



'''simple docstring'''







import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   logging.get_logger(__name__)







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[Any]										=      WavLMForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE ,		config=SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      downstream_dict['projector.weight']
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      downstream_dict['projector.bias']
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      downstream_dict['model.post_net.linear.weight']
		SCREAMING_SNAKE_CASE_						:int										=      downstream_dict['model.post_net.linear.bias']
		return model







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      WavLMForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE ,		config=SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:List[Any]										=      downstream_dict['model.linear.weight']
		SCREAMING_SNAKE_CASE_						:str										=      downstream_dict['model.linear.bias']
		return model







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      WavLMForXVector.from_pretrained(SCREAMING_SNAKE_CASE ,		config=SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Any										=      downstream_dict['connector.weight']
		SCREAMING_SNAKE_CASE_						:int										=      downstream_dict['connector.bias']
		for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
				SCREAMING_SNAKE_CASE_						:Dict										=      downstream_dict[
				    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
				]
				SCREAMING_SNAKE_CASE_						:Tuple										=      downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

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







@torch.no_grad()
def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:str										=      torch.load(SCREAMING_SNAKE_CASE ,		map_location='cpu'     )

		SCREAMING_SNAKE_CASE_						:Any										=      checkpoint['Downstream']

		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      WavaVecaFeatureExtractor.from_pretrained(
		    SCREAMING_SNAKE_CASE ,		return_attention_mask=SCREAMING_SNAKE_CASE ,		do_normalize=SCREAMING_SNAKE_CASE     )

		SCREAMING_SNAKE_CASE_						:Dict										=      hf_config.architectures[0]
		if arch.endswith('ForSequenceClassification'     ):
				SCREAMING_SNAKE_CASE_						:Tuple										=      convert_classification(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		elif arch.endswith('ForAudioFrameClassification'     ):
				SCREAMING_SNAKE_CASE_						:str										=      convert_diarization(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		elif arch.endswith('ForXVector'     ):
				SCREAMING_SNAKE_CASE_						: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:
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      checkpoint['Featurizer']['weights']

		hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE     )
		hf_model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__     : Tuple				=   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__     : Dict				=   parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


__snake_case		:Any 						=	random.Random()





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase=1.0  ,					_UpperCAmelCase=None  ,					_UpperCAmelCase=None							):
    if rng is None:
        __a					=							global_rng

    __a					=							[]
    for batch_idx in range(shape[0]							):
        values.append([]							)
        for _ in range(shape[1]							):
            values[-1].append(rng.random() * scale							)

    return values

@require_torch
class        _A			(      unittest.TestCase       ):



       def __init__(						self				:	Dict	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	Tuple=7	,						__SCREAMING_SNAKE_CASE				:	Any=400	,						__SCREAMING_SNAKE_CASE				:	Any=2_000	,						__SCREAMING_SNAKE_CASE				:	int=1	,						__SCREAMING_SNAKE_CASE				:	Dict=0.0	,						__SCREAMING_SNAKE_CASE				:	Optional[int]=24_000	,						__SCREAMING_SNAKE_CASE				:	Dict=True	,						):


           '''simple docstring'''

           __a					=							parent
           __a					=							batch_size
           __a					=							min_seq_length
           __a					=							max_seq_length
           __a					=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           __a					=							feature_size
           __a					=							padding_value
           __a					=							sampling_rate
           __a					=							return_attention_mask



       def 						_lowerCamelCase     (						self				:	List[str]):


           '''simple docstring'''

           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "return_attention_mask": self.return_attention_mask,
           }





       def 						_lowerCamelCase     (						self				:	List[Any]	,						__SCREAMING_SNAKE_CASE				:	Union[str, Any]=False	,						__SCREAMING_SNAKE_CASE				:	Optional[Any]=False):


           '''simple docstring'''

           def _flatten(__SCREAMING_SNAKE_CASE				:	List[str]):
               return list(itertools.chain(*__SCREAMING_SNAKE_CASE))

           if equal_length:
               __a					=							floats_list((self.batch_size, self.max_seq_length))
           else:
               # make sure that inputs increase in size
               __a					=							[
                   _flatten(floats_list((x, self.feature_size)))
                   for x in range(self.min_seq_length	,						self.max_seq_length	,						self.seq_length_diff)
               ]

           if numpify:
               __a					=							[np.asarray(__SCREAMING_SNAKE_CASE) for x in audio_inputs]

           return audio_inputs


@require_torch
class        _A			(      __UpperCAmelCase ,unittest.TestCase       ):
       UpperCamelCase__    :       str		   =		EncodecFeatureExtractor



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


           '''simple docstring'''

           __a					=							EnCodecFeatureExtractionTester(self)



       def 						_lowerCamelCase     (						self				:	Optional[int]):


           '''simple docstring'''

           __a					=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           # create three inputs of length 800, 1000, and 1200
           __a					=							[floats_list((1, x))[0] for x in range(800	,						1_400	,						200)]
           __a					=							[np.asarray(__SCREAMING_SNAKE_CASE) for audio_input in audio_inputs]

