






import unittest

from cer import CER


a    =       CER()
class        __snake_case(					unittest.TestCase      ):



 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






    lowercase__						:	Any         = ["""White House"""]
    lowercase__						:	Union[str, Any]         = ["""white house"""]
    # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
    lowercase__						:	Any         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertTrue(abs(char_error_rate - 0.1_818_181_818	) < 1e-6	)



 def 						UpperCAmelCase    (	self   :   Optional[int]	):

    """simple docstring"""






    lowercase__						:	int         = ["""were wolf"""]
    lowercase__						:	Any         = ["""werewolf"""]
    # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
    lowercase__						:	Optional[int]         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertTrue(abs(char_error_rate - 0.1_111_111	) < 1e-6	)

    lowercase__						:	Union[str, Any]         = ["""werewolf"""]
    lowercase__						:	List[str]         = ["""weae     wolf"""]
    # S = 1, D = 1, I = 0, N = 8, CER = 0.25
    lowercase__						:	Union[str, Any]         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertTrue(abs(char_error_rate - 0.25	) < 1e-6	)

    # consecutive whitespaces case 1
    lowercase__						:	Union[str, Any]         = ["""were wolf"""]
    lowercase__						:	Union[str, Any]         = ["""were               wolf"""]
    # S = 0, D = 0, I = 0, N = 9, CER = 0
    lowercase__						:	Optional[Any]         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)

    # consecutive whitespaces case 2
    lowercase__						:	Union[str, Any]         = ["""were   wolf"""]
    lowercase__						:	List[Any]         = ["""were               wolf"""]
    # S = 0, D = 0, I = 0, N = 9, CER = 0
    lowercase__						:	List[Any]         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)



 def 						UpperCAmelCase    (	self   :   str	):

    """simple docstring"""






    lowercase__						:	List[str]         = ["""werewolf"""]
    lowercase__						:	Dict         = ["""weaewolf"""]
    # S = 1, D = 0, I = 0, N = 8, CER = 0.125
    lowercase__						:	int         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertTrue(abs(char_error_rate - 0.125	) < 1e-6	)



 def 						UpperCAmelCase    (	self   :   Optional[Any]	):

    """simple docstring"""






    lowercase__						:	Optional[int]         = ["""werewolf"""]
    lowercase__						:	List[Any]         = ["""wereawolf"""]
    # S = 0, D = 1, I = 0, N = 8, CER = 0.125
    lowercase__						:	Optional[int]         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertTrue(abs(char_error_rate - 0.125	) < 1e-6	)



 def 						UpperCAmelCase    (	self   :   Optional[Any]	):

    """simple docstring"""






    lowercase__						:	List[str]         = ["""werewolf"""]
    lowercase__						:	Any         = ["""wereolf"""]
    # S = 0, D = 0, I = 1, N = 8, CER = 0.125
    lowercase__						:	Optional[Any]         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertTrue(abs(char_error_rate - 0.125	) < 1e-6	)



 def 						UpperCAmelCase    (	self   :   str	):

    """simple docstring"""






    lowercase__						:	Tuple         = ["""werewolf"""]
    lowercase__						:	Union[str, Any]         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertEqual(a_ ,		0.0	)



 def 						UpperCAmelCase    (	self   :   Union[str, Any]	):

    """simple docstring"""






    lowercase__						:	Any         = ["""werewolf""", """I am your father"""]
    lowercase__						:	int         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertEqual(a_ ,		0.0	)

    lowercase__						:	List[Any]         = ["""werewolf""", """I am your father""", """doge"""]
    lowercase__						:	str         = ["""werxwolf""", """I       am your father""", """doge"""]
    # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
    lowercase__						:	Optional[int]         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertTrue(abs(char_error_rate - 0.03_571_428	) < 1e-6	)



 def 						UpperCAmelCase    (	self   :   Optional[Any]	):

    """simple docstring"""






    lowercase__						:	List[str]         = ["""My hovercraft""", """is full of eels"""]
    lowercase__						:	int         = ["""My hovercraft is full""", """ of eels"""]
    # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
    # whitespace at the front of " of eels" will be strip during preporcessing
    # so need to insert 2 whitespaces
    lowercase__						:	str         = cer.compute(predictions=a_ ,		references=a_ ,		concatenate_texts=a_	)
    self.assertTrue(abs(char_error_rate - 0.071_428	) < 1e-6	)



 def 						UpperCAmelCase    (	self   :   Tuple	):

    """simple docstring"""






    lowercase__						:	Union[str, Any]         = ["""我能吞下玻璃而不伤身体"""]
    lowercase__						:	str         = [""" 能吞虾玻璃而 不霜身体啦"""]
    # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
    lowercase__						:	Optional[Any]         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertTrue(abs(char_error_rate - 0.4_545_454_545	) < 1e-6	)

    lowercase__						:	Union[str, Any]         = ["""我能吞下玻璃""", """而不伤身体"""]
    lowercase__						:	Tuple         = ["""我    能 吞 下 玻 璃""", """而不伤身体"""]
    # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
    lowercase__						:	Tuple         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertTrue(abs(char_error_rate - 0.454_545_454_545	) < 1e-6	)

    lowercase__						:	Union[str, Any]         = ["""我能吞下玻璃而不伤身体"""]
    lowercase__						:	int         = cer.compute(predictions=a_ ,		references=a_	)
    self.assertFalse(a_ ,		0.0	)







 def 						UpperCAmelCase    (	self   :   Optional[int]	):

    """simple docstring"""






    lowercase__						:	Tuple         = [""""""]
    lowercase__						:	Any         = ["""Hypothesis"""]
    with self.assertRaises(a_	):
       cer.compute(predictions=a_ ,		references=a_	)


if __name__ == "__main__":
 unittest.main()










import argparse

import torch

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


logging.set_verbosity_info()
a    =       logging.get_logger(__name__)
def       a__						(  A		,				A		,				A					):
   lowercase__						:	int         = WavLMForSequenceClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	Dict         = downstream_dict["""projector.weight"""]
   lowercase__						:	int         = downstream_dict["""projector.bias"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.post_net.linear.weight"""]
   lowercase__						:	List[str]         = downstream_dict["""model.post_net.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Tuple         = WavLMForAudioFrameClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	List[Any]         = downstream_dict["""model.linear.weight"""]
   lowercase__						:	Optional[int]         = downstream_dict["""model.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Optional[int]         = WavLMForXVector.from_pretrained(A		,				config=A					)
   lowercase__						:	List[str]         = downstream_dict["""connector.weight"""]
   lowercase__						:	int         = downstream_dict["""connector.bias"""]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
      lowercase__						:	Optional[Any]         = downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      lowercase__						:	List[str]         = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   lowercase__						:	Union[str, Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
   lowercase__						:	Any         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
   lowercase__						:	List[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
   lowercase__						:	List[Any]         = downstream_dict["""objective.W"""]
   return model
@torch.no_grad()
def       a__						(  A		,				A		,				A		,				A					):
   lowercase__						:	List[Any]         = torch.load(A		,				map_location="""cpu"""					)

   lowercase__						:	Tuple         = checkpoint["""Downstream"""]

   lowercase__						:	List[str]         = WavLMConfig.from_pretrained(A					)
   lowercase__						:	Dict         = WavaVecaFeatureExtractor.from_pretrained(
       A		,				return_attention_mask=A		,				do_normalize=A					)

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

   if hf_config.use_weighted_layer_sum:
      lowercase__						:	Union[str, Any]         = checkpoint["""Featurizer"""]["""weights"""]

   hf_feature_extractor.save_pretrained(A					)
   hf_model.save_pretrained(A					)


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)


