





import argparse

import torch

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


logging.set_verbosity_info()
_A :   Dict  			=  logging.get_logger(__name__)



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







   """simple docstring"""
   lowerCamelCase__       :						str        =							WavaVecaForSequenceClassification.from_pretrained(UpperCAmelCase      ,  config=UpperCAmelCase					)
   lowerCamelCase__       :						List[Any]        =							downstream_dict['''projector.weight''']
   lowerCamelCase__       :						str        =							downstream_dict['''projector.bias''']
   lowerCamelCase__       :						Optional[Any]        =							downstream_dict['''model.post_net.linear.weight''']
   lowerCamelCase__       :						Any        =							downstream_dict['''model.post_net.linear.bias''']
   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[Any]:







   """simple docstring"""
   lowerCamelCase__       :						int        =							WavaVecaForAudioFrameClassification.from_pretrained(UpperCAmelCase      ,  config=UpperCAmelCase					)
   lowerCamelCase__       :						Optional[Any]        =							downstream_dict['''model.linear.weight''']
   lowerCamelCase__       :						Optional[Any]        =							downstream_dict['''model.linear.bias''']
   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    str:







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

   lowerCamelCase__       :						int        =							downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
   lowerCamelCase__       :						Tuple        =							downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
   lowerCamelCase__       :						List[str]        =							downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
   lowerCamelCase__       :						List[str]        =							downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
   lowerCamelCase__       :						Optional[int]        =							downstream_dict['''objective.W''']
   return model



@torch.no_grad()
def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   lowerCamelCase__       :						str        =							torch.load(UpperCAmelCase      ,  map_location='''cpu'''					)

   lowerCamelCase__       :						Optional[int]        =							checkpoint['''Downstream''']

   lowerCamelCase__       :						str        =							WavaVecaConfig.from_pretrained(UpperCAmelCase					)
   lowerCamelCase__       :						Optional[int]        =							WavaVecaFeatureExtractor.from_pretrained(
       UpperCAmelCase      ,  return_attention_mask=UpperCAmelCase      ,  do_normalize=UpperCAmelCase					)

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

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

   hf_feature_extractor.save_pretrained(UpperCAmelCase					)
   hf_model.save_pretrained(UpperCAmelCase					)


if __name__ == "__main__":
       _A :   Optional[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 :   List[str]  			=  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


_A :   Any  			=  logging.get_logger(__name__)

_A :   int  			=  {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Dict			   =   "timesformer"







       def __init__(				self					:     List[str]		, A					:     int=2_2_4		, A					:     Optional[Any]=1_6		, A					:     str=3		, A					:     str=8		, A					:     Any=7_6_8		, A					:     Dict=1_2		, A					:     Optional[Any]=1_2		, A					:     Any=3_0_7_2		, A					:     str="gelu"		, A					:     Optional[int]=0.0		, A					:     Union[str, Any]=0.0		, A					:     List[Any]=0.02		, A					:     int=1e-6		, A					:     Tuple=True		, A					:     Any="divided_space_time"		, A					:     Optional[Any]=0		, **A					:     Tuple		, )       ->str:
          super().__init__(**A      )

          lowerCamelCase__       :						Optional[Any]        =							image_size
          lowerCamelCase__       :						int        =							patch_size
          lowerCamelCase__       :						Any        =							num_channels
          lowerCamelCase__       :						Optional[int]        =							num_frames

          lowerCamelCase__       :						Optional[Any]        =							hidden_size
          lowerCamelCase__       :						Optional[int]        =							num_hidden_layers
          lowerCamelCase__       :						Optional[int]        =							num_attention_heads
          lowerCamelCase__       :						Dict        =							intermediate_size
          lowerCamelCase__       :						Optional[Any]        =							hidden_act
          lowerCamelCase__       :						Union[str, Any]        =							hidden_dropout_prob
          lowerCamelCase__       :						Dict        =							attention_probs_dropout_prob
          lowerCamelCase__       :						List[Any]        =							initializer_range
          lowerCamelCase__       :						str        =							layer_norm_eps
          lowerCamelCase__       :						Union[str, Any]        =							qkv_bias

          lowerCamelCase__       :						str        =							attention_type
          lowerCamelCase__       :						List[str]        =							drop_path_rate