           # Test not batched input
           __a					=							feat_extract(audio_inputs[0]	,						return_tensors='''np''').input_values
           __a					=							feat_extract(np_audio_inputs[0]	,						return_tensors='''np''').input_values
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						atol=1E-3))

           # Test batched
           __a					=							feat_extract(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''np''').input_values
           __a					=							feat_extract(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''np''').input_values
           for enc_seq_a, enc_seq_a in zip(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE):
               self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						atol=1E-3))



       def 						_lowerCamelCase     (						self				:	Optional[Any]):


           '''simple docstring'''

           __a					=							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
           __a					=							np.random.rand(100).astype(np.floataa)
           __a					=							np_audio_inputs.tolist()

           for inputs in [py_audio_inputs, np_audio_inputs]:
               __a					=							feature_extractor.pad([{'''input_values''': inputs}]	,						return_tensors='''np''')
               self.assertTrue(np_processed.input_values.dtype == np.floataa)
               __a					=							feature_extractor.pad([{'''input_values''': inputs}]	,						return_tensors='''pt''')
               self.assertTrue(pt_processed.input_values.dtype == torch.floataa)



       def 						_lowerCamelCase     (						self				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	str):


           '''simple docstring'''

           from datasets import load_dataset

           __a					=							load_dataset('''hf-internal-testing/librispeech_asr_dummy'''	,						'''clean'''	,						split='''validation''')
           # automatic decoding with librispeech
           __a					=							ds.sort('''id''').select(range(__SCREAMING_SNAKE_CASE))[:num_samples]['''audio''']

           return [x["array"] for x in audio_samples]



       def 						_lowerCamelCase     (						self				:	int):


           '''simple docstring'''

           __a					=							torch.tensor(
               [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
                3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
                2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
                4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
                7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
                4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03])
           # fmt: on
           __a					=							self._load_datasamples(1)
           __a					=							EncodecFeatureExtractor()
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(input_values.shape	,						(1, 1, 93_680))
           self.assertTrue(torch.allclose(input_values[0, 0, :30]	,						__SCREAMING_SNAKE_CASE	,						atol=1E-6))



       def 						_lowerCamelCase     (						self				:	Optional[int]):


           '''simple docstring'''

           __a					=							torch.tensor(
               [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
                3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
                2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
                4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
                7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
                4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03])
           # fmt: on
           __a					=							self._load_datasamples(1)
           __a					=							[np.tile(input_audio[0][None]	,						reps=(2, 1))]
           input_audio[0][1] *= 0.5
           __a					=							EncodecFeatureExtractor(feature_size=2)
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(input_values.shape	,						(1, 2, 93_680))
           self.assertTrue(torch.allclose(input_values[0, 0, :30]	,						__SCREAMING_SNAKE_CASE	,						atol=1E-6))
           self.assertTrue(torch.allclose(input_values[0, 1, :30]	,						EXPECTED_INPUT_VALUES * 0.5	,						atol=1E-6))





       def 						_lowerCamelCase     (						self				:	Any):


           '''simple docstring'''

           __a					=							self._load_datasamples(2)
           # would be easier if the stride was like
           __a					=							EncodecFeatureExtractor(feature_size=1	,						chunk_length_s=1	,						overlap=0.01)

           # pad and trunc raise an error ?
           with self.assertRaisesRegex(
               __SCREAMING_SNAKE_CASE	,						'''^Both padding and truncation were set. Make sure you only set one.$'''	,						):
               __a					=							feature_extractor(
                   __SCREAMING_SNAKE_CASE	,						padding='''max_length'''	,						truncation=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values

           # truncate to chunk
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						truncation=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(2, 1, 71_520))  # 2 chunks

           # force truncate to max_length
           __a					=							feature_extractor(
               __SCREAMING_SNAKE_CASE	,						truncation=__SCREAMING_SNAKE_CASE	,						max_length=48_000	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(2, 1, 48_000))

           # pad to chunk
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(padded_outputs.shape	,						(2, 1, 95_280))

           # pad to chunk
           __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(2, 1, 95_280))

           # force pad to max length
           __a					=							feature_extractor(
               __SCREAMING_SNAKE_CASE	,						padding='''max_length'''	,						max_length=100_000	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(2, 1, 100_000))

           # force no pad
           with self.assertRaisesRegex(
               __SCREAMING_SNAKE_CASE	,						'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''	,						):
               __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values

           __a					=							feature_extractor(input_audio[0]	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(1, 1, 93_680))

           # no pad if no chunk_length_s
           __a					=							None
           with self.assertRaisesRegex(
               __SCREAMING_SNAKE_CASE	,						'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''	,						):
               __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values

           __a					=							feature_extractor(input_audio[0]	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(1, 1, 93_680))

           # no pad if no overlap
           __a					=							2
           __a					=							None
           with self.assertRaisesRegex(
               __SCREAMING_SNAKE_CASE	,						'''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'''	,						):
               __a					=							feature_extractor(__SCREAMING_SNAKE_CASE	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values

           __a					=							feature_extractor(input_audio[0]	,						padding=__SCREAMING_SNAKE_CASE	,						return_tensors='''pt''').input_values
           self.assertEquals(truncated_outputs.shape	,						(1, 1, 93_680))





