



"""simple docstring"""

import argparse

import torch

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


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





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: Union[str, Any]					):
							__a		:		int									=       WavaVecaForSequenceClassification.from_pretrained(_lowerCamelCase  ,       config=_lowerCamelCase					)
							__a		:		Optional[Any]									=       downstream_dict["""projector.weight"""]
							__a		:		Dict									=       downstream_dict["""projector.bias"""]
							__a		:		str									=       downstream_dict["""model.post_net.linear.weight"""]
							__a		:		Optional[int]									=       downstream_dict["""model.post_net.linear.bias"""]
							return model





def       __magic_name__    (   _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: List[str]					):
							__a		:		Optional[int]									=       WavaVecaForAudioFrameClassification.from_pretrained(_lowerCamelCase  ,       config=_lowerCamelCase					)
							__a		:		Union[str, Any]									=       downstream_dict["""model.linear.weight"""]
							__a		:		int									=       downstream_dict["""model.linear.bias"""]
							return model





def       __magic_name__    (   _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: Union[str, Any]					):
							__a		:		List[Any]									=       WavaVecaForXVector.from_pretrained(_lowerCamelCase  ,       config=_lowerCamelCase					)
							__a		:		Dict									=       downstream_dict["""connector.weight"""]
							__a		:		int									=       downstream_dict["""connector.bias"""]
							for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
														__a		:		List[Any]									=       downstream_dict[
														    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
														]
														__a		:		List[Any]									=       downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

							__a		:		Union[str, Any]									=       downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
							__a		:		List[str]									=       downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
							__a		:		List[Any]									=       downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
							__a		:		Tuple									=       downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
							__a		:		Tuple									=       downstream_dict["""objective.W"""]
							return model





@torch.no_grad()
def       __magic_name__    (   _lowerCamelCase				: Tuple  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: Optional[int]					):
							__a		:		int									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)

							__a		:		Dict									=       checkpoint["""Downstream"""]

							__a		:		List[Any]									=       WavaVecaConfig.from_pretrained(_lowerCamelCase					)
							__a		:		Optional[Any]									=       WavaVecaFeatureExtractor.from_pretrained(
							    _lowerCamelCase  ,       return_attention_mask=_lowerCamelCase  ,       do_normalize=_lowerCamelCase					)

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

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

							hf_feature_extractor.save_pretrained(_lowerCamelCase					)
							hf_model.save_pretrained(_lowerCamelCase					)


if __name__ == "__main__":
	lowercase__			   =  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.")
	lowercase__			   =  parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






"""simple docstring"""

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 SCREAMING_SNAKE_CASE__					(		__snake_case      ):



						def __init__(self						,     _lowercase						,     _lowercase=None						,     _lowercase=None						,     _lowercase=0							):





													'''simple docstring'''

													__a		:		Any									=       1.0 if scale is None else scale
													__a		:		str									=       0.0 if loc is None else loc

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



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.base_dist.mean * self.scale + self.loc



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.base_dist.variance * self.scale**2





						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.variance.sqrt()







class SCREAMING_SNAKE_CASE__					(		nn.Module      ):



						def __init__(self						,     _lowercase						,     _lowercase						,     _lowercase						,     **_lowercase							):





													'''simple docstring'''

													super().__init__(**_lowercase							)
													__a		:		str									=       args_dim
													__a		:		List[Any]									=       nn.ModuleList([nn.Linear(_lowercase						,     _lowercase							) for dim in args_dim.values()]							)
													__a		:		Dict									=       domain_map





						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a		:		List[Any]									=       [proj(_lowercase							) for proj in self.proj]

													return self.domain_map(*_lowercase							)







class SCREAMING_SNAKE_CASE__					(		nn.Module      ):



						def __init__(self						,     _lowercase							):





													'''simple docstring'''

													super().__init__()
													__a		:		Optional[int]									=       function





						def     lowerCAmelCase__(self						,     _lowercase						,     *_lowercase							):





													'''simple docstring'''

													return self.function(_lowercase						,     *_lowercase							)







class SCREAMING_SNAKE_CASE__					:
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42
						_lowerCAmelCase							=					42



						def __init__(self						,     _lowercase = 1							):





													'''simple docstring'''

													__a		:		Optional[int]									=       dim
													__a		:		str									=       {k: dim * self.args_dim[k] for k in self.args_dim}



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													if self.dim == 1:
																				return self.distribution_class(*_lowercase							)
													else:
																				return Independent(self.distribution_class(*_lowercase							)						,     1							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None						,     _lowercase = None						,     ):





													'''simple docstring'''

													__a		:		Tuple									=       self._base_distribution(_lowercase							)
													if loc is None and scale is None:
																				return distr
													else:
																				return AffineTransformed(_lowercase						,     loc=_lowercase						,     scale=_lowercase						,     event_dim=self.event_dim							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return () if self.dim == 1 else (self.dim,)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return len(self.event_shape							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 0.0



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													return ParameterProjection(
													    in_features=_lowercase						,     args_dim=self.args_dim						,     domain_map=LambdaLayer(self.domain_map							)						,     )



						def     lowerCAmelCase__(self						,     *_lowercase							):





													'''simple docstring'''

													raise NotImplementedError()





						@staticmethod
						def     lowerCAmelCase__(_lowercase							):





													'''simple docstring'''

													return (x + torch.sqrt(torch.square(_lowercase							) + 4.0							)) / 2.0







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					{"df": 1, "loc": 1, "scale": 1}
						_lowerCAmelCase							=					StudentT



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		int									=       cls.squareplus(_lowercase							).clamp_min(torch.finfo(scale.dtype							).eps							)
													__a		:		Optional[Any]									=       2.0 + cls.squareplus(_lowercase							)
													return df.squeeze(-1							), loc.squeeze(-1							), scale.squeeze(-1							)







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					{"loc": 1, "scale": 1}
						_lowerCAmelCase							=					Normal



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		str									=       cls.squareplus(_lowercase							).clamp_min(torch.finfo(scale.dtype							).eps							)
													return loc.squeeze(-1							), scale.squeeze(-1							)







class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					{"total_count": 1, "logits": 1}
						_lowerCAmelCase							=					NegativeBinomial



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       cls.squareplus(_lowercase							)
													return total_count.squeeze(-1							), logits.squeeze(-1							)



						def     lowerCAmelCase__(self						,     _lowercase							):





													'''simple docstring'''

													__a							,     __a		:		Optional[Any]									=       distr_args
													if self.dim == 1:
																				return self.distribution_class(total_count=_lowercase						,     logits=_lowercase							)
													else:
																				return Independent(self.distribution_class(total_count=_lowercase						,     logits=_lowercase							)						,     1							)





						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None						,     _lowercase = None							):





													'''simple docstring'''

													__a							,     __a		:		List[Any]									=       distr_args

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

													return self._base_distribution((total_count, logits)							)

