


'''simple docstring'''

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase :       Optional[int]										= logging.get_logger(__name__)

def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase):
						__lowerCAmelCase											= UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase__, config=lowerCAmelCase__)
						__lowerCAmelCase											= downstream_dict['''projector.weight''']
						__lowerCAmelCase											= downstream_dict['''projector.bias''']
						__lowerCAmelCase											= downstream_dict['''model.post_net.linear.weight''']
						__lowerCAmelCase											= downstream_dict['''model.post_net.linear.bias''']
						return model

def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase):
						__lowerCAmelCase											= UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCAmelCase__, config=lowerCAmelCase__)
						__lowerCAmelCase											= downstream_dict['''model.linear.weight''']
						__lowerCAmelCase											= downstream_dict['''model.linear.bias''']
						return model

def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase):
						__lowerCAmelCase											= UniSpeechSatForXVector.from_pretrained(lowerCAmelCase__, config=lowerCAmelCase__)
						__lowerCAmelCase											= downstream_dict['''connector.weight''']
						__lowerCAmelCase											= downstream_dict['''connector.bias''']
						for i, kernel_size in enumerate(hf_config.tdnn_kernel):
												__lowerCAmelCase											= downstream_dict[
												    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
												]
												__lowerCAmelCase											= downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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

@torch.no_grad()
def 							__magic_name__(  lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase):
						__lowerCAmelCase											= torch.load(lowerCAmelCase__, map_location='''cpu''')

						__lowerCAmelCase											= checkpoint['''Downstream''']

						__lowerCAmelCase											= UniSpeechSatConfig.from_pretrained(lowerCAmelCase__)
						__lowerCAmelCase											= WavaVecaFeatureExtractor.from_pretrained(
						    lowerCAmelCase__, return_attention_mask=lowerCAmelCase__, do_normalize=lowerCAmelCase__)

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

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

						hf_feature_extractor.save_pretrained(lowerCAmelCase__)
						hf_model.save_pretrained(lowerCAmelCase__)


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





"""simple docstring"""




lowercase_			     =  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"

def        lowercase							(		lowerCAmelCase__ :     bytes			) ->      bytes:
		# Make sure the supplied data is a bytes-like object
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__a           =			f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
				raise TypeError(lowerCAmelCase__			)

		__a           =			''''''.join(bin(lowerCAmelCase__			)[2:].zfill(8			) for byte in data			)

		__a           =			len(lowerCAmelCase__			) % 6 != 0

		if padding_needed:
				# The padding that will be added later
				__a           =			b'''=''' * ((6 - len(lowerCAmelCase__			) % 6) // 2)

				# Append binary_stream with arbitrary binary digits (0's by default) to make its
				# length a multiple of 6.
				binary_stream += "0" * (6 - len(lowerCAmelCase__			) % 6)
		else:
				__a           =			b''''''

		# Encode every 6 binary digits to their corresponding Base64 character
		return (
		    "".join(
		        B64_CHARSET[int(binary_stream[index : index + 6]    ,					2			)]
		        for index in range(0    ,					len(lowerCAmelCase__			)    ,					6			)			).encode()
		    + padding
		)

def        lowercase							(		lowerCAmelCase__ :     str			) ->      bytes:
		# Make sure encoded_data is either a string or a bytes-like object
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			) and not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				__a           =			(
				    '''argument should be a bytes-like object or ASCII string, '''
				    f'''not \'{encoded_data.__class__.__name__}\''''
				)
				raise TypeError(lowerCAmelCase__			)

		# In case encoded_data is a bytes-like object, make sure it contains only
		# ASCII characters so we convert it to a string object
		if isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				try:
						__a           =			encoded_data.decode('''utf-8'''			)
				except UnicodeDecodeError:
						raise ValueError('''base64 encoded data should only contain ASCII characters'''			)

		__a           =			encoded_data.count('''='''			)

		# Check if the encoded string contains non base64 characters
		if padding:
				assert all(
				    char in B64_CHARSET for char in encoded_data[:-padding]			), "Invalid base64 character(s) found."
		else:
				assert all(
				    char in B64_CHARSET for char in encoded_data			), "Invalid base64 character(s) found."

		# Check the padding
		assert len(lowerCAmelCase__			) % 4 == 0 and padding < 3, "Incorrect padding"

		if padding:
				# Remove padding if there is one
				__a           =			encoded_data[:-padding]

				__a           =			''''''.join(
				    bin(B64_CHARSET.index(lowerCAmelCase__			)			)[2:].zfill(6			) for char in encoded_data			)[: -padding * 2]
		else:
				__a           =			''''''.join(
				    bin(B64_CHARSET.index(lowerCAmelCase__			)			)[2:].zfill(6			) for char in encoded_data			)

		__a           =			[
		    int(binary_stream[index : index + 8]    ,					2			)
		    for index in range(0    ,					len(lowerCAmelCase__			)    ,					8			)
		]

		return bytes(lowerCAmelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






