




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE:    Optional[int]											=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:    List[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 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Any:
		for attribute in key.split('.'   ):
				SCREAMING_SNAKE_CASE_							=       getattr(lowerCAmelCase						,	lowerCAmelCase   )

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

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


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Dict:
		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       fairseq_model.state_dict()

		SCREAMING_SNAKE_CASE_							=       hf_model.feature_extractor

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

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


def 				_a       (				lowerCAmelCase   )->  Optional[int]:
		SCREAMING_SNAKE_CASE_							=       HubertConfig()
		SCREAMING_SNAKE_CASE_							=       model.config

		SCREAMING_SNAKE_CASE_							=       fs_config.activation_dropout
		SCREAMING_SNAKE_CASE_							=       False
		SCREAMING_SNAKE_CASE_							=       fs_config.attention_dropout
		SCREAMING_SNAKE_CASE_							=       False
		SCREAMING_SNAKE_CASE_							=       eval(fs_config.extractor_conv_feature_layers   )
		SCREAMING_SNAKE_CASE_							=       [x[0] for x in conv_layers]
		SCREAMING_SNAKE_CASE_							=       [x[1] for x in conv_layers]
		SCREAMING_SNAKE_CASE_							=       [x[2] for x in conv_layers]
		SCREAMING_SNAKE_CASE_							=       'gelu'
		SCREAMING_SNAKE_CASE_							=       'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
		SCREAMING_SNAKE_CASE_							=       False
		SCREAMING_SNAKE_CASE_							=       0.0
		SCREAMING_SNAKE_CASE_							=       0.0
		SCREAMING_SNAKE_CASE_							=       fs_config.activation_fn
		SCREAMING_SNAKE_CASE_							=       fs_config.dropout
		SCREAMING_SNAKE_CASE_							=       fs_config.encoder_embed_dim
		SCREAMING_SNAKE_CASE_							=       0.0_2
		SCREAMING_SNAKE_CASE_							=       fs_config.encoder_ffn_embed_dim
		SCREAMING_SNAKE_CASE_							=       1E-5
		SCREAMING_SNAKE_CASE_							=       0.0
		SCREAMING_SNAKE_CASE_							=       fs_config.encoder_attention_heads
		SCREAMING_SNAKE_CASE_							=       fs_config.conv_pos_groups
		SCREAMING_SNAKE_CASE_							=       fs_config.conv_pos
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       fs_config.encoder_layers

		return config


@torch.no_grad()
def 				_a       (				lowerCAmelCase						,	lowerCAmelCase=None   )->  List[Any]:
		SCREAMING_SNAKE_CASE_							=       distilhubert().model.model

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

		SCREAMING_SNAKE_CASE_							=       WavaVecaFeatureExtractor(
		    feature_size=1						,	sampling_rate=16000						,	padding_value=0						,	do_normalize=lowerCAmelCase						,	return_attention_mask=lowerCAmelCase						,	)
		SCREAMING_SNAKE_CASE_							=       HubertModel(lowerCAmelCase   )

		recursively_load_weights(lowerCAmelCase						,	lowerCAmelCase   )

		feature_extractor.save_pretrained(lowerCAmelCase   )
		hf_model.save_pretrained(lowerCAmelCase   )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    List[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''')
						SCREAMING_SNAKE_CASE:    Tuple											=   parser.parse_args()
						convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)





from __future__ import annotations


def 				_a       (				lowerCAmelCase   )->  list[int]:
		if not list_of_ints:
				return []

		if min(lowerCAmelCase   ) < 0:
				raise ValueError('All numbers must be positive'   )

		SCREAMING_SNAKE_CASE_							=       max(len(bin(lowerCAmelCase   )[2:]   ) for x in list_of_ints   )
		return _msd_radix_sort(lowerCAmelCase						,	lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  list[int]:
		if bit_position == 0 or len(lowerCAmelCase   ) in [0, 1]:
				return list_of_ints

		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       []
		# Split numbers based on bit at bit_position from the right
		for number in list_of_ints:
				if (number >> (bit_position - 1)) & 1:
						# number has a one at bit bit_position
						ones.append(lowerCAmelCase   )
				else:
						# number has a zero at bit bit_position
						zeros.append(lowerCAmelCase   )

    # recursively split both lists further
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )

		# recombine lists
		SCREAMING_SNAKE_CASE_							=       zeros
		res.extend(lowerCAmelCase   )

		return res


def 				_a       (				lowerCAmelCase   )->  Dict:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if not list_of_ints or length == 1:
				return

		if min(lowerCAmelCase   ) < 0:
				raise ValueError('All numbers must be positive'   )

		SCREAMING_SNAKE_CASE_							=       max(len(bin(lowerCAmelCase   )[2:]   ) for x in list_of_ints   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	0						,	lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Optional[int]:
		if bit_position == 0 or end_index - begin_index <= 1:
				return

		bit_position -= 1

		SCREAMING_SNAKE_CASE_							=       begin_index
		SCREAMING_SNAKE_CASE_							=       end_index - 1
		while i <= j:
				SCREAMING_SNAKE_CASE_							=       False
				if not (list_of_ints[i] >> bit_position) & 1:
						# found zero at the beginning
						i += 1
						SCREAMING_SNAKE_CASE_							=       True
				if (list_of_ints[j] >> bit_position) & 1:
						# found one at the end
						j -= 1
						SCREAMING_SNAKE_CASE_							=       True

				if changed:
						continue

				SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       list_of_ints[j], list_of_ints[i]
				j -= 1
				if j != i:
						i += 1

		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()