





"""simple docstring"""
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__UpperCamelCase    :	Optional[Any]						=     logging.get_logger(__name__)

__UpperCamelCase    :	int						=     {
    '''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 (_UpperCAmelCase				:						Union[str, Any]     ,    _UpperCAmelCase				:						Optional[Any]     ,    _UpperCAmelCase				:						Any     ,    _UpperCAmelCase				:						List[str]     ,    _UpperCAmelCase				:						Optional[int] ):





 for attribute in key.split('.' ):
  lowerCAmelCase													=   getattr(__a     ,    __a )

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

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

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Tuple     ,    _UpperCAmelCase				:						Dict ):
 lowerCAmelCase													=   []
 lowerCAmelCase													=   fairseq_model.state_dict()

 lowerCAmelCase													=   hf_model.feature_extractor

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

    if key in name:
     lowerCAmelCase													=   True
     if "*" in mapped_key:
      lowerCAmelCase													=   name.split(__a )[0].split('.' )[-2]
      lowerCAmelCase													=   mapped_key.replace('*'     ,    __a )
     if "weight_g" in name:
      lowerCAmelCase													=   '''weight_g'''
     elif "weight_v" in name:
      lowerCAmelCase													=   '''weight_v'''
     elif "weight" in name:
      lowerCAmelCase													=   '''weight'''
     elif "bias" in name:
      lowerCAmelCase													=   '''bias'''
     else:
      lowerCAmelCase													=   None
     set_recursively(__a     ,    __a     ,    __a     ,    __a     ,    __a )
    continue
  if not is_used:
   unused_weights.append(__a )

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

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

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Dict ):
 lowerCAmelCase													=   HubertConfig()
 lowerCAmelCase													=   model.config

 lowerCAmelCase													=   fs_config.activation_dropout
 lowerCAmelCase													=   False
 lowerCAmelCase													=   fs_config.attention_dropout
 lowerCAmelCase													=   False
 lowerCAmelCase													=   eval(fs_config.extractor_conv_feature_layers )
 lowerCAmelCase													=   [x[0] for x in conv_layers]
 lowerCAmelCase													=   [x[1] for x in conv_layers]
 lowerCAmelCase													=   [x[2] for x in conv_layers]
 lowerCAmelCase													=   '''gelu'''
 lowerCAmelCase													=   '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
 lowerCAmelCase													=   False
 lowerCAmelCase													=   0.0
 lowerCAmelCase													=   0.0
 lowerCAmelCase													=   fs_config.activation_fn
 lowerCAmelCase													=   fs_config.dropout
 lowerCAmelCase													=   fs_config.encoder_embed_dim
 lowerCAmelCase													=   0.02
 lowerCAmelCase													=   fs_config.encoder_ffn_embed_dim
 lowerCAmelCase													=   1e-5
 lowerCAmelCase													=   0.0
 lowerCAmelCase													=   fs_config.encoder_attention_heads
 lowerCAmelCase													=   fs_config.conv_pos_groups
 lowerCAmelCase													=   fs_config.conv_pos
 lowerCAmelCase													=   len(__a )
 lowerCAmelCase													=   fs_config.encoder_layers

 return config

@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						List[Any]     ,    _UpperCAmelCase				:						Any=None ):
 lowerCAmelCase													=   distilhubert().model.model

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

 lowerCAmelCase													=   WavaVecaFeatureExtractor(
     feature_size=1     ,    sampling_rate=1_6000     ,    padding_value=0     ,    do_normalize=__a     ,    return_attention_mask=__a     ,    )
 lowerCAmelCase													=   HubertModel(__a )

 recursively_load_weights(__a     ,    __a )

 feature_extractor.save_pretrained(__a )
 hf_model.save_pretrained(__a )


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












"""simple docstring"""
def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						int     ,    _UpperCAmelCase				:						int ):
 return (input_a, input_a).count(0 ) % 2

def 		_SCREAMING_SNAKE_CASE ():
 assert xor_gate(0     ,    0 ) == 0
 assert xor_gate(0     ,    1 ) == 1
 assert xor_gate(1     ,    0 ) == 1
 assert xor_gate(1     ,    1 ) == 0


if __name__ == "__main__":
       print(xor_gate(0, 0))
       print(xor_gate(0, 1))





