


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 	_lowerCamelCase  (		A_  :	Optional[Any]							,  A_  :	List[str]							,  A_  :	Dict							,  A_  :	Union[str, Any]							,  A_  :	int      )			->			List[str]:

   '''simple docstring'''






   for attribute in key.split("."      ):
      UpperCamelCase__ :			List[Any] 						=getattr(A_							,  A_      )

   if weight_type is not None:
      UpperCamelCase__ :			Union[str, Any] 						=getattr(A_							,  A_      ).shape
   else:
      UpperCamelCase__ :			Union[str, 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":
      UpperCamelCase__ :			Tuple 						=value
   elif weight_type == "weight_g":
      UpperCamelCase__ :			Union[str, Any] 						=value
   elif weight_type == "weight_v":
      UpperCamelCase__ :			int 						=value
   elif weight_type == "bias":
      UpperCamelCase__ :			Tuple 						=value
   else:
      UpperCamelCase__ :			int 						=value

   logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''      )
def 	_lowerCamelCase  (		A_  :	int							,  A_  :	List[Any]      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=[]
   UpperCamelCase__ :			Tuple 						=fairseq_model.state_dict()

   UpperCamelCase__ :			Optional[Any] 						=hf_model.feature_extractor

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

            if key in name:
               UpperCamelCase__ :			Union[str, Any] 						=True
               if "*" in mapped_key:
                  UpperCamelCase__ :			int 						=name.split(A_      )[0].split("."      )[-2]
                  UpperCamelCase__ :			Optional[Any] 						=mapped_key.replace("*"							,  A_      )
               if "weight_g" in name:
                  UpperCamelCase__ :			List[str] 						="weight_g"
               elif "weight_v" in name:
                  UpperCamelCase__ :			int 						="weight_v"
               elif "weight" in name:
                  UpperCamelCase__ :			str 						="weight"
               elif "bias" in name:
                  UpperCamelCase__ :			Optional[int] 						="bias"
               else:
                  UpperCamelCase__ :			Tuple 						=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 	_lowerCamelCase  (		A_  :	List[str]							,  A_  :	List[Any]							,  A_  :	Any							,  A_  :	Tuple							,  A_  :	Tuple      )			->			Optional[int]:

   '''simple docstring'''






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

   '''simple docstring'''






   UpperCamelCase__ :			int 						=HubertConfig()
   UpperCamelCase__ :			Tuple 						=model.config

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

   return config
@torch.no_grad()
def 	_lowerCamelCase  (		A_  :	List[str]							,  A_  :	str=None      )			->			Optional[int]:

   '''simple docstring'''






   UpperCamelCase__ :			Tuple 						=distilhubert().model.model

   if config_path is not None:
      UpperCamelCase__ :			Optional[int] 						=HubertConfig.from_pretrained(A_      )
   else:
      UpperCamelCase__ :			Dict 						=convert_config(A_      )
   UpperCamelCase__ :			Dict 						=model.eval()

   UpperCamelCase__ :			List[str] 						=WavaVecaFeatureExtractor(
       feature_size=1							,  sampling_rate=1_6_0_0_0							,  padding_value=0							,  do_normalize=A_							,  return_attention_mask=A_							,  )
   UpperCamelCase__ :			List[Any] 						=HubertModel(A_      )

   recursively_load_weights(A_							,  A_      )

   feature_extractor.save_pretrained(A_      )
   hf_model.save_pretrained(A_      )


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)





from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap



@total_ordering
class 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						None:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=word
         UpperCamelCase__ :			Dict 						=count




      def __eq__(		self   ,			__SCREAMING_SNAKE_CASE)					->						bool:







         """simple docstring"""







         if not isinstance(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE):
            return NotImplemented
         return self.count == other.count




      def __lt__(		self   ,			__SCREAMING_SNAKE_CASE)					->						bool:







         """simple docstring"""







         if not isinstance(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE):
            return NotImplemented
         return self.count < other.count




def 	_lowerCamelCase  (		A_  :	list[str]							,  A_  :	int      )			->			list[str]:

   '''simple docstring'''






   UpperCamelCase__ :			Heap[WordCount] 						=Heap()
   UpperCamelCase__ :			Any 						=Counter(A_      )
   heap.build_max_heap(
       [WordCount(A_							,  A_      ) for word, count in count_by_word.items()]      )
   return [heap.extract_max().word for _ in range(min(A_							,  len(A_      )      )      )]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

