			=    apply_spec_augment
           __snake_case       :				List[str] 			=    mask_time_prob
           __snake_case       :				Dict 			=    mask_time_length
           __snake_case       :				int 			=    mask_time_min_masks
           __snake_case       :				List[str] 			=    mask_feature_prob
           __snake_case       :				Tuple 			=    mask_feature_length
           __snake_case       :				int 			=    mask_feature_min_masks

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





       @property
       def 			snake_case__    (							self					:     Any			):
           return functools.reduce(operator.mul			,			self.conv_stride			,			1			)



