

"""simple docstring"""





import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class       _lowerCAmelCase						(			lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      torch.FloatTensor
 __UpperCAmelCase			:      torch.FloatTensor







def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : Tuple=0.999,    UpperCamelCase_  : Optional[Any]="cosine",    )			->		int:
   '''simple docstring'''


   if alpha_transform_type == "cosine":

      def alpha_bar_fn(UpperCamelCase_  : Optional[Any]):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(UpperCamelCase_  : List[Any]):
         return math.exp(t * -12.0)

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

   __lowercase			=		[]
   for i in range(UpperCamelCase_):
      __lowercase			=		i / num_diffusion_timesteps
      __lowercase			=		(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(UpperCamelCase_) / alpha_bar_fn(UpperCamelCase_),    UpperCamelCase_))
   return torch.tensor(UpperCamelCase_,    dtype=torch.floataa)




class       _lowerCAmelCase						(			lowercase  ,lowercase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Union[str, Any]     	=			1


 @register_to_config
 def __init__(    self							:    Optional[Any],      UpperCAmelCase__							:    int = 1_0_0_0,      UpperCAmelCase__							:    float = 0.0_001,      UpperCAmelCase__							:    float = 0.02,      UpperCAmelCase__							:    str = "linear",      UpperCAmelCase__							:    float = 0.0,      UpperCAmelCase__							:    Optional[np.ndarray] = None,      UpperCAmelCase__							:    bool = True,      ):
    if trained_betas is not None:
       __lowercase			=		torch.from_numpy(UpperCAmelCase__			)
    elif beta_schedule == "linear":
       __lowercase			=		torch.linspace(UpperCAmelCase__,      UpperCAmelCase__,      UpperCAmelCase__,      dtype=torch.floataa			)
    elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       __lowercase			=		(
           torch.linspace(beta_start**0.5,      beta_end**0.5,      UpperCAmelCase__,      dtype=torch.floataa			) ** 2
       )
    elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       __lowercase			=		betas_for_alpha_bar(UpperCAmelCase__			)
    elif beta_schedule == "sigmoid":
       # GeoDiff sigmoid schedule
       __lowercase			=		torch.linspace(-6,      6,      UpperCAmelCase__			)
       __lowercase			=		torch.sigmoid(UpperCAmelCase__			) * (beta_end - beta_start) + beta_start
    else:
       raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""			)

    __lowercase			=		1.0 - self.betas
    __lowercase			=		torch.cumprod(self.alphas,      dim=0			)
    __lowercase			=		torch.tensor(1.0			)

    __lowercase			=		torch.tensor(1.0			)

    # standard deviation of the initial noise distribution
    __lowercase			=		1.0

    # setable values
    __lowercase			=		None
    __lowercase			=		torch.from_numpy(np.arange(0,      UpperCAmelCase__			)[::-1].copy()			)

    __lowercase			=		eta


 def       _lowercase				(    self							:    Optional[Any],      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    Optional[int] = None			):
    return sample


 def       _lowercase				(    self							:    Optional[int],      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    int = 1_0,      UpperCAmelCase__							:    int = 1_0,      UpperCAmelCase__							:    Union[str, torch.device] = None,      ):
    __lowercase			=		min(self.config.num_train_timesteps,      UpperCAmelCase__			)
    __lowercase			=		num_inference_steps

    __lowercase			=		[]

    __lowercase			=		{}
    for j in range(0,      num_inference_steps - jump_length,      UpperCAmelCase__			):
       __lowercase			=		jump_n_sample - 1

    __lowercase			=		num_inference_steps
    while t >= 1:
       __lowercase			=		t - 1
       timesteps.append(UpperCAmelCase__			)

       if jumps.get(UpperCAmelCase__,      0			) > 0:
          __lowercase			=		jumps[t] - 1
          for _ in range(UpperCAmelCase__			):
             __lowercase			=		t + 1
             timesteps.append(UpperCAmelCase__			)

