




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCamelCase__		=						logging.get_logger(__name__)

UpperCamelCase__		=						{
    '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_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',
    'mask_emb': 'masked_spec_embed',
}






def  snake_case (							A						,       A						,       A						,       A						,       A      )							->							Dict:


	for attribute in key.split('.'      ):
		lowerCamelCase_							:		Optional[int]							       =							getattr(A						,       A      )

	if weight_type is not None:
		lowerCamelCase_							:		Optional[Any]							       =							getattr(A						,       A      ).shape
	else:
		lowerCamelCase_							:		Any							       =							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_							:		List[Any]							       =							value
	elif weight_type == "weight_g":
		lowerCamelCase_							:		Optional[Any]							       =							value
	elif weight_type == "weight_v":
		lowerCamelCase_							:		Any							       =							value
	elif weight_type == "bias":
		lowerCamelCase_							:		Optional[int]							       =							value
	else:
		lowerCamelCase_							:		Dict							       =							value

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






def  snake_case (							A						,       A      )							->							str:
	lowerCamelCase_							:		Optional[Any]							       =							[]
	lowerCamelCase_							:		List[str]							       =							fairseq_model.state_dict()

	lowerCamelCase_							:		Optional[Any]							       =							hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		lowerCamelCase_							:		int							       =							False
		if "conv_layers" in name:
			load_conv_layer(
			    A						,       A						,       A						,       A						,       hf_model.config.feat_extract_norm == 'group'						,       )
			lowerCamelCase_							:		Optional[int]							       =							True
		else:
			for key, mapped_key in MAPPING.items():
				lowerCamelCase_							:		Tuple							       =							mapped_key

				if key in name:
					lowerCamelCase_							:		Any							       =							True
					if "*" in mapped_key:
						lowerCamelCase_							:		Optional[Any]							       =							name.split(A      )[0].split('.'      )[-2]
						lowerCamelCase_							:		Dict							       =							mapped_key.replace('*'						,       A      )
					if "weight_g" in name:
						lowerCamelCase_							:		Tuple							       =							'weight_g'
					elif "weight_v" in name:
						lowerCamelCase_							:		str							       =							'weight_v'
					elif "weight" in name:
						lowerCamelCase_							:		Tuple							       =							'weight'
					elif "bias" in name:
						lowerCamelCase_							:		List[Any]							       =							'bias'
					else:
						lowerCamelCase_							:		Dict							       =							None
					set_recursively(A						,       A						,       A						,       A						,       A      )
				continue
		if not is_used:
			unused_weights.append(A      )

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






def  snake_case (							A						,       A						,       A						,       A						,       A      )							->							Optional[int]:
	lowerCamelCase_							:		Tuple							       =							full_name.split('conv_layers.'      )[-1]
	lowerCamelCase_							:		List[Any]							       =							name.split('.'      )
	lowerCamelCase_							:		Optional[Any]							       =							int(items[0]      )
	lowerCamelCase_							:		Union[str, Any]							       =							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_							:		Optional[Any]							       =							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_							:		Any							       =							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_							:		Optional[Any]							       =							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_							:		Optional[Any]							       =							value
			logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
	else:
		unused_weights.append(A      )






def  snake_case (							A      )							->							Union[str, Any]:
	lowerCamelCase_							:		Any							       =							HubertConfig()
	lowerCamelCase_							:		int							       =							model.config

	lowerCamelCase_							:		List[str]							       =							fs_config.activation_dropout
	lowerCamelCase_							:		str							       =							False
	lowerCamelCase_							:		Any							       =							fs_config.attention_dropout
	lowerCamelCase_							:		Optional[int]							       =							False
	lowerCamelCase_							:		Tuple							       =							eval(fs_config.extractor_conv_feature_layers      )
	lowerCamelCase_							:		Union[str, Any]							       =							[x[0] for x in conv_layers]
	lowerCamelCase_							:		List[Any]							       =							[x[1] for x in conv_layers]
	lowerCamelCase_							:		Tuple							       =							[x[2] for x in conv_layers]
	lowerCamelCase_							:		List[str]							       =							'gelu'
	lowerCamelCase_							:		List[str]							       =							'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	lowerCamelCase_							:		Tuple							       =							False
	lowerCamelCase_							:		Union[str, Any]							       =							0.0
	lowerCamelCase_							:		List[Any]							       =							0.0
	lowerCamelCase_							:		Any							       =							fs_config.activation_fn
	lowerCamelCase_							:		Optional[Any]							       =							fs_config.dropout
	lowerCamelCase_							:		List[Any]							       =							fs_config.encoder_embed_dim
	lowerCamelCase_							:		str							       =							0.02
	lowerCamelCase_							:		Optional[int]							       =							fs_config.encoder_ffn_embed_dim
	lowerCamelCase_							:		int							       =							1e-5
	lowerCamelCase_							:		Dict							       =							0.0
	lowerCamelCase_							:		Optional[int]							       =							fs_config.encoder_attention_heads
	lowerCamelCase_							:		Any							       =							fs_config.conv_pos_groups
	lowerCamelCase_							:		Optional[Any]							       =							fs_config.conv_pos
	lowerCamelCase_							:		List[Any]							       =							len(A      )
	lowerCamelCase_							:		Dict							       =							fs_config.encoder_layers

	return config






@torch.no_grad()
def  snake_case (							A						,       A=None      )							->							Union[str, Any]:
	lowerCamelCase_							:		int							       =							distilhubert().model.model

	if config_path is not None:
		lowerCamelCase_							:		Dict							       =							HubertConfig.from_pretrained(A      )
	else:
		lowerCamelCase_							:		List[Any]							       =							convert_config(A      )
	lowerCamelCase_							:		int							       =							model.eval()

	lowerCamelCase_							:		List[str]							       =							WavaVecaFeatureExtractor(
	    feature_size=1						,       sampling_rate=16000						,       padding_value=0						,       do_normalize=A						,       return_attention_mask=A						,       )
	lowerCamelCase_							:		Optional[int]							       =							HubertModel(A      )

	recursively_load_weights(A						,       A      )

	feature_extractor.save_pretrained(A      )
	hf_model.save_pretrained(A      )


if __name__ == "__main__":
							UpperCamelCase__		=						argparse.ArgumentParser()
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
							UpperCamelCase__		=						parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






UpperCamelCase__		=						{
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.0_009_842_073,
    "short-ton": 0.0_011_023_122,
    "pound": 2.2_046_244_202,
    "stone": 0.1_574_731_728,
    "ounce": 35.273_990_723,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.022136652e26,
}

UpperCamelCase__		=						{
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1_016.04_608,
    "short-ton": 907.184,
    "pound": 0.453_592,
    "stone": 6.35_029,
    "ounce": 0.0_283_495,
    "carrat": 0.0_002,
    "atomic-mass-unit": 1.660540199e-27,
}






def  snake_case (							A						,       A						,       A      )							->							float:




	'''simple docstring'''


	if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
		lowerCamelCase_							:		Union[str, Any]							       =							(
		    f"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
		    f"Supported values are: {', '.join(A      )}"
		)
		raise ValueError(A      )
	return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
							import doctest

							doctest.testmod()
