

'''simple docstring'''




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

snake_case__          =						{
    """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__    ( lowerCamelCase__				:   List[Any]			,		lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   str			,		lowerCamelCase__				:   str			,		lowerCamelCase__				:   List[Any]			)   ->   str:
					for attribute in key.split('''.'''			):
										A_ :     Union[str, Any] =							getattr(lowerCamelCase__			,		lowerCamelCase__			)

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

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




def 						snake_case__    ( lowerCamelCase__				:   Dict			,		lowerCamelCase__				:   Any			)   ->   List[str]:
					A_ :     Optional[int] =							[]
					A_ :     Optional[Any] =							fairseq_model.state_dict()

					A_ :     Optional[Any] =							hf_model.feature_extractor

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

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

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




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




def 						snake_case__    ( lowerCamelCase__				:   List[str]			)   ->   List[Any]:
					A_ :     Optional[int] =							HubertConfig()
					A_ :     Any =							model.config

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

					return config




@torch.no_grad()
def 						snake_case__    ( lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   int=None			)   ->   Any:
					A_ :     Any =							distilhubert().model.model

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

					A_ :     str =							WavaVecaFeatureExtractor(
					    feature_size=1			,		sampling_rate=1_6_0_0_0			,		padding_value=0			,		do_normalize=lowerCamelCase__			,		return_attention_mask=lowerCamelCase__			,		)
					A_ :     List[str] =							HubertModel(lowerCamelCase__			)

					recursively_load_weights(lowerCamelCase__			,		lowerCamelCase__			)

					feature_extractor.save_pretrained(lowerCamelCase__			)
					hf_model.save_pretrained(lowerCamelCase__			)


if __name__ == "__main__":
							snake_case__          =						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""")
							snake_case__          =						parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)









'''simple docstring'''




def 						snake_case__    ( lowerCamelCase__				:   float			)   ->   float:
					return -num if num < 0 else num




def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   int:
					if len(lowerCamelCase__			) == 0:
										raise ValueError('''abs_min() arg is an empty sequence'''			)
					A_ :     Any =							x[0]
					for i in x:
										if abs_val(lowerCamelCase__			) < abs_val(lowerCamelCase__			):
															A_ :     Tuple =							i
					return j




def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   int:
					if len(lowerCamelCase__			) == 0:
										raise ValueError('''abs_max() arg is an empty sequence'''			)
					A_ :     List[str] =							x[0]
					for i in x:
										if abs(lowerCamelCase__			) > abs(lowerCamelCase__			):
															A_ :     Any =							i
					return j




def 						snake_case__    ( lowerCamelCase__				:   list[int]			)   ->   int:
					if len(lowerCamelCase__			) == 0:
										raise ValueError('''abs_max_sort() arg is an empty sequence'''			)
					return sorted(lowerCamelCase__			,		key=lowerCamelCase__			)[-1]




def 						snake_case__    ( )   ->   Optional[Any]:
					assert abs_val(0			) == 0
					assert abs_val(3_4			) == 3_4
					assert abs_val(-1_0_0_0_0_0_0_0_0_0_0_0			) == 1_0_0_0_0_0_0_0_0_0_0_0

					A_ :     Dict =							[-3, -1, 2, -1_1]
					assert abs_max(lowerCamelCase__			) == -1_1
					assert abs_max_sort(lowerCamelCase__			) == -1_1
					assert abs_min(lowerCamelCase__			) == -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							test_abs_val()
							print(abs_val(-34))  # --> 34



