import argparse

import torch

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


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







def  __lowerCAmelCase							(						__SCREAMING_SNAKE_CASE    : List[str]						,				__SCREAMING_SNAKE_CASE    : Tuple						,				__SCREAMING_SNAKE_CASE    : Dict					):







    '''simple docstring'''
    __snake_case    :							Any             =  WavLMForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__						,				config=SCREAMING_SNAKE_CASE__					)
    __snake_case    :							Any             =  downstream_dict['projector.weight']
    __snake_case    :							Optional[int]             =  downstream_dict['projector.bias']
    __snake_case    :							str             =  downstream_dict['model.post_net.linear.weight']
    __snake_case    :							Optional[int]             =  downstream_dict['model.post_net.linear.bias']
    return model







def  __lowerCAmelCase							(						__SCREAMING_SNAKE_CASE    : Dict						,				__SCREAMING_SNAKE_CASE    : Any						,				__SCREAMING_SNAKE_CASE    : Optional[Any]					):







    '''simple docstring'''
    __snake_case    :							Optional[Any]             =  WavLMForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE__						,				config=SCREAMING_SNAKE_CASE__					)
    __snake_case    :							Union[str, Any]             =  downstream_dict['model.linear.weight']
    __snake_case    :							Tuple             =  downstream_dict['model.linear.bias']
    return model







def  __lowerCAmelCase							(						__SCREAMING_SNAKE_CASE    : Tuple						,				__SCREAMING_SNAKE_CASE    : Tuple						,				__SCREAMING_SNAKE_CASE    : List[str]					):







    '''simple docstring'''
    __snake_case    :							Dict             =  WavLMForXVector.from_pretrained(SCREAMING_SNAKE_CASE__						,				config=SCREAMING_SNAKE_CASE__					)
    __snake_case    :							Any             =  downstream_dict['connector.weight']
    __snake_case    :							Dict             =  downstream_dict['connector.bias']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
        __snake_case    :							Optional[Any]             =  downstream_dict[
            F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
        ]
        __snake_case    :							Optional[int]             =  downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

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







@torch.no_grad()
def  __lowerCAmelCase							(						__SCREAMING_SNAKE_CASE    : List[str]						,				__SCREAMING_SNAKE_CASE    : Optional[int]						,				__SCREAMING_SNAKE_CASE    : List[str]						,				__SCREAMING_SNAKE_CASE    : List[str]					):







    '''simple docstring'''
    __snake_case    :							Union[str, Any]             =  torch.load(SCREAMING_SNAKE_CASE__						,				map_location="""cpu"""					)

    __snake_case    :							int             =  checkpoint['Downstream']

    __snake_case    :							Tuple             =  WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE__					)
    __snake_case    :							Tuple             =  WavaVecaFeatureExtractor.from_pretrained(
        SCREAMING_SNAKE_CASE__						,				return_attention_mask=SCREAMING_SNAKE_CASE__						,				do_normalize=SCREAMING_SNAKE_CASE__					)

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

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

    hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__					)
    hf_model.save_pretrained(SCREAMING_SNAKE_CASE__					)


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)







class        A_					:




      '''simple docstring'''


      def __init__(				self:       Tuple						,      a:       Dict=None				):
       __lowerCamelCase		:							List[Any]							=						data
       __lowerCamelCase		:							Optional[Any]							=						None




      def __repr__(				self:       Optional[int]				):
       __lowerCamelCase		:							Any							=						''
       __lowerCamelCase		:							List[Any]							=						self
       while temp:
        string_rep += F'<{temp.data}> ---> '
        __lowerCamelCase		:							Any							=						temp.next
       string_rep += "<END>"
       return string_rep



def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):

 # if elements_list is empty
 if not elements_list:
  raise Exception('The Elements List is empty'						)

 # Set first element as Head
 __lowerCamelCase		:							Optional[int]							=						Node(elements_list[0]						)
 __lowerCamelCase		:							List[str]							=						head
 # Loop through elements from position 1
 for data in elements_list[1:]:
  __lowerCamelCase		:							str							=						Node(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Tuple							=						current.next
 return head


lowercase_           =	[1, 3, 5, 3_2, 4_4, 1_2, 4_3]
print(F"""List: {list_data}""")
print('Creating Linked List from List.')
lowercase_           =	make_linked_list(list_data)
print('Linked List:')



print(linked_list)

