


'''simple docstring'''




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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





def   _a		(lowercase__			:   int							,   lowercase__			:   Optional[int]							,   lowercase__			:   Optional[Any]						)			->			Dict:






     """simple docstring"""
     __snake_case           =						UniSpeechSatForSequenceClassification.from_pretrained(lowercase__							,   config=lowercase__						)
     __snake_case           =						downstream_dict['projector.weight']
     __snake_case           =						downstream_dict['projector.bias']
     __snake_case           =						downstream_dict['model.post_net.linear.weight']
     __snake_case           =						downstream_dict['model.post_net.linear.bias']
     return model





def   _a		(lowercase__			:   Dict							,   lowercase__			:   int							,   lowercase__			:   List[Any]						)			->			str:






     """simple docstring"""
     __snake_case           =						UniSpeechSatForAudioFrameClassification.from_pretrained(lowercase__							,   config=lowercase__						)
     __snake_case           =						downstream_dict['model.linear.weight']
     __snake_case           =						downstream_dict['model.linear.bias']
     return model





def   _a		(lowercase__			:   List[Any]							,   lowercase__			:   List[Any]							,   lowercase__			:   Union[str, Any]						)			->			Any:






     """simple docstring"""
     __snake_case           =						UniSpeechSatForXVector.from_pretrained(lowercase__							,   config=lowercase__						)
     __snake_case           =						downstream_dict['connector.weight']
     __snake_case           =						downstream_dict['connector.bias']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
          __snake_case           =						downstream_dict[
              f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
          ]
          __snake_case           =						downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

     __snake_case           =						downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
     __snake_case           =						downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
     __snake_case           =						downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
     __snake_case           =						downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
     __snake_case           =						downstream_dict['objective.W']
     return model





@torch.no_grad()
def   _a		(lowercase__			:   Dict							,   lowercase__			:   int							,   lowercase__			:   str							,   lowercase__			:   int						)			->			str:






     """simple docstring"""
     __snake_case           =						torch.load(lowercase__							,   map_location='cpu'						)

     __snake_case           =						checkpoint['Downstream']

     __snake_case           =						UniSpeechSatConfig.from_pretrained(lowercase__						)
     __snake_case           =						WavaVecaFeatureExtractor.from_pretrained(
         lowercase__							,   return_attention_mask=lowercase__							,   do_normalize=lowercase__						)

     __snake_case           =						hf_config.architectures[0]
     if arch.endswith('ForSequenceClassification'						):
          __snake_case           =						convert_classification(lowercase__							,   lowercase__							,   lowercase__						)
     elif arch.endswith('ForAudioFrameClassification'						):
          __snake_case           =						convert_diarization(lowercase__							,   lowercase__							,   lowercase__						)
     elif arch.endswith('ForXVector'						):
          __snake_case           =						convert_xvector(lowercase__							,   lowercase__							,   lowercase__						)
     else:
          raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'						)

     if hf_config.use_weighted_layer_sum:
          __snake_case           =						checkpoint['Featurizer']['weights']

     hf_feature_extractor.save_pretrained(lowercase__						)
     hf_model.save_pretrained(lowercase__						)


if __name__ == "__main__":
       _a   :   Union[str, 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   :   Optional[int]												=   parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=0						):
						UpperCAmelCase__:	Any									= 1.0 if scale is None else scale
						UpperCAmelCase__:	int									= 0.0 if loc is None else loc

						super().__init__(lowerCamelCase__				,      [AffineTransform(loc=self.loc				,      scale=self.scale				,      event_dim=lowerCamelCase__						)]						)





					@property
					def    _UpperCAmelCase    (				self						):
						return self.base_dist.mean * self.scale + self.loc





					@property
					def    _UpperCAmelCase    (				self						):
						return self.base_dist.variance * self.scale**2





					@property
					def    _UpperCAmelCase    (				self						):
						return self.variance.sqrt()

class __UpperCamelCase     (				nn.Module		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						):
						super().__init__(**lowerCamelCase__						)
						UpperCAmelCase__:	Any									= args_dim
						UpperCAmelCase__:	List[str]									= nn.ModuleList([nn.Linear(lowerCamelCase__				,      lowerCamelCase__						) for dim in args_dim.values()]						)
						UpperCAmelCase__:	Any									= domain_map





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	str									= [proj(lowerCamelCase__						) for proj in self.proj]

						return self.domain_map(*lowerCamelCase__						)

class __UpperCamelCase     (				nn.Module		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__						):
						super().__init__()
						UpperCAmelCase__:	str									= function





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      *lowerCamelCase__						):
						return self.function(lowerCamelCase__				,      *lowerCamelCase__						)

class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   42
					__magic_name__        =   42
					__magic_name__        =   42





					def __init__(				self				,      lowerCamelCase__ = 1						):
						UpperCAmelCase__:	str									= dim
						UpperCAmelCase__:	Any									= {k: dim * self.args_dim[k] for k in self.args_dim}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						if self.dim == 1:
							return self.distribution_class(*lowerCamelCase__						)
						else:
							return Independent(self.distribution_class(*lowerCamelCase__						)				,      1						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      ):
						UpperCAmelCase__:	Optional[Any]									= self._base_distribution(lowerCamelCase__						)
						if loc is None and scale is None:
							return distr
						else:
							return AffineTransformed(lowerCamelCase__				,      loc=lowerCamelCase__				,      scale=lowerCamelCase__				,      event_dim=self.event_dim						)





					@property
					def    _UpperCAmelCase    (				self						):
						return () if self.dim == 1 else (self.dim,)





					@property
					def    _UpperCAmelCase    (				self						):
						return len(self.event_shape						)





					@property
					def    _UpperCAmelCase    (				self						):
						return 0.0





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						return ParameterProjection(
						    in_features=lowerCamelCase__				,      args_dim=self.args_dim				,      domain_map=LambdaLayer(self.domain_map						)				,      )





					def    _UpperCAmelCase    (				self				,      *lowerCamelCase__						):
						raise NotImplementedError()





					@staticmethod
					def    _UpperCAmelCase    (				lowerCamelCase__						):
						return (x + torch.sqrt(torch.square(lowerCamelCase__						) + 4.0						)) / 2.0

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"df": 1, "loc": 1, "scale": 1}
					__magic_name__        =   StudentT





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Dict									= cls.squareplus(lowerCamelCase__						).clamp_min(torch.finfo(scale.dtype						).eps						)
						UpperCAmelCase__:	List[Any]									= 2.0 + cls.squareplus(lowerCamelCase__						)
						return df.squeeze(-1						), loc.squeeze(-1						), scale.squeeze(-1						)

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"loc": 1, "scale": 1}
					__magic_name__        =   Normal





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	str									= cls.squareplus(lowerCamelCase__						).clamp_min(torch.finfo(scale.dtype						).eps						)
						return loc.squeeze(-1						), scale.squeeze(-1						)

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   {"total_count": 1, "logits": 1}
					__magic_name__        =   NegativeBinomial





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Dict									= cls.squareplus(lowerCamelCase__						)
						return total_count.squeeze(-1						), logits.squeeze(-1						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__							,						UpperCAmelCase__:	str									= distr_args
						if self.dim == 1:
							return self.distribution_class(total_count=lowerCamelCase__				,      logits=lowerCamelCase__						)
						else:
							return Independent(self.distribution_class(total_count=lowerCamelCase__				,      logits=lowerCamelCase__						)				,      1						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None						):
						UpperCAmelCase__							,						UpperCAmelCase__:	List[str]									= distr_args

						if scale is not None:
							# See scaling property of Gamma.
							logits += scale.log()

						return self._base_distribution((total_count, logits)						)