


"""simple docstring"""




import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


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

__magic_name__						: Union[str, Any]    =				{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
    'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
    'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
__magic_name__						: str    =				[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def       a_ (	lowercase__ :str,			lowercase__ :List[Any],			lowercase__ :Any,			lowercase__ :Optional[int],			lowercase__ :List[Any]     ):
			for attribute in key.split("""."""     ):
						__lowerCamelCase          =      getattr(lowercase__,			lowercase__     )

			if weight_type is not None:
						__lowerCamelCase          =      getattr(lowercase__,			lowercase__     ).shape
			else:
						__lowerCamelCase          =      hf_pointer.shape

			assert hf_shape == value.shape, (
			    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
			    f' {value.shape} for {full_name}'
			)

			if weight_type == "weight":
						__lowerCamelCase          =      value
			elif weight_type == "weight_g":
						__lowerCamelCase          =      value
			elif weight_type == "weight_v":
						__lowerCamelCase          =      value
			elif weight_type == "bias":
						__lowerCamelCase          =      value
			else:
						__lowerCamelCase          =      value

			logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'     )





def       a_ (	lowercase__ :Optional[Any],			lowercase__ :str     ):
			__lowerCamelCase          =      []
			__lowerCamelCase          =      fairseq_model.state_dict()

			__lowerCamelCase          =      hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						__lowerCamelCase          =      False
						if "conv_layers" in name:
									load_conv_layer(
									    lowercase__,			lowercase__,			lowercase__,			lowercase__,			hf_model.config.feat_extract_norm == """group""",			)
									__lowerCamelCase          =      True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split("""w2v_model."""     )[-1] == name.split("""."""     )[0]:
															__lowerCamelCase          =      True
															if "*" in mapped_key:
																		__lowerCamelCase          =      name.split(lowercase__     )[0].split("""."""     )[-2]
																		__lowerCamelCase          =      mapped_key.replace("""*""",			lowercase__     )
															if "weight_g" in name:
																		__lowerCamelCase          =      """weight_g"""
															elif "weight_v" in name:
																		__lowerCamelCase          =      """weight_v"""
															elif "bias" in name and "relative_attention_bias" not in name:
																		__lowerCamelCase          =      """bias"""
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		__lowerCamelCase          =      """weight"""
															else:
																		__lowerCamelCase          =      None

															set_recursively(lowercase__,			lowercase__,			lowercase__,			lowercase__,			lowercase__     )
												continue
						if not is_used:
									unused_weights.append(lowercase__     )

			logger.warning(f'Unused weights: {unused_weights}'     )





def       a_ (	lowercase__ :Any,			lowercase__ :Optional[Any],			lowercase__ :Tuple,			lowercase__ :Union[str, Any],			lowercase__ :Union[str, Any]     ):
			__lowerCamelCase          =      full_name.split("""conv_layers."""     )[-1]
			__lowerCamelCase          =      name.split("""."""     )
			__lowerCamelCase          =      int(items[0]     )
			__lowerCamelCase          =      int(items[1]     )

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    f'{full_name} has size {value.shape}, but'
									    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
									)
									__lowerCamelCase          =      value
									logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'     )
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    f'{full_name} has size {value.shape}, but'
									    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
									)
									__lowerCamelCase          =      value
									logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'     )
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
									    " found."
									)
									__lowerCamelCase          =      value
									logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    f'{full_name} has size {value.shape}, but'
									    f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
									)
									__lowerCamelCase          =      value
									logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
			else:
						unused_weights.append(lowercase__     )





@torch.no_grad()
def       a_ (	lowercase__ :Optional[int],			lowercase__ :List[str],			lowercase__ :Union[str, Any]=None     ):
			# load the pre-trained checkpoints
			__lowerCamelCase          =      torch.load(lowercase__     )
			__lowerCamelCase          =      WavLMConfigOrig(checkpoint["""cfg"""]     )
			__lowerCamelCase          =      WavLMOrig(lowercase__     )
			model.load_state_dict(checkpoint["""model"""]     )
			model.eval()

			if config_path is not None:
						__lowerCamelCase          =      WavLMConfig.from_pretrained(lowercase__     )
			else:
						__lowerCamelCase          =      WavLMConfig()

			__lowerCamelCase          =      WavLMModel(lowercase__     )

			recursively_load_weights(lowercase__,			lowercase__     )

			hf_wavlm.save_pretrained(lowercase__     )


if __name__ == "__main__":
					__magic_name__						: List[Any]    =				argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					__magic_name__						: Any    =				parser.parse_args()
					convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








"""simple docstring"""




__magic_name__						: Optional[Any]    =				'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'





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

			__lowerCamelCase          =      """""".join(bin(lowercase__     )[2:].zfill(8     ) for byte in data     )

			__lowerCamelCase          =      len(lowercase__     ) % 6 != 0

			if padding_needed:
						# The padding that will be added later
						__lowerCamelCase          =      B"""=""" * ((6 - len(lowercase__     ) % 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(lowercase__     ) % 6)
			else:
						__lowerCamelCase          =      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(lowercase__     ),			6     )     ).encode()
			    + padding
			)





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

			# 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(lowercase__,			lowercase__     ):
						try:
									__lowerCamelCase          =      encoded_data.decode("""utf-8"""     )
						except UnicodeDecodeError:
									raise ValueError("""base64 encoded data should only contain ASCII characters"""     )

			__lowerCamelCase          =      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(lowercase__     ) % 4 == 0 and padding < 3, "Incorrect padding"

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

						__lowerCamelCase          =      """""".join(
						    bin(B64_CHARSET.index(lowercase__     )     )[2:].zfill(6     ) for char in encoded_data     )[: -padding * 2]
			else:
						__lowerCamelCase          =      """""".join(
						    bin(B64_CHARSET.index(lowercase__     )     )[2:].zfill(6     ) for char in encoded_data     )

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

			return bytes(lowercase__     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




