




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowercase_      :							Union[str, Any]    							=					logging.get_logger(__name__)

lowercase_      :							List[str]    							=					{
    '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 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						for attribute in key.split("."		):
												_UpperCAmelCase						=	getattr(snake_case_      ,       snake_case_		)

						if weight_type is not None:
												_UpperCAmelCase						=	getattr(snake_case_      ,       snake_case_		).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 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_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(
																		    snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       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(snake_case_		)[0].split("."		)[-2]
																																				_UpperCAmelCase						=	mapped_key.replace("*"      ,       snake_case_		)
																														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(snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		)
																								continue
												if not is_used:
																		unused_weights.append(snake_case_		)

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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_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(snake_case_		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						_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(snake_case_		)
						_UpperCAmelCase						=	fs_config.encoder_layers

						return config


@torch.no_grad()
def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_=None		):

						'''simple docstring'''


						_UpperCAmelCase						=	distilhubert().model.model

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

						_UpperCAmelCase						=	WavaVecaFeatureExtractor(
						    feature_size=1      ,       sampling_rate=1_6000      ,       padding_value=0      ,       do_normalize=snake_case_      ,       return_attention_mask=snake_case_      ,       )
						_UpperCAmelCase						=	HubertModel(snake_case_		)

						recursively_load_weights(snake_case_      ,       snake_case_		)

						feature_extractor.save_pretrained(snake_case_		)
						hf_model.save_pretrained(snake_case_		)


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










def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						return int(snake_case_		) if x - int(snake_case_		) >= 0 else int(snake_case_		) - 1


if __name__ == "__main__":
				import doctest

				doctest.testmod()




