"""simple docstring"""







import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

SCREAMING_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   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	for attribute in key.split('.'      ):
		_lowercase			:     Tuple      					=				getattr(_lowercase  ,_lowercase      )

	if weight_type is not None:
		_lowercase			:     Tuple      					=				getattr(_lowercase  ,_lowercase      ).shape
	else:
		_lowercase			:     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":
		_lowercase			:     Tuple      					=				value
	elif weight_type == "weight_g":
		_lowercase			:     int      					=				value
	elif weight_type == "weight_v":
		_lowercase			:     Dict      					=				value
	elif weight_type == "bias":
		_lowercase			:     Optional[int]      					=				value
	else:
		_lowercase			:     str      					=				value

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


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Union[str, Any]      					=				[]
	_lowercase			:     Any      					=				fairseq_model.state_dict()

	_lowercase			:     Any      					=				hf_model.feature_extractor

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

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

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


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





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


def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	_lowercase			:     Tuple      					=				HubertConfig()
	_lowercase			:     Optional[int]      					=				model.config

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

	return config


@torch.no_grad()
def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase=None      ):






	"""simple docstring"""





	_lowercase			:     Optional[Any]      					=				distilhubert().model.model

	if config_path is not None:
		_lowercase			:     Any      					=				HubertConfig.from_pretrained(_lowercase      )
	else:
		_lowercase			:     List[str]      					=				convert_config(_lowercase      )
	_lowercase			:     List[Any]      					=				model.eval()

	_lowercase			:     int      					=				WavaVecaFeatureExtractor(
	    feature_size=1  ,sampling_rate=16_000  ,padding_value=0  ,do_normalize=_lowercase  ,return_attention_mask=_lowercase  ,)
	_lowercase			:     Optional[Any]      					=				HubertModel(_lowercase      )

	recursively_load_weights(_lowercase  ,_lowercase      )

	feature_extractor.save_pretrained(_lowercase      )
	hf_model.save_pretrained(_lowercase      )


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










"""simple docstring"""




from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime




class 					_a		( _lowerCAmelCase							):






				def __init__(					self						:       Tuple,					*lowerCAmelCase__						:       Tuple,					**lowerCAmelCase__						:       Optional[int]				)      -> Union[str, Any]:




										'''simple docstring'''



										super().__init__(*lowerCAmelCase__,					**lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Tuple				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       (
										    next_prime(value % self.size_table				)
										    if not is_prime(value % self.size_table				)
										    else value % self.size_table
										)  # gt = bigger than
										return next_prime_gt - (data % next_prime_gt)






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Tuple				)      -> Any:




										'''simple docstring'''



										return (increment * self.__hash_function_a(lowerCAmelCase__,					lowerCAmelCase__				)) % self.size_table






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Optional[Any]=None				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       1
										_UpperCamelCase     :   Dict      =       self.hash_function(lowerCAmelCase__				)

										while self.values[new_key] is not None and self.values[new_key] != key:
																_UpperCamelCase     :   Optional[int]      =       (
																    self.__hash_double_function(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)
																    if self.balanced_factor() >= self.lim_charge
																    else None
																)
																if new_key is None:
																						break
																else:
																						i += 1

										return new_key



