

"""simple docstring"""






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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer






@dataclass
class      _UpperCAmelCase      (		__a):
			__a			:							torch.FloatTensor






class      _UpperCAmelCase      (		__a     ,  __a):
			@register_to_config
			def __init__(		self   ,		_A = 3   ,		_A = 3   ,		_A = ("DownEncoderBlock2D",)   ,		_A = ("UpDecoderBlock2D",)   ,		_A = (64,)   ,		_A = 1   ,		_A = "silu"   ,		_A = 3   ,		_A = 32   ,		_A = 2_56   ,		_A = 32   ,		_A = None   ,		_A = 0.18215   ,		_A = "group"   ,		)     ->     List[Any]:






				'''simple docstring'''







				super().__init__()

				# pass init params to Encoder
				_UpperCAmelCase     :				int          =       Encoder(
				    in_channels=_A   ,		out_channels=_A   ,		down_block_types=_A   ,		block_out_channels=_A   ,		layers_per_block=_A   ,		act_fn=_A   ,		norm_num_groups=_A   ,		double_z=_A   ,		)

				_UpperCAmelCase     :				Optional[Any]          =       vq_embed_dim if vq_embed_dim is not None else latent_channels

				_UpperCAmelCase     :				List[Any]          =       nn.Convad(_A   ,		_A   ,		1  )
				_UpperCAmelCase     :				List[Any]          =       VectorQuantizer(_A   ,		_A   ,		beta=0.25   ,		remap=_A   ,		sane_index_shape=_A  )
				_UpperCAmelCase     :				Optional[Any]          =       nn.Convad(_A   ,		_A   ,		1  )

				# pass init params to Decoder
				_UpperCAmelCase     :				Union[str, Any]          =       Decoder(
				    in_channels=_A   ,		out_channels=_A   ,		up_block_types=_A   ,		block_out_channels=_A   ,		layers_per_block=_A   ,		act_fn=_A   ,		norm_num_groups=_A   ,		norm_type=_A   ,		)
			@apply_forward_hook
			def 				__snake_case			(		self   ,		_A   ,		_A = True  )     ->     Tuple:






				'''simple docstring'''







				_UpperCAmelCase     :				Union[str, Any]          =       self.encoder(_A  )
				_UpperCAmelCase     :				List[str]          =       self.quant_conv(_A  )

				if not return_dict:
					return (h,)

				return VQEncoderOutput(latents=_A  )
			@apply_forward_hook
			def 				__snake_case			(		self   ,		_A   ,		_A = False   ,		_A = True  )     ->     Optional[Any]:






				'''simple docstring'''







				if not force_not_quantize:
					_UpperCAmelCase     :				int          =       self.quantize(_A  )
				else:
					_UpperCAmelCase     :				Union[str, Any]          =       h
				_UpperCAmelCase     :				Optional[Any]          =       self.post_quant_conv(_A  )
				_UpperCAmelCase     :				Any          =       self.decoder(_A   ,		quant if self.config.norm_type == """spatial""" else None  )

				if not return_dict:
					return (dec,)

				return DecoderOutput(sample=_A  )
			def 				__snake_case			(		self   ,		_A   ,		_A = True  )     ->     Tuple:






				'''simple docstring'''







				_UpperCAmelCase     :				Optional[Any]          =       sample
				_UpperCAmelCase     :				Union[str, Any]          =       self.encode(_A  ).latents
				_UpperCAmelCase     :				Optional[int]          =       self.decode(_A  ).sample

				if not return_dict:
					return (dec,)

				return DecoderOutput(sample=_A  )













'''simple docstring'''
import argparse

import torch

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


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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  downstream_dict["projector.weight"]
   lowercase_						:     str							     =  downstream_dict["projector.bias"]
   lowercase_						:     int							     =  downstream_dict["model.post_net.linear.weight"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["model.post_net.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Any							     =  downstream_dict["model.linear.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["model.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




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

   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   lowercase_						:     List[Any]							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["objective.W"]
   return model




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  torch.load(_UpperCamelCase    ,		map_location="cpu"				)

   lowercase_						:     Dict							     =  checkpoint["Downstream"]

   lowercase_						:     Optional[Any]							     =  WavaVecaConfig.from_pretrained(_UpperCamelCase				)
   lowercase_						:     Dict							     =  WavaVecaFeatureExtractor.from_pretrained(
       _UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		do_normalize=_UpperCamelCase				)

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

   if hf_config.use_weighted_layer_sum:
      lowercase_						:     List[str]							     =  checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(_UpperCamelCase				)
   hf_model.save_pretrained(_UpperCamelCase				)


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




