






"""simple docstring"""





def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       if not root or root not in binary_tree_mirror_dictionary:
              return
       lowercase__    ,      lowercase__:							Tuple         =     binary_tree_mirror_dictionary[root][:2]
       lowercase__:							Optional[Any]         =     [right_child, left_child]
       binary_tree_mirror_dict(snake_case_		,  snake_case_						)
       binary_tree_mirror_dict(snake_case_		,  snake_case_						)






def 							A(    snake_case_		,  snake_case_ = 1						):
       """simple docstring"""





       if not binary_tree:
              raise ValueError("binary tree cannot be empty"						)
       if root not in binary_tree:
              lowercase__:							Any         =     F"""root {root} is not present in the binary_tree"""
              raise ValueError(snake_case_						)
       lowercase__:							int         =     dict(snake_case_						)
       binary_tree_mirror_dict(snake_case_		,  snake_case_						)
       return binary_tree_mirror_dictionary


if __name__ == "__main__":
  UpperCamelCase					=     {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F"Binary tree: {binary_tree}")
  UpperCamelCase					=     binary_tree_mirror(binary_tree, 5)
  print(F"Binary tree mirror: {binary_tree_mirror_dictionary}")







'''simple docstring'''

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCAmelCase_			:			Optional[Any]    	=   {
    "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 __a			(						__lowerCamelCase      :    Dict			,		__lowerCamelCase      :    Optional[int]			,		__lowerCamelCase      :    str			,		__lowerCamelCase      :    int			,		__lowerCamelCase      :    Union[str, Any]					)						->				Optional[Any]:
      '''simple docstring'''


      for attribute in key.split("."					):
            lowercase_     			=   getattr(__lowerCamelCase			,		__lowerCamelCase					)

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

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


def __a			(						__lowerCamelCase      :    List[str]			,		__lowerCamelCase      :    Optional[int]					)						->				List[Any]:
      '''simple docstring'''


      lowercase_     			=   []
      lowercase_     			=   fairseq_model.state_dict()

      lowercase_     			=   hf_model.feature_extractor

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

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


      lowercase_     			=   full_name.split("conv_layers."					)[-1]
      lowercase_     			=   name.split("."					)
      lowercase_     			=   int(items[0]					)
      lowercase_     			=   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.'
                  )
                  lowercase_     			=   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.'
                  )
                  lowercase_     			=   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."
                  )
                  lowercase_     			=   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.'
                  )
                  lowercase_     			=   value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
      else:
            unused_weights.append(__lowerCamelCase					)


def __a			(						__lowerCamelCase      :    Optional[int]					)						->				Dict:
      '''simple docstring'''


      lowercase_     			=   HubertConfig()
      lowercase_     			=   model.config

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

      return config


@torch.no_grad()
def __a			(						__lowerCamelCase      :    Optional[Any]			,		__lowerCamelCase      :    Any=None					)						->				Any:
      '''simple docstring'''


      lowercase_     			=   distilhubert().model.model

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

      lowercase_     			=   WavaVecaFeatureExtractor(
          feature_size=1			,		sampling_rate=16_000			,		padding_value=0			,		do_normalize=__lowerCamelCase			,		return_attention_mask=__lowerCamelCase			,		)
      lowercase_     			=   HubertModel(__lowerCamelCase					)

      recursively_load_weights(__lowerCamelCase			,		__lowerCamelCase					)

      feature_extractor.save_pretrained(__lowerCamelCase					)
      hf_model.save_pretrained(__lowerCamelCase					)


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






