

"""simple docstring"""





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


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




@dataclass
class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      torch.FloatTensor







class       _lowerCAmelCase						(			lowercase  ,lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      List[str]     	=			1


 @register_to_config
 def __init__(    self							:    Union[str, Any],      UpperCAmelCase__							:    int = 4_0,      UpperCAmelCase__							:    float = 0.002,      UpperCAmelCase__							:    float = 80.0,      UpperCAmelCase__							:    float = 0.5,      UpperCAmelCase__							:    float = 1.0,      UpperCAmelCase__							:    float = 7.0,      UpperCAmelCase__							:    bool = True,      ):
    # standard deviation of the initial noise distribution
    __lowercase			=		sigma_max

    __lowercase			=		np.linspace(0,      1,      UpperCAmelCase__			)
    __lowercase			=		self._convert_to_karras(UpperCAmelCase__			)
    __lowercase			=		self.sigma_to_t(UpperCAmelCase__			)

    # setable values
    __lowercase			=		None
    __lowercase			=		torch.from_numpy(UpperCAmelCase__			)
    __lowercase			=		torch.from_numpy(UpperCAmelCase__			)
    __lowercase			=		False
    __lowercase			=		False


 def       _lowercase				(    self							:    List[Any],      UpperCAmelCase__							:    Dict,      UpperCAmelCase__							:    Any=None			):
    if schedule_timesteps is None:
       __lowercase			=		self.timesteps

    __lowercase			=		(schedule_timesteps == timestep).nonzero()
    return indices.item()


 def       _lowercase				(    self							:    List[str],      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    Union[float, torch.FloatTensor]			):
    # Get sigma corresponding to timestep
    if isinstance(UpperCAmelCase__,      torch.Tensor			):
       __lowercase			=		timestep.to(self.timesteps.device			)
    __lowercase			=		self.index_for_timestep(UpperCAmelCase__			)
    __lowercase			=		self.sigmas[step_idx]

    __lowercase			=		sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

    __lowercase			=		True
    return sample


 def       _lowercase				(    self							:    Optional[int],      UpperCAmelCase__							:    Union[float, np.ndarray]			):
    if not isinstance(UpperCAmelCase__,      np.ndarray			):
       __lowercase			=		np.array(UpperCAmelCase__,      dtype=np.floataa			)

    __lowercase			=		1_0_0_0 * 0.25 * np.log(sigmas + 1E-44			)

    return timesteps


 def       _lowercase				(    self							:    int,      UpperCAmelCase__							:    Optional[int] = None,      UpperCAmelCase__							:    Union[str, torch.device] = None,      UpperCAmelCase__							:    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(UpperCAmelCase__			)			):
          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}."""			)

       __lowercase			=		np.array(UpperCAmelCase__,      dtype=np.intaa			)
       __lowercase			=		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."""			)

       __lowercase			=		num_inference_steps

       __lowercase			=		self.config.num_train_timesteps // self.num_inference_steps
       __lowercase			=		(np.arange(0,      UpperCAmelCase__			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
       __lowercase			=		False

    # Map timesteps to Karras sigmas directly for multistep sampling
    # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
    __lowercase			=		self.config.num_train_timesteps
    __lowercase			=		timesteps[::-1].copy()
    __lowercase			=		ramp / (num_train_timesteps - 1)
    __lowercase			=		self._convert_to_karras(UpperCAmelCase__			)
    __lowercase			=		self.sigma_to_t(UpperCAmelCase__			)

    __lowercase			=		np.concatenate([sigmas, [self.sigma_min]]			).astype(np.floataa			)
    __lowercase			=		torch.from_numpy(UpperCAmelCase__			).to(device=UpperCAmelCase__			)

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


 def       _lowercase				(    self							:    str,      UpperCAmelCase__							:    int			):
    __lowercase			=		self.config.sigma_min
    __lowercase			=		self.config.sigma_max

    __lowercase			=		self.config.rho
    __lowercase			=		sigma_min ** (1 / rho)
    __lowercase			=		sigma_max ** (1 / rho)
    __lowercase			=		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas


 def       _lowercase				(    self							:    int,      UpperCAmelCase__							:    str			):
    __lowercase			=		self.config.sigma_data

