

'''simple docstring'''



import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase				: Union[str, Any]         =  logging.get_logger(__name__)







def      a__ (						a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   WavLMForSequenceClassification.from_pretrained(a__   ,					config=a__		)
     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""projector.weight"""]
     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""projector.bias"""]
     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""model.post_net.linear.weight"""]
     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""model.post_net.linear.bias"""]
     return model







def      a__ (						a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   WavLMForAudioFrameClassification.from_pretrained(a__   ,					config=a__		)
     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""model.linear.weight"""]
     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""model.linear.bias"""]
     return model







def      a__ (						a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   WavLMForXVector.from_pretrained(a__   ,					config=a__		)
     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""connector.weight"""]
     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""connector.bias"""]
     for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
          __SCREAMING_SNAKE_CASE						     =   downstream_dict[
              F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
          ]
          __SCREAMING_SNAKE_CASE						     =   downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
     __SCREAMING_SNAKE_CASE						     =   downstream_dict["""objective.W"""]
     return model







@torch.no_grad()
def      a__ (						a__   ,					a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   torch.load(a__   ,					map_location="""cpu"""		)

     __SCREAMING_SNAKE_CASE						     =   checkpoint["""Downstream"""]

     __SCREAMING_SNAKE_CASE						     =   WavLMConfig.from_pretrained(a__		)
     __SCREAMING_SNAKE_CASE						     =   WavaVecaFeatureExtractor.from_pretrained(
         a__   ,					return_attention_mask=a__   ,					do_normalize=a__		)

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

     if hf_config.use_weighted_layer_sum:
          __SCREAMING_SNAKE_CASE						     =   checkpoint["""Featurizer"""]["""weights"""]

     hf_feature_extractor.save_pretrained(a__		)
     hf_model.save_pretrained(a__		)


if __name__ == "__main__":
  UpperCAmelCase				: str         =  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				: Tuple         =  parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









'''simple docstring'''



from typing import Mapping

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


UpperCAmelCase				: Union[str, Any]         =  logging.get_logger(__name__)







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "mt5"
 lowerCAmelCase__						      =   ["past_key_values"]
 def __init__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=250_112      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=512      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=64      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=1_024      ,							__SCREAMING_SNAKE_CASE      : List[str]=8      ,							__SCREAMING_SNAKE_CASE      : Any=None      ,							__SCREAMING_SNAKE_CASE      : List[str]=6      ,							__SCREAMING_SNAKE_CASE      : Any=32      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=128      ,							__SCREAMING_SNAKE_CASE      : List[str]=0.1      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=1E-6      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=1.0      ,							__SCREAMING_SNAKE_CASE      : Tuple="gated-gelu"      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Tuple="T5Tokenizer"      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=False      ,							__SCREAMING_SNAKE_CASE      : Tuple=0      ,							__SCREAMING_SNAKE_CASE      : str=1      ,							__SCREAMING_SNAKE_CASE      : Dict=0      ,							**__SCREAMING_SNAKE_CASE      : str      ,							)						->				List[Any]:


      """simple docstring"""




      super().__init__(
          is_encoder_decoder=__SCREAMING_SNAKE_CASE      ,							tokenizer_class=__SCREAMING_SNAKE_CASE      ,							tie_word_embeddings=__SCREAMING_SNAKE_CASE      ,							pad_token_id=__SCREAMING_SNAKE_CASE      ,							eos_token_id=__SCREAMING_SNAKE_CASE      ,							decoder_start_token_id=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE      ,							)
      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   d_model
      __SCREAMING_SNAKE_CASE						     =   d_kv
      __SCREAMING_SNAKE_CASE						     =   d_ff
      __SCREAMING_SNAKE_CASE						     =   num_layers
      __SCREAMING_SNAKE_CASE						     =   (
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      __SCREAMING_SNAKE_CASE						     =   num_heads
      __SCREAMING_SNAKE_CASE						     =   relative_attention_num_buckets
      __SCREAMING_SNAKE_CASE						     =   relative_attention_max_distance
      __SCREAMING_SNAKE_CASE						     =   dropout_rate
      __SCREAMING_SNAKE_CASE						     =   layer_norm_epsilon
      __SCREAMING_SNAKE_CASE						     =   initializer_factor
      __SCREAMING_SNAKE_CASE						     =   feed_forward_proj
      __SCREAMING_SNAKE_CASE						     =   use_cache

      __SCREAMING_SNAKE_CASE						     =   self.feed_forward_proj.split("""-"""    )
      __SCREAMING_SNAKE_CASE						     =   act_info[-1]
      __SCREAMING_SNAKE_CASE						     =   act_info[0] == """gated"""

      if len(__SCREAMING_SNAKE_CASE    ) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE    ) > 2:
           raise ValueError(
               f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
               """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
               """'gated-gelu' or 'relu'"""    )

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
           __SCREAMING_SNAKE_CASE						     =   """gelu_new"""
 @property
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


      """simple docstring"""




      return self.d_model
 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				int:


      """simple docstring"""




      return self.num_heads


 @property
 def    UpperCAmelCase__	(	self      : List[str]    )						->				Union[str, Any]:


      """simple docstring"""




      return self.num_layers







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   {
          """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
          """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
      }
      if self.use_past:
           __SCREAMING_SNAKE_CASE						     =   """past_encoder_sequence + sequence"""
           __SCREAMING_SNAKE_CASE						     =   {0: """batch"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
      else:
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """decoder_sequence"""}

      if self.use_past:
           self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE      ,							direction="""inputs"""    )

      return common_inputs
 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				int:


      """simple docstring"""




      return 13


 @property
 def    UpperCAmelCase__	(	self      : Tuple    )						->				float:


      """simple docstring"""




      return 5E-4






