



import argparse

import torch

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


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

def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      lowercase__     =  WavaVecaForSequenceClassification.from_pretrained(A__						,  config=A__	)
      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']
      return model

def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      lowercase__     =  WavaVecaForAudioFrameClassification.from_pretrained(A__						,  config=A__	)
      lowercase__     =  downstream_dict['model.linear.weight']
      lowercase__     =  downstream_dict['model.linear.bias']
      return model

def   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      lowercase__     =  WavaVecaForXVector.from_pretrained(A__						,  config=A__	)
      lowercase__     =  downstream_dict['connector.weight']
      lowercase__     =  downstream_dict['connector.bias']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
            lowercase__     =  downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            lowercase__     =  downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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

@torch.no_grad()
def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__	):
      lowercase__     =  torch.load(A__						,  map_location='cpu'	)

      lowercase__     =  checkpoint['Downstream']

      lowercase__     =  WavaVecaConfig.from_pretrained(A__	)
      lowercase__     =  WavaVecaFeatureExtractor.from_pretrained(
          A__						,  return_attention_mask=A__						,  do_normalize=A__	)

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

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

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


if __name__ == "__main__":
       a__      :				Tuple   	=			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)





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a__      :				List[str]   	=			logging.get_logger(__name__)

a__      :				List[Any]   	=			{
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}



class       UpperCAmelCase__(							lowerCamelCase     ,							lowerCamelCase	):

   '''simple docstring'''




   A    :  List[str]       				=    "focalnet"


   def __init__(						self  :			Dict			,				lowerCAmelCase  :			Union[str, Any]=2_24			,				lowerCAmelCase  :			List[str]=4			,				lowerCAmelCase  :			int=3			,				lowerCAmelCase  :			Union[str, Any]=96			,				lowerCAmelCase  :			List[Any]=False			,				lowerCAmelCase  :			int=[1_92, 3_84, 7_68, 7_68]			,				lowerCAmelCase  :			str=[2, 2, 6, 2]			,				lowerCAmelCase  :			Tuple=[2, 2, 2, 2]			,				lowerCAmelCase  :			Optional[Any]=[3, 3, 3, 3]			,				lowerCAmelCase  :			int="gelu"			,				lowerCAmelCase  :			Any=4.0			,				lowerCAmelCase  :			List[str]=0.0			,				lowerCAmelCase  :			Union[str, Any]=0.1			,				lowerCAmelCase  :			Union[str, Any]=False			,				lowerCAmelCase  :			Tuple=1E-4			,				lowerCAmelCase  :			List[Any]=False			,				lowerCAmelCase  :			Optional[int]=False			,				lowerCAmelCase  :			List[str]=False			,				lowerCAmelCase  :			str=0.02			,				lowerCAmelCase  :			Optional[int]=1E-5			,				lowerCAmelCase  :			List[Any]=32			,				lowerCAmelCase  :			List[Any]=None			,				lowerCAmelCase  :			Union[str, Any]=None			,				**lowerCAmelCase  :			str			,				)	->				List[str]:




         """simple docstring"""

         super().__init__(**lowerCAmelCase)

         lowercase__     =  image_size
         lowercase__     =  patch_size
         lowercase__     =  num_channels
         lowercase__     =  embed_dim
         lowercase__     =  use_conv_embed
         lowercase__     =  hidden_sizes
         lowercase__     =  depths
         lowercase__     =  focal_levels
         lowercase__     =  focal_windows
         lowercase__     =  hidden_act
         lowercase__     =  mlp_ratio
         lowercase__     =  hidden_dropout_prob
         lowercase__     =  drop_path_rate
         lowercase__     =  use_layerscale
         lowercase__     =  layerscale_value
         lowercase__     =  use_post_layernorm
         lowercase__     =  use_post_layernorm_in_modulation
         lowercase__     =  normalize_modulator
         lowercase__     =  initializer_range
         lowercase__     =  layer_norm_eps
         lowercase__     =  encoder_stride
         lowercase__     =  ['stem'] + [f'''stage{idx}''' for idx in range(1			,				len(self.depths) + 1)]
         lowercase__,	lowercase__     =  get_aligned_output_features_output_indices(
             out_features=lowerCAmelCase			,				out_indices=lowerCAmelCase			,				stage_names=self.stage_names)
