




'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_							:	Optional[Any]	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	str	     =		{
    '''post_extract_proj''': '''feature_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.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}







def       _UpperCamelCase			(_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  List[Any]						)-> str:




     '''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       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  int ,						_lowerCamelCase				:  Optional[int]						)-> List[str]:




     '''simple docstring'''





     __snake_case            =	[]
     __snake_case            =	fairseq_model.state_dict()

     __snake_case            =	hf_model.sew.feature_extractor if is_finetuned else 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            =	'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                    if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
                         __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       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  Optional[int]						)-> Optional[Any]:




     '''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       _UpperCamelCase			(_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  List[Any]						)-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	SEWConfig()
     if is_finetuned:
          __snake_case            =	model.wav_encoder.wav_model.cfg
     else:
          __snake_case            =	model.cfg

     __snake_case            =	fs_config.conv_bias
     __snake_case            =	eval(fs_config.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            =	0.0
     __snake_case            =	fs_config.activation_fn.name
     __snake_case            =	fs_config.encoder_embed_dim
     __snake_case            =	0.02
     __snake_case            =	fs_config.encoder_ffn_embed_dim
     __snake_case            =	1E-5
     __snake_case            =	fs_config.encoder_layerdrop
     __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
     __snake_case            =	fs_config.squeeze_factor

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          __snake_case            =	model.cfg
          __snake_case            =	fs_config.final_dropout
          __snake_case            =	fs_config.layerdrop
     __snake_case            =	fs_config.activation_dropout
     __snake_case            =	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     __snake_case            =	fs_config.attention_dropout
     __snake_case            =	fs_config.dropout_input
     __snake_case            =	fs_config.dropout
     __snake_case            =	fs_config.mask_channel_length
     __snake_case            =	fs_config.mask_channel_prob
     __snake_case            =	fs_config.mask_length
     __snake_case            =	fs_config.mask_prob

     __snake_case            =	'''Wav2Vec2FeatureExtractor'''
     __snake_case            =	'''Wav2Vec2CTCTokenizer'''

     return config







@torch.no_grad()
def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  Tuple=None ,						_lowerCamelCase				:  Optional[int]=None ,						_lowerCamelCase				:  Optional[Any]=True						)-> int:




     '''simple docstring'''





     if is_finetuned:
          __snake_case       ,     __snake_case       ,     __snake_case            =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path] ,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
     else:
          __snake_case       ,     __snake_case       ,     __snake_case            =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

     if config_path is not None:
          __snake_case            =	SEWConfig.from_pretrained(_lowerCamelCase						)
     else:
          __snake_case            =	convert_config(model[0] ,						_lowerCamelCase						)
     __snake_case            =	model[0].eval()

     __snake_case            =	True if config.feat_extract_norm == '''layer''' else False
     __snake_case            =	WavaVecaFeatureExtractor(
         feature_size=1 ,						sampling_rate=1_60_00 ,						padding_value=0 ,						do_normalize=_lowerCamelCase ,						return_attention_mask=_lowerCamelCase ,						)

     if is_finetuned:
          if dict_path:
               __snake_case            =	Dictionary.load(_lowerCamelCase						)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               __snake_case            =	target_dict.pad_index
               __snake_case            =	target_dict.bos_index
               __snake_case            =	target_dict.pad_index
               __snake_case            =	target_dict.bos_index
               __snake_case            =	target_dict.eos_index
               __snake_case            =	len(target_dict.symbols						)
               __snake_case            =	os.path.join(_lowerCamelCase ,						'''vocab.json'''						)
               if not os.path.isdir(_lowerCamelCase						):
                    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowerCamelCase						)						)
                    return
               os.makedirs(_lowerCamelCase ,						exist_ok=_lowerCamelCase						)
               with open(_lowerCamelCase ,						'''w''' ,						encoding='''utf-8'''						) as vocab_handle:
                    json.dump(target_dict.indices ,						_lowerCamelCase						)
               __snake_case            =	WavaVecaCTCTokenizer(
                   _lowerCamelCase ,						unk_token=target_dict.unk_word ,						pad_token=target_dict.pad_word ,						bos_token=target_dict.bos_word ,						eos_token=target_dict.eos_word ,						word_delimiter_token='''|''' ,						do_lower_case=_lowerCamelCase ,						)
               __snake_case            =	WavaVecaProcessor(feature_extractor=_lowerCamelCase ,						tokenizer=_lowerCamelCase						)
               processor.save_pretrained(_lowerCamelCase						)

          __snake_case            =	SEWForCTC(_lowerCamelCase						)
     else:
          __snake_case            =	SEWModel(_lowerCamelCase						)
          feature_extractor.save_pretrained(_lowerCamelCase						)

     recursively_load_weights(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)

     hf_model.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
     UpperCAmelCase_							:	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('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
     parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
     parser.add_argument(
         '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
     )
     UpperCAmelCase_							:	str	     =		parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )












'''simple docstring'''



class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						val
             SCREAMING_SNAKE_CASE   :      Any    					=						None
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						None







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if self.val:
                   if val < self.val:
                         if self.left is None:
                               SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Node(A  )
                         else:
                               self.left.insert(A  )
                   elif val > self.val:
                         if self.right is None:
                               SCREAMING_SNAKE_CASE   :      int    					=						Node(A  )
                         else:
                               self.right.insert(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						val




def   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      if root:
            inorder(root.left      ,__UpperCamelCase    )
            res.append(root.val    )
            inorder(root.right      ,__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      if len(__UpperCamelCase    ) == 0:
            return arr
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Node(arr[0]    )
      for i in range(1      ,len(__UpperCamelCase    )    ):
            root.insert(arr[i]    )
      # Traverse BST in order.
      SCREAMING_SNAKE_CASE   :      Dict    					=						[]
      inorder(__UpperCamelCase      ,__UpperCamelCase    )
      return res


if __name__ == "__main__":
      print(tree_sort([1_0, 1, 3, 2, 9, 1_4, 1_3]))


