


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
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 						_UpperCAmelCase      (     _SCREAMING_SNAKE_CASE							):
		lowerCamelCase_	:			Optional[int]       			=  4_2
		lowerCamelCase_	:			int       			=  None




def     lowercase	(    a     , a=0.999     , a="cosine"     , ):



		'''simple docstring'''




		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(a ):
						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_				:Union[str, Any]				     =						i / num_diffusion_timesteps
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						(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 						_UpperCAmelCase      (     _SCREAMING_SNAKE_CASE		,		_SCREAMING_SNAKE_CASE							):






		@register_to_config
		def __init__(						self				:     str   ,  UpperCAmelCase				:     int = 10_00   ,  UpperCAmelCase				:     str = "fixed_small_log"   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Optional[float] = 1.0   ,  UpperCAmelCase				:     str = "epsilon"   ,  UpperCAmelCase				:     str = "squaredcos_cap_v2"   ,  ):
				if beta_schedule != "squaredcos_cap_v2":
						raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'")

				SCREAMING_SNAKE_CASE_				:Dict				     =						betas_for_alpha_bar(UpperCAmelCase)

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

				# standard deviation of the initial noise distribution
				SCREAMING_SNAKE_CASE_				:int				     =						1.0

				# setable values
				SCREAMING_SNAKE_CASE_				:Any				     =						None
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						torch.from_numpy(np.arange(0   ,  UpperCAmelCase)[::-1].copy())

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						variance_type






		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     torch.FloatTensor   ,  UpperCAmelCase				:     Optional[int] = None):
				return sample






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     Union[str, torch.device] = None):
				SCREAMING_SNAKE_CASE_				:int				     =						num_inference_steps
				SCREAMING_SNAKE_CASE_				:int				     =						(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						(np.arange(0   ,  UpperCAmelCase) * step_ratio).round()[::-1].copy().astype(np.intaa)
				SCREAMING_SNAKE_CASE_				:Dict				     =						torch.from_numpy(UpperCAmelCase).to(UpperCAmelCase)






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     List[str]=None   ,  UpperCAmelCase				:     Union[str, Any]=None   ,  UpperCAmelCase				:     Dict=None):
				if prev_timestep is None:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						t - 1

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						1 - alpha_prod_t
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
						SCREAMING_SNAKE_CASE_				:int				     =						self.betas[t]
				else:
						SCREAMING_SNAKE_CASE_				:Any				     =						1 - alpha_prod_t / 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
				SCREAMING_SNAKE_CASE_				:List[str]				     =						beta_prod_t_prev / beta_prod_t * beta

				if variance_type is None:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						self.config.variance_type

				# hacks - were probably added for training stability
				if variance_type == "fixed_small_log":
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						torch.log(torch.clamp(UpperCAmelCase   ,  min=1E-20))
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						torch.exp(0.5 * variance)
				elif variance_type == "learned_range":
						# NOTE difference with DDPM scheduler
						SCREAMING_SNAKE_CASE_				:str				     =						variance.log()
						SCREAMING_SNAKE_CASE_				:Tuple				     =						beta.log()

						SCREAMING_SNAKE_CASE_				:List[Any]				     =						(predicted_variance + 1) / 2
						SCREAMING_SNAKE_CASE_				:Dict				     =						frac * max_log + (1 - frac) * min_log

				return variance






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     torch.FloatTensor   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     torch.FloatTensor   ,  UpperCAmelCase				:     Optional[int] = None   ,  UpperCAmelCase				:     Any=None   ,  UpperCAmelCase				:     bool = True   ,  ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.split(UpperCAmelCase   ,  sample.shape[1]   ,  dim=1)
				else:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						None

				# 1. compute alphas, betas
				if prev_timestep is None:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						t - 1

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE_				:Any				     =						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						1 - alpha_prod_t
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						self.betas[t]
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.alphas[t]
				else:
						SCREAMING_SNAKE_CASE_				:Dict				     =						1 - alpha_prod_t / alpha_prod_t_prev
						SCREAMING_SNAKE_CASE_				:Dict				     =						1 - beta

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if self.config.prediction_type == "epsilon":
						SCREAMING_SNAKE_CASE_				:str				     =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model_output
				else:
						raise ValueError(
						    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
						    " for the UnCLIPScheduler.")

