

"""simple docstring"""





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       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase			=		tf.constant([-1_0_0, -1.0, -0.1, 0, 0.1, 1.0, 100.0]			)
    __lowercase			=		get_tf_activation("gelu"			)
    __lowercase			=		get_tf_activation("gelu_10"			)

    __lowercase			=		gelu(lowerCAmelCase_			)
    __lowercase			=		geluaa(lowerCAmelCase_			)

    __lowercase			=		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       _lowercase				(    self							:    List[str]			):
    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_			)



"""simple docstring"""





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_a				   =							['UtteranceLevel']




@torch.no_grad()
def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : int,    UpperCamelCase_  : Optional[int],    UpperCamelCase_  : Union[str, Any])			->		Any:
   '''simple docstring'''


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

   __lowercase			=		checkpoint["Downstream"]

   __lowercase			=		HubertConfig.from_pretrained(UpperCamelCase_)
   __lowercase			=		HubertForSequenceClassification.from_pretrained(UpperCamelCase_,    config=UpperCamelCase_)
   __lowercase			=		WavaVecaFeatureExtractor.from_pretrained(
       UpperCamelCase_,    return_attention_mask=UpperCamelCase_,    do_normalize=UpperCamelCase_)

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

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

   hf_feature_extractor.save_pretrained(UpperCamelCase_)
   hf_model.save_pretrained(UpperCamelCase_)


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




