


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

SCREAMING_SNAKE_CASE		: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      _lowerCAmelCase  (							lowerCAmelCase_							:int						,  lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:Union[str, Any]							)->int:
   '''simple docstring'''






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

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

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



def      _lowerCAmelCase  (							lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:Any							)->int:
   '''simple docstring'''






   snake_case_         =   []
   snake_case_         =   fairseq_model.state_dict()

   snake_case_         =   hf_model.feature_extractor

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

            if key in name:
               snake_case_         =   True
               if "*" in mapped_key:
                  snake_case_         =   name.split(lowerCAmelCase_							)[0].split("."							)[-2]
                  snake_case_         =   mapped_key.replace("*"						,  lowerCAmelCase_							)
               if "weight_g" in name:
                  snake_case_         =   "weight_g"
               elif "weight_v" in name:
                  snake_case_         =   "weight_v"
               elif "weight" in name:
                  snake_case_         =   "weight"
               elif "bias" in name:
                  snake_case_         =   "bias"
               else:
                  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      _lowerCAmelCase  (							lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Dict							)->Dict:
   '''simple docstring'''






   snake_case_         =   full_name.split("conv_layers."							)[-1]
   snake_case_         =   name.split("."							)
   snake_case_         =   int(items[0]							)
   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.'''
         )
         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.'''
         )
         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."
         )
         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.'''
         )
         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      _lowerCAmelCase  (							lowerCAmelCase_							:Dict							)->str:
   '''simple docstring'''






   snake_case_         =   HubertConfig()
   snake_case_         =   model.config

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

   return config



@torch.no_grad()
def      _lowerCAmelCase  (							lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:List[str]=None							)->Tuple:
   '''simple docstring'''






   snake_case_         =   distilhubert().model.model

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

   snake_case_         =   WavaVecaFeatureExtractor(
       feature_size=1						,  sampling_rate=16_000						,  padding_value=0						,  do_normalize=lowerCAmelCase_						,  return_attention_mask=lowerCAmelCase_						,  )
   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		:Dict											=       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		:Optional[Any]											=       parser.parse_args()
   convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








def      _lowerCAmelCase  (							lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:Tuple							)->Optional[Any]:
   '''simple docstring'''






   assert b != 0, "This cannot accept modulo that is == 0"
   if n == 0:
      return 1

   if n % 2 == 1:
      return (bin_exp_mod(lowerCAmelCase_						,  n - 1						,  lowerCAmelCase_							) * a) % b

   snake_case_         =   bin_exp_mod(lowerCAmelCase_						,  n / 2						,  lowerCAmelCase_							)
   return (r * r) % b


if __name__ == "__main__":
   try:
      SCREAMING_SNAKE_CASE		:str											=       int(input('''Enter Base : ''').strip())
      SCREAMING_SNAKE_CASE		:List[Any]											=       int(input('''Enter Power : ''').strip())
      SCREAMING_SNAKE_CASE		:Dict											=       int(input('''Enter Modulo : ''').strip())
   except ValueError:
      print('''Invalid literal for integer''')

   print(bin_exp_mod(BASE, POWER, MODULO))




