





'''simple docstring'''






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

A :			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_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    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__					,	lowercase__					,	lowercase__    ) ->str:
						for attribute in key.split('.'    ):
												_snake_case: str	       =		getattr(SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_    )

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

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





def    lowercase_   (		lowercase__					,	lowercase__    ) ->Union[str, Any]:
						_snake_case: Any	       =		[]
						_snake_case: Any	       =		fairseq_model.state_dict()

						_snake_case: Dict	       =		hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												_snake_case: Any	       =		False
												if "conv_layers" in name:
																		load_conv_layer(
																		    SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_					,	hf_model.config.feat_extract_norm == 'group'					,	)
																		_snake_case: str	       =		True
												else:
																		for key, mapped_key in MAPPING.items():
																								_snake_case: Any	       =		mapped_key

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

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





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





def    lowercase_   (		lowercase__    ) ->Optional[Any]:
						_snake_case: str	       =		HubertConfig()
						_snake_case: List[Any]	       =		model.config

						_snake_case: Tuple	       =		fs_config.activation_dropout
						_snake_case: Optional[int]	       =		False
						_snake_case: Any	       =		fs_config.attention_dropout
						_snake_case: str	       =		False
						_snake_case: Any	       =		eval(fs_config.extractor_conv_feature_layers    )
						_snake_case: Dict	       =		[x[0] for x in conv_layers]
						_snake_case: Union[str, Any]	       =		[x[1] for x in conv_layers]
						_snake_case: List[str]	       =		[x[2] for x in conv_layers]
						_snake_case: Union[str, Any]	       =		'''gelu'''
						_snake_case: Tuple	       =		'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						_snake_case: Dict	       =		False
						_snake_case: Tuple	       =		0.0
						_snake_case: Optional[Any]	       =		0.0
						_snake_case: List[Any]	       =		fs_config.activation_fn
						_snake_case: Any	       =		fs_config.dropout
						_snake_case: int	       =		fs_config.encoder_embed_dim
						_snake_case: List[Any]	       =		0.0_2
						_snake_case: Tuple	       =		fs_config.encoder_ffn_embed_dim
						_snake_case: Union[str, Any]	       =		1e-5
						_snake_case: Any	       =		0.0
						_snake_case: Any	       =		fs_config.encoder_attention_heads
						_snake_case: Any	       =		fs_config.conv_pos_groups
						_snake_case: Optional[int]	       =		fs_config.conv_pos
						_snake_case: Union[str, Any]	       =		len(SCREAMING_SNAKE_CASE_    )
						_snake_case: List[Any]	       =		fs_config.encoder_layers

						return config





@torch.no_grad()
def    lowercase_   (		lowercase__					,	lowercase__=None    ) ->List[Any]:
						_snake_case: List[Any]	       =		distilhubert().model.model

						if config_path is not None:
												_snake_case: Union[str, Any]	       =		HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_    )
						else:
												_snake_case: Any	       =		convert_config(SCREAMING_SNAKE_CASE_    )
						_snake_case: str	       =		model.eval()

						_snake_case: List[Any]	       =		WavaVecaFeatureExtractor(
						    feature_size=1					,	sampling_rate=1_6000					,	padding_value=0					,	do_normalize=SCREAMING_SNAKE_CASE_					,	return_attention_mask=SCREAMING_SNAKE_CASE_					,	)
						_snake_case: Union[str, Any]	       =		HubertModel(SCREAMING_SNAKE_CASE_    )

						recursively_load_weights(SCREAMING_SNAKE_CASE_					,	SCREAMING_SNAKE_CASE_    )

						feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_    )
						hf_model.save_pretrained(SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
		A :			Any							   =      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')
		A :			Union[str, Any]							   =      parser.parse_args()
		convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)












def        A__						(							SCREAMING_SNAKE_CASE_ = 1_0_0_0		) ->  int:
		lowerCamelCase :			Dict    		=[]
		lowerCamelCase :			Dict    		=[]
		lowerCamelCase :			Union[str, Any]    		=3
		lowerCamelCase :			List[str]    		=5
		lowerCamelCase :			Union[str, Any]    		=1
		while True:
				lowerCamelCase :			Dict    		=z * temp
				if result < n:
						zmulti.append(SCREAMING_SNAKE_CASE_		)
						temp += 1
				else:
						lowerCamelCase :			List[Any]    		=1
						break
		while True:
				lowerCamelCase :			Optional[Any]    		=x * temp
				if result < n:
						xmulti.append(SCREAMING_SNAKE_CASE_		)
						temp += 1
				else:
						break
		lowerCamelCase :			str    		=list(set(xmulti + zmulti		)		)
		return sum(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
				print(F"""{solution() = }""")

