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 SchedulerMixin


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







@dataclass
class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	torch.FloatTensor







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase   ,							__UpperCamelCase    ):
       A							:	List[str]          =	1

       @register_to_config
       def __init__(							self					:     List[Any]			,			_lowerCAmelCase					:     int = 40			,			_lowerCAmelCase					:     float = 0.002			,			_lowerCAmelCase					:     float = 80.0			,			_lowerCAmelCase					:     float = 0.5			,			_lowerCAmelCase					:     float = 1.0			,			_lowerCAmelCase					:     float = 7.0			,			_lowerCAmelCase					:     bool = True			,			):
           # standard deviation of the initial noise distribution
           __snake_case       :				Optional[int] 			=    sigma_max

           __snake_case       :				int 			=    np.linspace(0			,			1			,			_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    self._convert_to_karras(_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    self.sigma_to_t(_lowerCAmelCase			)

           # setable values
           __snake_case       :				List[Any] 			=    None
           __snake_case       :				Union[str, Any] 			=    torch.from_numpy(_lowerCAmelCase			)
           __snake_case       :				Union[str, Any] 			=    torch.from_numpy(_lowerCAmelCase			)
           __snake_case       :				Optional[int] 			=    False
           __snake_case       :				Dict 			=    False

       def 			snake_case__    (							self					:     Any			,			_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     Optional[Any]=None			):
           if schedule_timesteps is None:
               __snake_case       :				Any 			=    self.timesteps

           __snake_case       :				int 			=    (schedule_timesteps == timestep).nonzero()
           return indices.item()

       def 			snake_case__    (							self					:     Optional[int]			,			_lowerCAmelCase					:     torch.FloatTensor			,			_lowerCAmelCase					:     Union[float, torch.FloatTensor]			):
           # Get sigma corresponding to timestep
           if isinstance(_lowerCAmelCase			,			torch.Tensor			):
               __snake_case       :				Optional[int] 			=    timestep.to(self.timesteps.device			)
           __snake_case       :				List[str] 			=    self.index_for_timestep(_lowerCAmelCase			)
           __snake_case       :				List[str] 			=    self.sigmas[step_idx]

           __snake_case       :				int 			=    sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

           __snake_case       :				Optional[Any] 			=    True
           return sample

       def 			snake_case__    (							self					:     Optional[Any]			,			_lowerCAmelCase					:     Union[float, np.ndarray]			):
           if not isinstance(_lowerCAmelCase			,			np.ndarray			):
               __snake_case       :				List[Any] 			=    np.array(_lowerCAmelCase			,			dtype=np.floataa			)

           __snake_case       :				Any 			=    10_00 * 0.25 * np.log(sigmas + 1e-44			)

           return timesteps

       def 			snake_case__    (							self					:     Tuple			,			_lowerCAmelCase					:     Optional[int] = None			,			_lowerCAmelCase					:     Union[str, torch.device] = None			,			_lowerCAmelCase					:     Optional[List[int]] = None			,			):
           if num_inference_steps is None and timesteps is None:
               raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""			)

           if num_inference_steps is not None and timesteps is not None:
               raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""			)

           # Follow DDPMScheduler custom timesteps logic
           if timesteps is not None:
               for i in range(1			,			len(_lowerCAmelCase			)			):
                   if timesteps[i] >= timesteps[i - 1]:
                       raise ValueError("""`timesteps` must be in descending order."""			)

               if timesteps[0] >= self.config.num_train_timesteps:
                   raise ValueError(
                       f'''`timesteps` must start before `self.config.train_timesteps`:'''
                       f''' {self.config.num_train_timesteps}.'''			)

               __snake_case       :				Dict 			=    np.array(_lowerCAmelCase			,			dtype=np.intaa			)
               __snake_case       :				Dict 			=    True
           else:
               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.'''			)

               __snake_case       :				Dict 			=    num_inference_steps

               __snake_case       :				Optional[int] 			=    self.config.num_train_timesteps // self.num_inference_steps
               __snake_case       :				Tuple 			=    (np.arange(0			,			_lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
               __snake_case       :				Optional[int] 			=    False

           # Map timesteps to Karras sigmas directly for multistep sampling
           # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
           __snake_case       :				Tuple 			=    self.config.num_train_timesteps
           __snake_case       :				Tuple 			=    timesteps[::-1].copy()
           __snake_case       :				List[str] 			=    ramp / (num_train_timesteps - 1)
           __snake_case       :				Optional[Any] 			=    self._convert_to_karras(_lowerCAmelCase			)
           __snake_case       :				int 			=    self.sigma_to_t(_lowerCAmelCase			)

