



"""simple docstring"""
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_UpperCAmelCase       =     {
    """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        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase							,     lowercase	):
   for attribute in key.split("""."""	):
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =getattr(lowercase							,     lowercase	)

   if weight_type is not None:
      SCREAMING_SNAKE_CASE_:	str            =getattr(lowercase							,     lowercase	).shape
   else:
      SCREAMING_SNAKE_CASE_:	str            =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_:	Optional[int]            =value
   elif weight_type == "weight_g":
      SCREAMING_SNAKE_CASE_:	Any            =value
   elif weight_type == "weight_v":
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =value
   elif weight_type == "bias":
      SCREAMING_SNAKE_CASE_:	List[Any]            =value
   else:
      SCREAMING_SNAKE_CASE_:	Any            =value

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





def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Dict            =[]
   SCREAMING_SNAKE_CASE_:	Any            =fairseq_model.state_dict()

   SCREAMING_SNAKE_CASE_:	str            =hf_model.feature_extractor

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

            if key in name:
               SCREAMING_SNAKE_CASE_:	List[str]            =True
               if "*" in mapped_key:
                  SCREAMING_SNAKE_CASE_:	Any            =name.split(lowercase	)[0].split("""."""	)[-2]
                  SCREAMING_SNAKE_CASE_:	Optional[int]            =mapped_key.replace("""*"""							,     lowercase	)
               if "weight_g" in name:
                  SCREAMING_SNAKE_CASE_:	Union[str, Any]            ="""weight_g"""
               elif "weight_v" in name:
                  SCREAMING_SNAKE_CASE_:	Any            ="""weight_v"""
               elif "weight" in name:
                  SCREAMING_SNAKE_CASE_:	List[str]            ="""weight"""
               elif "bias" in name:
                  SCREAMING_SNAKE_CASE_:	List[str]            ="""bias"""
               else:
                  SCREAMING_SNAKE_CASE_:	Any            =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        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	List[Any]            =full_name.split("""conv_layers."""	)[-1]
   SCREAMING_SNAKE_CASE_:	List[Any]            =name.split("""."""	)
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =int(items[0]	)
   SCREAMING_SNAKE_CASE_:	List[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.'''
         )
         SCREAMING_SNAKE_CASE_:	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.'''
         )
         SCREAMING_SNAKE_CASE_:	List[str]            =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_:	List[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.'''
         )
         SCREAMING_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(lowercase	)





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	str            =HubertConfig()
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =model.config

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

   return config





@torch.no_grad()
def        __magic_name__			(							lowercase							,     lowercase=None	):
   SCREAMING_SNAKE_CASE_:	Any            =distilhubert().model.model

   if config_path is not None:
      SCREAMING_SNAKE_CASE_:	str            =HubertConfig.from_pretrained(lowercase	)
   else:
      SCREAMING_SNAKE_CASE_:	Tuple            =convert_config(lowercase	)
   SCREAMING_SNAKE_CASE_:	Any            =model.eval()

   SCREAMING_SNAKE_CASE_:	Any            =WavaVecaFeatureExtractor(
       feature_size=1							,     sampling_rate=1_6000							,     padding_value=0							,     do_normalize=lowercase							,     return_attention_mask=lowercase							,     )
   SCREAMING_SNAKE_CASE_:	List[str]            =HubertModel(lowercase	)

   recursively_load_weights(lowercase							,     lowercase	)

   feature_extractor.save_pretrained(lowercase	)
   hf_model.save_pretrained(lowercase	)


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








"""simple docstring"""
from __future__ import annotations

import sys
class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   int						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =letter
     SCREAMING_SNAKE_CASE_:	int            =freq
     SCREAMING_SNAKE_CASE_:	dict[str, str]            ={}





  def __repr__(  self	:   Optional[Any]						)       ->   str:


     '''simple docstring'''




     return f'''{self.letter}:{self.freq}'''







class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Letter | TreeNode    ,						lowerCAmelCase	:   Letter | TreeNode						)       ->   List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =freq
     SCREAMING_SNAKE_CASE_:	Letter | TreeNode            =left
     SCREAMING_SNAKE_CASE_:	Letter | TreeNode            =right







def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	dict[str, int]            ={}
   with open(lowercase	) as f:
      while True:
         SCREAMING_SNAKE_CASE_:	List[Any]            =f.read(1	)
         if not c:
            break
         SCREAMING_SNAKE_CASE_:	Any            =chars[c] + 1 if c in chars else 1
   return sorted((Letter(lowercase							,     lowercase	) for c, f in chars.items())							,     key=lambda lowercase	: x.freq	)





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	list[Letter | TreeNode]            =letters  # type: ignore
   while len(lowercase	) > 1:
      SCREAMING_SNAKE_CASE_:	Any            =response.pop(0	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =response.pop(0	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =left.freq + right.freq
      SCREAMING_SNAKE_CASE_:	Any            =TreeNode(lowercase							,     lowercase							,     lowercase	)
      response.append(lowercase	)
      response.sort(key=lambda lowercase	: x.freq	)
   return response[0]





def        __magic_name__			(							lowercase							,     lowercase	):
   if isinstance(lowercase							,     lowercase	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =bitstring
      return [root]
   SCREAMING_SNAKE_CASE_:	TreeNode            =root  # type: ignore
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[]
   letters += traverse_tree(treenode.left							,     bitstring + """0"""	)
   letters += traverse_tree(treenode.right							,     bitstring + """1"""	)
   return letters





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =parse_file(lowercase	)
   SCREAMING_SNAKE_CASE_:	str            =build_tree(lowercase	)
   SCREAMING_SNAKE_CASE_:	List[Any]            ={
       k: v for letter in traverse_tree(lowercase							,     """"""	) for k, v in letter.bitstring.items()
   }
   print(f'''Huffman Coding  of {file_path}: '''	)
   with open(lowercase	) as f:
      while True:
         SCREAMING_SNAKE_CASE_:	Any            =f.read(1	)
         if not c:
            break
         print(letters[c]							,     end=""" """	)
   print()


if __name__ == "__main__":
   # pass the file path to the huffman function
   huffman(sys.argv[1])



