import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf

     from transformers.activations_tf import get_tf_activation







@require_tf
class        UpperCAmelCase_					(				unittest.TestCase     ):




    def     __lowerCamelCase			(       self	: List[str] )						->			str:
      a__  		=  tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0] )
      a__  		=  get_tf_activation("gelu" )
      a__  		=  get_tf_activation("gelu_10" )

      a__  		=  gelu(lowerCAmelCase )
      a__  		=  geluaa(lowerCAmelCase )

      a__  		=  tf.where(y_gelu_aa < 10.0   ,   1.0   ,   0.0 )

      self.assertEqual(tf.math.reduce_max(lowerCAmelCase ).numpy().item()   ,   10.0 )
      self.assertTrue(np.allclose(y_gelu * clipped_mask   ,   y_gelu_aa * clipped_mask ) )




    def     __lowerCamelCase			(       self	: int )						->			Optional[Any]:
      get_tf_activation("gelu" )
      get_tf_activation("gelu_10" )
      get_tf_activation("gelu_fast" )
      get_tf_activation("gelu_new" )
      get_tf_activation("glu" )
      get_tf_activation("mish" )
      get_tf_activation("quick_gelu" )
      get_tf_activation("relu" )
      get_tf_activation("sigmoid" )
      get_tf_activation("silu" )
      get_tf_activation("swish" )
      get_tf_activation("tanh" )
      with self.assertRaises(lowerCAmelCase ):
        get_tf_activation("bogus" )
      with self.assertRaises(lowerCAmelCase ):
        get_tf_activation(lowerCAmelCase )


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_SCREAMING_SNAKE_CASE     =     ["""UtteranceLevel"""]
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  torch.load(lowerCamelCase__			,					map_location="cpu"       )
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}"       )

  a__  		=  checkpoint["Downstream"]

  a__  		=  HubertConfig.from_pretrained(lowerCamelCase__       )
  a__  		=  HubertForSequenceClassification.from_pretrained(lowerCamelCase__			,					config=lowerCamelCase__       )
  a__  		=  WavaVecaFeatureExtractor.from_pretrained(
      lowerCamelCase__			,					return_attention_mask=lowerCamelCase__			,					do_normalize=lowerCamelCase__       )

  if hf_congfig.use_weighted_layer_sum:
    a__  		=  checkpoint["Featurizer"]["weights"]

  a__  		=  downstream_dict["projector.weight"]
  a__  		=  downstream_dict["projector.bias"]
  a__  		=  downstream_dict["model.post_net.linear.weight"]
  a__  		=  downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(lowerCamelCase__       )
  hf_model.save_pretrained(lowerCamelCase__       )


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

