






'''simple docstring'''






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

__A     						= {
    '''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	(					A	:							Tuple	,		A	:							Dict	,		A	:							Dict	,		A	:							Tuple	,		A	:							List[Any]	)     ->			Tuple:







   """simple docstring"""
   for attribute in key.split('.'	):
      __snake_case					:   str  				=			getattr(_UpperCamelCase	,		_UpperCamelCase	)

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

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




def 							_SCREAMING_SNAKE_CASE	(					A	:							str	,		A	:							Optional[Any]	)     ->			Union[str, Any]:







   """simple docstring"""
   __snake_case					:   Any  				=			[]
   __snake_case					:   Union[str, Any]  				=			fairseq_model.state_dict()

   __snake_case					:   Optional[Any]  				=			hf_model.feature_extractor

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

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

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




def 							_SCREAMING_SNAKE_CASE	(					A	:							str	,		A	:							List[str]	,		A	:							List[str]	,		A	:							Dict	,		A	:							Optional[int]	)     ->			Dict:







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




def 							_SCREAMING_SNAKE_CASE	(					A	:							int	)     ->			str:







   """simple docstring"""
   __snake_case					:   Tuple  				=			HubertConfig()
   __snake_case					:   Any  				=			model.config

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

   return config




@torch.no_grad()
def 							_SCREAMING_SNAKE_CASE	(					A	:							Union[str, Any]	,		A	:							str=None	)     ->			Optional[int]:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			distilhubert().model.model

   if config_path is not None:
      __snake_case					:   Optional[int]  				=			HubertConfig.from_pretrained(_UpperCamelCase	)
   else:
      __snake_case					:   List[str]  				=			convert_config(_UpperCamelCase	)
   __snake_case					:   Union[str, Any]  				=			model.eval()

   __snake_case					:   str  				=			WavaVecaFeatureExtractor(
       feature_size=1	,		sampling_rate=1_60_00	,		padding_value=0	,		do_normalize=_UpperCamelCase	,		return_attention_mask=_UpperCamelCase	,		)
   __snake_case					:   Optional[Any]  				=			HubertModel(_UpperCamelCase	)

   recursively_load_weights(_UpperCamelCase	,		_UpperCamelCase	)

   feature_extractor.save_pretrained(_UpperCamelCase	)
   hf_model.save_pretrained(_UpperCamelCase	)


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

"""simple docstring"""







def        __lowerCAmelCase							(_UpperCamelCase = 10**6						):

	if n <= 0:
		raise ValueError('Please enter an integer greater than 0'						)

	__lowerCAmelCase     :		Optional[Any]		     = list(range(n + 1						)						)
	for number in range(2			,    n + 1						):
		if phi[number] == number:
			phi[number] -= 1
			for multiple in range(number * 2			,    n + 1			,    _UpperCamelCase						):
				__lowerCAmelCase     :		Tuple		     = (phi[multiple] // number) * (number - 1)

	__lowerCAmelCase     :		Any		     = 1
	for number in range(1			,    n + 1						):
		if (answer / phi[answer]) < (number / phi[number]):
			__lowerCAmelCase     :		List[Any]		     = number

	return answer


if __name__ == "__main__":
							print(solution())