


import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    List[str]:




      """simple docstring"""

      snake_case_			= WavLMForSequenceClassification.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 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Dict:




      """simple docstring"""

      snake_case_			= WavLMForAudioFrameClassification.from_pretrained(lowercase_   ,   config=lowercase_   )
      snake_case_			= downstream_dict['''model.linear.weight''']
      snake_case_			= downstream_dict['''model.linear.bias''']
      return model






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    int:




      """simple docstring"""

      snake_case_			= WavLMForXVector.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 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Dict:




      """simple docstring"""

      snake_case_			= torch.load(lowercase_   ,   map_location='''cpu'''   )

      snake_case_			= checkpoint['''Downstream''']

      snake_case_			= WavLMConfig.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__":
   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)





'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

_lowerCamelCase				:		Optional[Any]				      =    {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					"""swiftformer"""






					def __init__(       self,    __UpperCamelCase=3,    __UpperCamelCase=[3, 3, 6, 4],    __UpperCamelCase=[48, 56, 112, 220],    __UpperCamelCase=4,    __UpperCamelCase=[True, True, True, True],    __UpperCamelCase="gelu",    __UpperCamelCase=3,    __UpperCamelCase=2,    __UpperCamelCase=1,    __UpperCamelCase=0.0,    __UpperCamelCase=True,    __UpperCamelCase=1E-5,    __UpperCamelCase=1E-5,    **__UpperCamelCase,    ):
							super().__init__(**__UpperCamelCase					)
							_snake_case      			=			num_channels
							_snake_case      			=			depths
							_snake_case      			=			embed_dims
							_snake_case      			=			mlp_ratio
							_snake_case      			=			downsamples
							_snake_case      			=			hidden_act
							_snake_case      			=			down_patch_size
							_snake_case      			=			down_stride
							_snake_case      			=			down_pad
							_snake_case      			=			drop_path_rate
							_snake_case      			=			use_layer_scale
							_snake_case      			=			layer_scale_init_value
							_snake_case      			=			batch_norm_eps





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					version.parse("""1.11"""				)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return 1E-4