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						torch.clamp(
						    UpperCAmelCase   ,  -self.config.clip_sample_range   ,  self.config.clip_sample_range)

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				SCREAMING_SNAKE_CASE_				:Any				     =						(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				SCREAMING_SNAKE_CASE_				:int				     =						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				SCREAMING_SNAKE_CASE_				:int				     =						0
				if t > 0:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						randn_tensor(
						    model_output.shape   ,  dtype=model_output.dtype   ,  generator=UpperCAmelCase   ,  device=model_output.device)

						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self._get_variance(
						    UpperCAmelCase   ,  predicted_variance=UpperCAmelCase   ,  prev_timestep=UpperCAmelCase   ,  )

						if self.variance_type == "fixed_small_log":
								SCREAMING_SNAKE_CASE_				:Optional[int]				     =						variance
						elif self.variance_type == "learned_range":
								SCREAMING_SNAKE_CASE_				:Tuple				     =						(0.5 * variance).exp()
						else:
								raise ValueError(
								    F"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
								    " for the UnCLIPScheduler.")

						SCREAMING_SNAKE_CASE_				:str				     =						variance * variance_noise

				SCREAMING_SNAKE_CASE_				:List[str]				     =						pred_prev_sample + variance

				if not return_dict:
						return (pred_prev_sample,)

				return UnCLIPSchedulerOutput(prev_sample=UpperCAmelCase   ,  pred_original_sample=UpperCAmelCase)







		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     torch.FloatTensor   ,  UpperCAmelCase				:     torch.FloatTensor   ,  UpperCAmelCase				:     torch.IntTensor   ,  ):
				# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.alphas_cumprod.to(device=original_samples.device   ,  dtype=original_samples.dtype)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						timesteps.to(original_samples.device)

				SCREAMING_SNAKE_CASE_				:Dict				     =						alphas_cumprod[timesteps] ** 0.5
				SCREAMING_SNAKE_CASE_				:Dict				     =						sqrt_alpha_prod.flatten()
				while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						sqrt_alpha_prod.unsqueeze(-1)

				SCREAMING_SNAKE_CASE_				:str				     =						(1 - alphas_cumprod[timesteps]) ** 0.5
				SCREAMING_SNAKE_CASE_				:Tuple				     =						sqrt_one_minus_alpha_prod.flatten()
				while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						sqrt_one_minus_alpha_prod.unsqueeze(-1)

				SCREAMING_SNAKE_CASE_				:str				     =						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples










'''simple docstring'''






import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__A   :    str											= logging.get_logger(__name__)







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[Any]				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  WavaVecaForSequenceClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : str     =  downstream_dict["""projector.weight"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""projector.bias"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""model.post_net.linear.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.post_net.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Any				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  WavaVecaForAudioFrameClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.linear.weight"""]
  lowerCAmelCase_     : List[str]     =  downstream_dict["""model.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[Any]     =  WavaVecaForXVector.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : List[Any]     =  downstream_dict["""connector.weight"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
    lowerCAmelCase_     : List[str]     =  downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    lowerCAmelCase_     : List[str]     =  downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  lowerCAmelCase_     : int     =  downstream_dict["""objective.W"""]
  return model







@torch.no_grad()
def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  str				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  torch.load(A__				,       map_location="""cpu"""   )

  lowerCAmelCase_     : Optional[int]     =  checkpoint["""Downstream"""]

  lowerCAmelCase_     : int     =  WavaVecaConfig.from_pretrained(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  WavaVecaFeatureExtractor.from_pretrained(
      A__				,       return_attention_mask=A__				,       do_normalize=A__   )

  lowerCAmelCase_     : Optional[int]     =  hf_config.architectures[0]
  if arch.endswith("""ForSequenceClassification"""   ):
    lowerCAmelCase_     : List[str]     =  convert_classification(A__				,       A__				,       A__   )
  elif arch.endswith("""ForAudioFrameClassification"""   ):
    lowerCAmelCase_     : Dict     =  convert_diarization(A__				,       A__				,       A__   )
  elif arch.endswith("""ForXVector"""   ):
    lowerCAmelCase_     : List[Any]     =  convert_xvector(A__				,       A__				,       A__   )
  else:
    raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'   )

  if hf_config.use_weighted_layer_sum:
    lowerCAmelCase_     : Optional[int]     =  checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(A__   )
  hf_model.save_pretrained(A__   )


if __name__ == "__main__":
  __A   :    List[Any]											= 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   :    Tuple											= parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