           __snake_case       :				Dict 			=    np.concatenate([sigmas, [self.sigma_min]]			).astype(np.floataa			)
           __snake_case       :				str 			=    torch.from_numpy(_lowerCAmelCase			).to(device=_lowerCAmelCase			)

           if str(_lowerCAmelCase			).startswith("""mps"""			):
               # mps does not support float64
               __snake_case       :				Optional[int] 			=    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					:     List[Any]			,			_lowerCAmelCase					:     Union[str, Any]			):
           __snake_case       :				float 			=    self.config.sigma_min
           __snake_case       :				float 			=    self.config.sigma_max

           __snake_case       :				List[Any] 			=    self.config.rho
           __snake_case       :				Dict 			=    sigma_min ** (1 / rho)
           __snake_case       :				Union[str, Any] 			=    sigma_max ** (1 / rho)
           __snake_case       :				int 			=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas

       def 			snake_case__    (							self					:     int			,			_lowerCAmelCase					:     Any			):
           __snake_case       :				List[Any] 			=    self.config.sigma_data

           __snake_case       :				str 			=    sigma_data**2 / (sigma**2 + sigma_data**2)
           __snake_case       :				Optional[Any] 			=    sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out

       def 			snake_case__    (							self					:     Any			,			_lowerCAmelCase					:     Any			):
           __snake_case       :				int 			=    self.config.sigma_min
           __snake_case       :				Dict 			=    self.config.sigma_data

           __snake_case       :				Any 			=    sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
           __snake_case       :				Any 			=    (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out

       def 			snake_case__    (							self					:     List[str]			,			_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"""
                       f''' `{self.__class__}.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       :				Any 			=    timestep.to(self.timesteps.device			)

           __snake_case       :				Any 			=    self.config.sigma_min
           __snake_case       :				Optional[Any] 			=    self.config.sigma_max

           __snake_case       :				Tuple 			=    self.index_for_timestep(_lowerCAmelCase			)

           # sigma_next corresponds to next_t in original implementation
           __snake_case       :				List[str] 			=    self.sigmas[step_index]
           if step_index + 1 < self.config.num_train_timesteps:
               __snake_case       :				str 			=    self.sigmas[step_index + 1]
           else:
               # Set sigma_next to sigma_min
               __snake_case       :				List[str] 			=    self.sigmas[-1]

           # Get scalings for boundary conditions
           __snake_case			, __snake_case       :				Optional[Any] 			=    self.get_scalings_for_boundary_condition(_lowerCAmelCase			)

           # 1. Denoise model output using boundary conditions
           __snake_case       :				Dict 			=    c_out * model_output + c_skip * sample
           if self.config.clip_denoised:
               __snake_case       :				int 			=    denoised.clamp(-1			,			1			)

           # 2. Sample z ~ N(0, s_noise^2 * I)
           # Noise is not used for onestep sampling.
           if len(self.timesteps			) > 1:
               __snake_case       :				Tuple 			=    randn_tensor(
                   model_output.shape			,			dtype=model_output.dtype			,			device=model_output.device			,			generator=_lowerCAmelCase			)
           else:
               __snake_case       :				Dict 			=    torch.zeros_like(_lowerCAmelCase			)
           __snake_case       :				Optional[int] 			=    noise * self.config.s_noise

           __snake_case       :				Optional[int] 			=    sigma_next.clamp(min=_lowerCAmelCase			,			max=_lowerCAmelCase			)

           # 3. Return noisy sample
           # tau = sigma_hat, eps = sigma_min
           __snake_case       :				Tuple 			=    denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

           if not return_dict:
               return (prev_sample,)

           return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     str			,			_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       :				List[str] 			=    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       :				Tuple 			=    self.timesteps.to(original_samples.device			,			dtype=torch.floataa			)
               __snake_case       :				Optional[int] 			=    timesteps.to(original_samples.device			,			dtype=torch.floataa			)
           else:
               __snake_case       :				List[Any] 			=    self.timesteps.to(original_samples.device			)
               __snake_case       :				Tuple 			=    timesteps.to(original_samples.device			)

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

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

           __snake_case       :				Union[str, Any] 			=    original_samples + noise * sigma
           return noisy_samples





       def __len__(							self					:     Dict			):
           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			)