    __lowercase			=		sigma_data**2 / (sigma**2 + sigma_data**2)
    __lowercase			=		sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
    return c_skip, c_out


 def       _lowercase				(    self							:    Tuple,      UpperCAmelCase__							:    Tuple			):
    __lowercase			=		self.config.sigma_min
    __lowercase			=		self.config.sigma_data

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


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

    if (
        isinstance(UpperCAmelCase__,      UpperCAmelCase__			)
        or isinstance(UpperCAmelCase__,      torch.IntTensor			)
        or isinstance(UpperCAmelCase__,      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(UpperCAmelCase__,      torch.Tensor			):
       __lowercase			=		timestep.to(self.timesteps.device			)

    __lowercase			=		self.config.sigma_min
    __lowercase			=		self.config.sigma_max

    __lowercase			=		self.index_for_timestep(UpperCAmelCase__			)

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

    # Get scalings for boundary conditions
    __lowercase	,__lowercase			=		self.get_scalings_for_boundary_condition(UpperCAmelCase__			)

    # 1. Denoise model output using boundary conditions
    __lowercase			=		c_out * model_output + c_skip * sample
    if self.config.clip_denoised:
       __lowercase			=		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:
       __lowercase			=		randn_tensor(
           model_output.shape,      dtype=model_output.dtype,      device=model_output.device,      generator=UpperCAmelCase__			)
    else:
       __lowercase			=		torch.zeros_like(UpperCAmelCase__			)
    __lowercase			=		noise * self.config.s_noise

    __lowercase			=		sigma_next.clamp(min=UpperCAmelCase__,      max=UpperCAmelCase__			)

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

    if not return_dict:
       return (prev_sample,)

    return CMStochasticIterativeSchedulerOutput(prev_sample=UpperCAmelCase__			)


 def       _lowercase				(    self							:    Dict,      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    torch.FloatTensor,      ):
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    __lowercase			=		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
       __lowercase			=		self.timesteps.to(original_samples.device,      dtype=torch.floataa			)
       __lowercase			=		timesteps.to(original_samples.device,      dtype=torch.floataa			)
    else:
       __lowercase			=		self.timesteps.to(original_samples.device			)
       __lowercase			=		timesteps.to(original_samples.device			)

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

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

    __lowercase			=		original_samples + noise * sigma
    return noisy_samples



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



"""simple docstring"""





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_a				   =							logging.get_logger(__name__)

_a				   =							['UtteranceLevel']




@torch.no_grad()
def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : int,    UpperCamelCase_  : Optional[int],    UpperCamelCase_  : Union[str, Any])			->		Any:
   '''simple docstring'''


   __lowercase			=		torch.load(UpperCamelCase_,    map_location="cpu")
   if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
      raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}""")

   __lowercase			=		checkpoint["Downstream"]

   __lowercase			=		HubertConfig.from_pretrained(UpperCamelCase_)
   __lowercase			=		HubertForSequenceClassification.from_pretrained(UpperCamelCase_,    config=UpperCamelCase_)
   __lowercase			=		WavaVecaFeatureExtractor.from_pretrained(
       UpperCamelCase_,    return_attention_mask=UpperCamelCase_,    do_normalize=UpperCamelCase_)

   if hf_congfig.use_weighted_layer_sum:
      __lowercase			=		checkpoint["Featurizer"]["weights"]

   __lowercase			=		downstream_dict["projector.weight"]
   __lowercase			=		downstream_dict["projector.bias"]
   __lowercase			=		downstream_dict["model.post_net.linear.weight"]
   __lowercase			=		downstream_dict["model.post_net.linear.bias"]

   hf_feature_extractor.save_pretrained(UpperCamelCase_)
   hf_model.save_pretrained(UpperCamelCase_)


if __name__ == "__main__":
   _a				   =							argparse.ArgumentParser()
   parser.add_argument(
       '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
   )
   parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
   parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
   _a				   =							parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




