"""simple docstring"""







import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCamelCase_    :    Any											=						logging.get_logger(__name__)

UpperCamelCase_    :    int											=						{
    '''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 					a__ (						UpperCAmelCase__    :     int						,     UpperCAmelCase__    :     Optional[Any]						,     UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     List[str]						,     UpperCAmelCase__    :     List[str]     )->		Optional[int]:
						for attribute in key.split("""."""     ):
												UpperCAmelCase__   		=						getattr(A__						,     A__     )

						if weight_type is not None:
												UpperCAmelCase__   		=						getattr(A__						,     A__     ).shape
						else:
												UpperCAmelCase__   		=						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":
												UpperCAmelCase__   		=						value
						elif weight_type == "weight_g":
												UpperCAmelCase__   		=						value
						elif weight_type == "weight_v":
												UpperCAmelCase__   		=						value
						elif weight_type == "bias":
												UpperCAmelCase__   		=						value
						else:
												UpperCAmelCase__   		=						value

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


def 					a__ (						UpperCAmelCase__    :     Any						,     UpperCAmelCase__    :     str     )->		List[str]:
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						fairseq_model.state_dict()

						UpperCAmelCase__   		=						hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												UpperCAmelCase__   		=						False
												if "conv_layers" in name:
																		load_conv_layer(
																		    A__						,     A__						,     A__						,     A__						,     hf_model.config.feat_extract_norm == """group"""						,     )
																		UpperCAmelCase__   		=						True
												else:
																		for key, mapped_key in MAPPING.items():
																								UpperCAmelCase__   		=						mapped_key

																								if key in name:
																														UpperCAmelCase__   		=						True
																														if "*" in mapped_key:
																																				UpperCAmelCase__   		=						name.split(A__     )[0].split("""."""     )[-2]
																																				UpperCAmelCase__   		=						mapped_key.replace("""*"""						,     A__     )
																														if "weight_g" in name:
																																				UpperCAmelCase__   		=						'weight_g'
																														elif "weight_v" in name:
																																				UpperCAmelCase__   		=						'weight_v'
																														elif "weight" in name:
																																				UpperCAmelCase__   		=						'weight'
																														elif "bias" in name:
																																				UpperCAmelCase__   		=						'bias'
																														else:
																																				UpperCAmelCase__   		=						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 					a__ (						UpperCAmelCase__    :     Optional[int]						,     UpperCAmelCase__    :     List[str]						,     UpperCAmelCase__    :     Tuple						,     UpperCAmelCase__    :     Union[str, Any]						,     UpperCAmelCase__    :     List[str]     )->		Tuple:
						UpperCAmelCase__   		=						full_name.split("""conv_layers."""     )[-1]
						UpperCAmelCase__   		=						name.split("""."""     )
						UpperCAmelCase__   		=						int(items[0]     )
						UpperCAmelCase__   		=						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.'
																		)
																		UpperCAmelCase__   		=						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.'
																		)
																		UpperCAmelCase__   		=						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."
																		)
																		UpperCAmelCase__   		=						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.'
																		)
																		UpperCAmelCase__   		=						value
																		logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
						else:
												unused_weights.append(A__     )


def 					a__ (						UpperCAmelCase__    :     List[Any]     )->		Union[str, Any]:
						UpperCAmelCase__   		=						HubertConfig()
						UpperCAmelCase__   		=						model.config

						UpperCAmelCase__   		=						fs_config.activation_dropout
						UpperCAmelCase__   		=						False
						UpperCAmelCase__   		=						fs_config.attention_dropout
						UpperCAmelCase__   		=						False
						UpperCAmelCase__   		=						eval(fs_config.extractor_conv_feature_layers     )
						UpperCAmelCase__   		=						[x[0] for x in conv_layers]
						UpperCAmelCase__   		=						[x[1] for x in conv_layers]
						UpperCAmelCase__   		=						[x[2] for x in conv_layers]
						UpperCAmelCase__   		=						'gelu'
						UpperCAmelCase__   		=						'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
						UpperCAmelCase__   		=						False
						UpperCAmelCase__   		=						0.0
						UpperCAmelCase__   		=						0.0
						UpperCAmelCase__   		=						fs_config.activation_fn
						UpperCAmelCase__   		=						fs_config.dropout
						UpperCAmelCase__   		=						fs_config.encoder_embed_dim
						UpperCAmelCase__   		=						0.02
						UpperCAmelCase__   		=						fs_config.encoder_ffn_embed_dim
						UpperCAmelCase__   		=						1e-5
						UpperCAmelCase__   		=						0.0
						UpperCAmelCase__   		=						fs_config.encoder_attention_heads
						UpperCAmelCase__   		=						fs_config.conv_pos_groups
						UpperCAmelCase__   		=						fs_config.conv_pos
						UpperCAmelCase__   		=						len(A__     )
						UpperCAmelCase__   		=						fs_config.encoder_layers

						return config


@torch.no_grad()
def 					a__ (						UpperCAmelCase__    :     int						,     UpperCAmelCase__    :     Optional[int]=None     )->		str:
						UpperCAmelCase__   		=						distilhubert().model.model

						if config_path is not None:
												UpperCAmelCase__   		=						HubertConfig.from_pretrained(A__     )
						else:
												UpperCAmelCase__   		=						convert_config(A__     )
						UpperCAmelCase__   		=						model.eval()

						UpperCAmelCase__   		=						WavaVecaFeatureExtractor(
						    feature_size=1						,     sampling_rate=1_6_0_0_0						,     padding_value=0						,     do_normalize=A__						,     return_attention_mask=A__						,     )
						UpperCAmelCase__   		=						HubertModel(A__     )

						recursively_load_weights(A__						,     A__     )

						feature_extractor.save_pretrained(A__     )
						hf_model.save_pretrained(A__     )


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


"""simple docstring"""







from __future__ import annotations


def 					a__ (						UpperCAmelCase__    :     int     )->		list[int]:
						assert (
						    isinstance(UpperCAmelCase__						,     UpperCAmelCase__     ) and mask > 0
						), f'mask needs to be positive integer, your input {mask}'
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						mask

						while submask:
												all_submasks.append(UpperCAmelCase__     )
												UpperCAmelCase__   		=						(submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()

