


'''simple docstring'''

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__lowercase:		List[str]							=     logging.get_logger(__name__)

__lowercase:		Optional[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      :				Tuple    ,		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				Optional[Any]    ,		_UpperCamelCase      :				Tuple   )		->					List[Any]:





   '''simple docstring'''







   for attribute in key.split("."   ):
      UpperCamelCase__ 						=						getattr(_UpperCamelCase    ,		_UpperCamelCase   )

   if weight_type is not None:
      UpperCamelCase__ 						=						getattr(_UpperCamelCase    ,		_UpperCamelCase   ).shape
   else:
      UpperCamelCase__ 						=						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__ 						=						value
   elif weight_type == "weight_g":
      UpperCamelCase__ 						=						value
   elif weight_type == "weight_v":
      UpperCamelCase__ 						=						value
   elif weight_type == "bias":
      UpperCamelCase__ 						=						value
   else:
      UpperCamelCase__ 						=						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      :				Tuple   )		->					Optional[Any]:





   '''simple docstring'''







   UpperCamelCase__ 						=						[]
   UpperCamelCase__ 						=						fairseq_model.state_dict()

   UpperCamelCase__ 						=						hf_model.feature_extractor

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

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





   '''simple docstring'''







   UpperCamelCase__ 						=						full_name.split("conv_layers."   )[-1]
   UpperCamelCase__ 						=						name.split("."   )
   UpperCamelCase__ 						=						int(items[0]   )
   UpperCamelCase__ 						=						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__ 						=						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__ 						=						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__ 						=						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__ 						=						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__(		_UpperCamelCase      :				Optional[Any]   )		->					Any:





   '''simple docstring'''







   UpperCamelCase__ 						=						HubertConfig()
   UpperCamelCase__ 						=						model.config

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

   return config



@torch.no_grad()
def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				Dict=None   )		->					Optional[int]:





   '''simple docstring'''







   UpperCamelCase__ 						=						distilhubert().model.model

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

   UpperCamelCase__ 						=						WavaVecaFeatureExtractor(
       feature_size=1    ,		sampling_rate=1_60_00    ,		padding_value=0    ,		do_normalize=_UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		)
   UpperCamelCase__ 						=						HubertModel(_UpperCamelCase   )

   recursively_load_weights(_UpperCamelCase    ,		_UpperCamelCase   )

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


if __name__ == "__main__":
       __lowercase:		Optional[int]							=     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")
       __lowercase:		Dict							=     parser.parse_args()
       convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)



'''simple docstring'''

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

__lowercase:		Optional[Any]							=     TypeVar("T", bound=bool)







class  UpperCAmelCase (       Generic[T]):

 def __init__( self :	int,					a_ :	T					):





    """simple docstring"""



    UpperCamelCase__ 						=						value
    UpperCamelCase__ 						=						None
    UpperCamelCase__ 						=						None

 @property
 def 		lowercase_						( self :	Dict					):





    """simple docstring"""



    return self._value

 @staticmethod
 def 		lowercase_						( a_ :	RandomizedHeapNode[T] | None,					a_ :	RandomizedHeapNode[T] | None					):





    """simple docstring"""



    if not roota:
       return roota

    if not roota:
       return roota

    if roota.value > roota.value:
       UpperCamelCase__						,		UpperCamelCase__ 						=						roota, roota

    if random.choice([True, False]					):
       UpperCamelCase__						,		UpperCamelCase__ 						=						roota.right, roota.left

    UpperCamelCase__ 						=						RandomizedHeapNode.merge(roota.left,					a_					)

    return roota







class  UpperCAmelCase (       Generic[T]):

 def __init__( self :	List[Any],					a_ :	Iterable[T] | None = ()					):





    """simple docstring"""



    UpperCamelCase__ 						=						None

    if data:
       for item in data:
          self.insert(a_					)

 def 		lowercase_						( self :	Any,					a_ :	T					):





    """simple docstring"""



    UpperCamelCase__ 						=						RandomizedHeapNode.merge(self._root,					RandomizedHeapNode(a_					)					)

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.top()

    if self._root is None:
       return None

    UpperCamelCase__ 						=						RandomizedHeapNode.merge(self._root.left,					self._root.right					)

    return result

 def 		lowercase_						( self :	Union[str, Any]					):





    """simple docstring"""



    if not self._root:
       raise IndexError("Can't get top element for the empty heap."					)
    return self._root.value

 def 		lowercase_						( self :	Tuple					):





    """simple docstring"""



    UpperCamelCase__ 						=						None

 def 		lowercase_						( self :	int					):





    """simple docstring"""



    UpperCamelCase__ 						=						[]
    while self:
       result.append(self.pop()					)

    return result

 def __bool__( self :	List[Any]					):





    """simple docstring"""



    return self._root is not None


if __name__ == "__main__":
       import doctest

       doctest.testmod()