    __lowercase			=		np.array(UpperCAmelCase__			) * (self.config.num_train_timesteps // self.num_inference_steps)
    __lowercase			=		torch.from_numpy(UpperCAmelCase__			).to(UpperCAmelCase__			)


 def       _lowercase				(    self							:    int,      UpperCAmelCase__							:    List[str]			):
    __lowercase			=		t - self.config.num_train_timesteps // self.num_inference_steps

    __lowercase			=		self.alphas_cumprod[t]
    __lowercase			=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
    __lowercase			=		1 - alpha_prod_t
    __lowercase			=		1 - alpha_prod_t_prev

    # For t > 0, compute predicted variance βt (see formula (6) and (7) from
    # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
    # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
    # variance to pred_sample
    # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
    # without eta.
    # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
    __lowercase			=		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

    return variance


 def       _lowercase				(    self							:    Optional[Any],      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    Optional[torch.Generator] = None,      UpperCAmelCase__							:    bool = True,      ):
    __lowercase			=		timestep
    __lowercase			=		timestep - self.config.num_train_timesteps // self.num_inference_steps

    # 1. compute alphas, betas
    __lowercase			=		self.alphas_cumprod[t]
    __lowercase			=		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
    __lowercase			=		1 - alpha_prod_t

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    __lowercase			=		(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

    # 3. Clip "predicted x_0"
    if self.config.clip_sample:
       __lowercase			=		torch.clamp(UpperCAmelCase__,      -1,      1			)

    # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
    # substitute formula (7) in the algorithm coming from DDPM paper
    # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
    # DDIM schedule gives the same results as DDPM with eta = 1.0
    # Noise is being reused in 7. and 8., but no impact on quality has
    # been observed.

    # 5. Add noise
    __lowercase			=		model_output.device
    __lowercase			=		randn_tensor(model_output.shape,      generator=UpperCAmelCase__,      device=UpperCAmelCase__,      dtype=model_output.dtype			)
    __lowercase			=		self.eta * self._get_variance(UpperCAmelCase__			) ** 0.5

    __lowercase			=		0
    if t > 0 and self.eta > 0:
       __lowercase			=		std_dev_t * noise

    # 6. compute "direction pointing to x_t" of formula (12)
    # from https://arxiv.org/pdf/2010.02502.pdf
    __lowercase			=		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

    # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
    __lowercase			=		alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

    # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
    __lowercase			=		(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

    # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
    __lowercase			=		mask * prev_known_part + (1.0 - mask) * prev_unknown_part

    if not return_dict:
       return (
           pred_prev_sample,
           pred_original_sample,
       )

    return RePaintSchedulerOutput(prev_sample=UpperCAmelCase__,      pred_original_sample=UpperCAmelCase__			)


 def       _lowercase				(    self							:    List[Any],      UpperCAmelCase__							:    List[str],      UpperCAmelCase__							:    List[str],      UpperCAmelCase__							:    Tuple=None			):
    __lowercase			=		self.config.num_train_timesteps // self.num_inference_steps

    for i in range(UpperCAmelCase__			):
       __lowercase			=		self.betas[timestep + i]
       if sample.device.type == "mps":
          # randn does not work reproducibly on mps
          __lowercase			=		randn_tensor(sample.shape,      dtype=sample.dtype,      generator=UpperCAmelCase__			)
          __lowercase			=		noise.to(sample.device			)
       else:
          __lowercase			=		randn_tensor(sample.shape,      generator=UpperCAmelCase__,      device=sample.device,      dtype=sample.dtype			)

       # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
       __lowercase			=		(1 - beta) ** 0.5 * sample + beta**0.5 * noise

    return sample


 def       _lowercase				(    self							:    Optional[Any],      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    torch.FloatTensor,      UpperCAmelCase__							:    torch.IntTensor,      ):
    raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."			)



 def __len__(    self							:    List[str]			):
    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)




