
"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()
lowercase__							:							str													=     logging.get_logger(__name__)




def 		A_	(							snake_case							:				Union[str, Any]	, snake_case							:				Optional[int]	, snake_case							:				str	) ->      List[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     WavLMForSequenceClassification.from_pretrained(snake_case	, config=snake_case	)
						__UpperCamelCase       					=     downstream_dict['''projector.weight''']
						__UpperCamelCase       					=     downstream_dict['''projector.bias''']
						__UpperCamelCase       					=     downstream_dict['''model.post_net.linear.weight''']
						__UpperCamelCase       					=     downstream_dict['''model.post_net.linear.bias''']
						return model




def 		A_	(							snake_case							:				List[str]	, snake_case							:				Optional[int]	, snake_case							:				Any	) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     WavLMForAudioFrameClassification.from_pretrained(snake_case	, config=snake_case	)
						__UpperCamelCase       					=     downstream_dict['''model.linear.weight''']
						__UpperCamelCase       					=     downstream_dict['''model.linear.bias''']
						return model




def 		A_	(							snake_case							:				Dict	, snake_case							:				Dict	, snake_case							:				Optional[Any]	) ->      Union[str, Any]:



						'''simple docstring'''

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

						__UpperCamelCase       					=     downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
						__UpperCamelCase       					=     downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
						__UpperCamelCase       					=     downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
						__UpperCamelCase       					=     downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
						__UpperCamelCase       					=     downstream_dict['''objective.W''']
						return model




@torch.no_grad()
def 		A_	(							snake_case							:				Optional[Any]	, snake_case							:				List[Any]	, snake_case							:				Optional[Any]	, snake_case							:				str	) ->      Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase       					=     torch.load(snake_case	, map_location='''cpu'''	)

						__UpperCamelCase       					=     checkpoint['''Downstream''']

						__UpperCamelCase       					=     WavLMConfig.from_pretrained(snake_case	)
						__UpperCamelCase       					=     WavaVecaFeatureExtractor.from_pretrained(
						    snake_case	, return_attention_mask=snake_case	, do_normalize=snake_case	)

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

						if hf_config.use_weighted_layer_sum:
												__UpperCamelCase       					=     checkpoint['''Featurizer''']['''weights''']

						hf_feature_extractor.save_pretrained(snake_case	)
						hf_model.save_pretrained(snake_case	)


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






import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
				from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

				from .test_feature_extraction_speechta import floats_list


lowercase__							:							List[Any]													=     get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")






@require_torch
class     SCREAMING_SNAKE_CASE__      (							unittest.TestCase      ):

							"""simple docstring"""






							def       A__						(					self     )->				List[Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     tempfile.mkdtemp()

													__UpperCamelCase       					=     SpeechTaTokenizer(SCREAMING_SNAKE_CASE_     )
													tokenizer.save_pretrained(self.tmpdirname     )

													__UpperCamelCase       					=     {
													    '''feature_size''': 1,
													    '''padding_value''': 0.0,
													    '''sampling_rate''': 16000,
													    '''do_normalize''': False,
													    '''num_mel_bins''': 80,
													    '''hop_length''': 16,
													    '''win_length''': 64,
													    '''win_function''': '''hann_window''',
													    '''fmin''': 80,
													    '''fmax''': 7600,
													    '''mel_floor''': 1E-10,
													    '''reduction_factor''': 2,
													    '''return_attention_mask''': True,
													}

													__UpperCamelCase       					=     os.path.join(self.tmpdirname       ,		SCREAMING_SNAKE_CASE_     )
													with open(self.feature_extraction_file       ,		'''w'''       ,		encoding='''utf-8'''     ) as fp:
																			fp.write(json.dumps(SCREAMING_SNAKE_CASE_     ) + '''\n'''     )



							def       A__						(					self       ,		**SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,		**SCREAMING_SNAKE_CASE_     )



							def       A__						(					self       ,		**SCREAMING_SNAKE_CASE_     )->				Union[str, Any]:





													'''simple docstring'''






													return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,		**SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				str:





													'''simple docstring'''






													shutil.rmtree(self.tmpdirname     )



							def       A__						(					self     )->				Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_tokenizer()
													__UpperCamelCase       					=     self.get_feature_extractor()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													processor.save_pretrained(self.tmpdirname     )
													__UpperCamelCase       					=     SpeechTaProcessor.from_pretrained(self.tmpdirname     )

													self.assertEqual(processor.tokenizer.get_vocab()       ,		tokenizer.get_vocab()     )
													self.assertIsInstance(processor.tokenizer       ,		SCREAMING_SNAKE_CASE_     )

													self.assertEqual(processor.feature_extractor.to_json_string()       ,		feature_extractor.to_json_string()     )
													self.assertIsInstance(processor.feature_extractor       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,		feature_extractor=self.get_feature_extractor()     )
													processor.save_pretrained(self.tmpdirname     )

													__UpperCamelCase       					=     self.get_tokenizer(bos_token='''(BOS)'''       ,		eos_token='''(EOS)'''     )
													__UpperCamelCase       					=     self.get_feature_extractor(do_normalize=SCREAMING_SNAKE_CASE_       ,		padding_value=1.0     )

													__UpperCamelCase       					=     SpeechTaProcessor.from_pretrained(
													    self.tmpdirname       ,		bos_token='''(BOS)'''       ,		eos_token='''(EOS)'''       ,		do_normalize=SCREAMING_SNAKE_CASE_       ,		padding_value=1.0     )

													self.assertEqual(processor.tokenizer.get_vocab()       ,		tokenizer_add_kwargs.get_vocab()     )
													self.assertIsInstance(processor.tokenizer       ,		SCREAMING_SNAKE_CASE_     )

													self.assertEqual(processor.feature_extractor.to_json_string()       ,		feature_extractor_add_kwargs.to_json_string()     )
													self.assertIsInstance(processor.feature_extractor       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     floats_list((3, 1000)     )

													__UpperCamelCase       					=     feature_extractor(audio=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )
													__UpperCamelCase       					=     processor(audio=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()       ,		input_processor[key].sum()       ,		delta=1E-2     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     floats_list((3, 1000)     )

													__UpperCamelCase       					=     feature_extractor(audio_target=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )
													__UpperCamelCase       					=     processor(audio_target=SCREAMING_SNAKE_CASE_       ,		return_tensors='''np'''     )

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()       ,		input_processor[key].sum()       ,		delta=1E-2     )



							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     '''This is a test string'''

													__UpperCamelCase       					=     processor(text=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer(SCREAMING_SNAKE_CASE_     )

													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]       ,		encoded_processor[key]     )



							def       A__						(					self     )->				int:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     '''This is a test string'''

													__UpperCamelCase       					=     processor(text_target=SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer(SCREAMING_SNAKE_CASE_     )

													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]       ,		encoded_processor[key]     )



							def       A__						(					self     )->				Dict:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													__UpperCamelCase       					=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													__UpperCamelCase       					=     processor.batch_decode(SCREAMING_SNAKE_CASE_     )
													__UpperCamelCase       					=     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     )

													self.assertListEqual(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_     )



							def       A__						(					self     )->				List[str]:





													'''simple docstring'''






													__UpperCamelCase       					=     self.get_feature_extractor()
													__UpperCamelCase       					=     self.get_tokenizer()

													__UpperCamelCase       					=     SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_       ,		feature_extractor=SCREAMING_SNAKE_CASE_     )

													self.assertListEqual(
													    processor.model_input_names       ,		feature_extractor.model_input_names       ,		msg='''`processor` and `feature_extractor` model input names do not match'''       ,		)




