


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase		   =			logging.get_logger(__name__)

lowerCamelCase		   =			'▁'

lowerCamelCase		   =			{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

lowerCamelCase		   =			{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

lowerCamelCase		   =			{'vinai/bartpho-syllable': 10_24}




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    Optional[Any] =VOCAB_FILES_NAMES
      UpperCamelCase__				:    Optional[Any] =PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__				:    Union[str, Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__				:    Optional[int] =['input_ids', 'attention_mask']






      def __init__(   self		:	Optional[int]     ,  lowercase_		:	Any     ,  lowercase_		:	Union[str, Any]     ,  lowercase_		:	Tuple="<s>"     ,  lowercase_		:	List[Any]="</s>"     ,  lowercase_		:	Optional[int]="</s>"     ,  lowercase_		:	int="<s>"     ,  lowercase_		:	Dict="<unk>"     ,  lowercase_		:	Optional[Any]="<pad>"     ,  lowercase_		:	int="<mask>"     ,  lowercase_		:	Optional[Dict[str, Any]] = None     ,  **lowercase_		:	str     ,  )   ->		None:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =AddedToken(lowercase_     ,  lstrip=lowercase_     ,  rstrip=lowercase_					) if isinstance(lowercase_     ,  lowercase_					) else mask_token

             _lowerCamelCase			:						Union[str, Any]            ={} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=lowercase_     ,  eos_token=lowercase_     ,  unk_token=lowercase_     ,  sep_token=lowercase_     ,  cls_token=lowercase_     ,  pad_token=lowercase_     ,  mask_token=lowercase_     ,  sp_model_kwargs=self.sp_model_kwargs     ,  **lowercase_     ,  )

             _lowerCamelCase			:						Union[str, Any]            =vocab_file
             _lowerCamelCase			:						Tuple            =monolingual_vocab_file
             _lowerCamelCase			:						int            =spm.SentencePieceProcessor(**self.sp_model_kwargs					)
             self.sp_model.Load(str(lowercase_					)					)

             # Load the reduced vocab

             # Keep order of special tokens for backward compatibility
             _lowerCamelCase			:						Any            ={}
             _lowerCamelCase			:						Optional[int]            =0
             for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
                    if str(lowercase_					) not in self.fairseq_tokens_to_ids:
                           _lowerCamelCase			:						int            =cnt
                           cnt += 1
             with open(lowercase_     ,  'r'     ,  encoding='utf-8'					) as f:
                    for line in f.readlines():
                           _lowerCamelCase			:						Union[str, Any]            =line.strip().split()[0]
                           _lowerCamelCase			:						Optional[Any]            =len(self.fairseq_tokens_to_ids					)
             if str(lowercase_					) not in self.fairseq_tokens_to_ids:
                    _lowerCamelCase			:						List[str]            =len(self.fairseq_tokens_to_ids					)

             _lowerCamelCase			:						List[str]            ={v: k for k, v in self.fairseq_tokens_to_ids.items()}






      def __getstate__(   self		:	Optional[Any]					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =self.__dict__.copy()
             _lowerCamelCase			:						Any            =None
             _lowerCamelCase			:						Optional[Any]            =self.sp_model.serialized_model_proto()
             return state






      def __setstate__(   self		:	Tuple     ,  lowercase_		:	Tuple					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =d

             # for backward compatibility
             if not hasattr(self     ,  'sp_model_kwargs'					):
                    _lowerCamelCase			:						Union[str, Any]            ={}

             _lowerCamelCase			:						List[Any]            =spm.SentencePieceProcessor(**self.sp_model_kwargs					)
             self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	List[int]     ,  lowercase_		:	Optional[List[int]] = None					)   ->		List[int]:
             """simple docstring"""




             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             _lowerCamelCase			:						int            =[self.cls_token_id]
             _lowerCamelCase			:						List[str]            =[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	List[int]     ,  lowercase_		:	Optional[List[int]] = None     ,  lowercase_		:	bool = False					)   ->		List[int]:
             """simple docstring"""




             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=lowercase_     ,  token_ids_a=lowercase_     ,  already_has_special_tokens=lowercase_					)

             if token_ids_a is None:
                    return [1] + ([0] * len(lowercase_					)) + [1]
             return [1] + ([0] * len(lowercase_					)) + [1, 1] + ([0] * len(lowercase_					)) + [1]






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	List[int]     ,  lowercase_		:	Optional[List[int]] = None					)   ->		List[int]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =[self.sep_token_id]
             _lowerCamelCase			:						int            =[self.cls_token_id]

             if token_ids_a is None:
                    return len(cls + token_ids_a + sep					) * [0]
             return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]






      @property
      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[Any]:
             """simple docstring"""




             return len(self.fairseq_ids_to_tokens					)






      def 			lowerCamelCase			(   self		:	Any					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            ={self.convert_ids_to_tokens(lowercase_					): i for i in range(self.vocab_size					)}
             vocab.update(self.added_tokens_encoder					)
             return vocab






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	str					)   ->		List[str]:
             """simple docstring"""




             return self.sp_model.encode(lowercase_     ,  out_type=lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple     ,  lowercase_		:	int					)   ->		Any:
             """simple docstring"""




             if token in self.fairseq_tokens_to_ids:
                    return self.fairseq_tokens_to_ids[token]
             else:
                    return self.unk_token_id






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	Union[str, Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             return self.fairseq_ids_to_tokens[index]






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	Union[str, Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =''.join(lowercase_					).replace(lowercase_     ,  ' '					).strip()
             return out_string






      def 			lowerCamelCase			(   self		:	Dict     ,  lowercase_		:	str     ,  lowercase_		:	Optional[str] = None					)   ->		Tuple[str]:
             """simple docstring"""




             if not os.path.isdir(lowercase_					):
                    logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
                    return
             _lowerCamelCase			:						Optional[Any]            =os.path.join(
                 lowercase_     ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
             _lowerCamelCase			:						Tuple            =os.path.join(
                 lowercase_     ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']     ,  )

             if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase_					) and os.path.isfile(self.vocab_file					):
                    copyfile(self.vocab_file     ,  lowercase_					)
             elif not os.path.isfile(self.vocab_file					):
                    with open(lowercase_     ,  'wb'					) as fi:
                           _lowerCamelCase			:						Any            =self.sp_model.serialized_model_proto()
                           fi.write(lowercase_					)

             if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
                 lowercase_					) and os.path.isfile(self.monolingual_vocab_file					):
                    copyfile(self.monolingual_vocab_file     ,  lowercase_					)
             elif not os.path.isfile(self.monolingual_vocab_file					):
                    with open(lowercase_     ,  'w'     ,  encoding='utf-8'					) as fp:
                           for token in self.fairseq_tokens_to_ids:
                                  if token not in self.all_special_tokens:
                                         fp.write(F'''{str(lowercase_					)} \n'''					)

             return out_vocab_file, out_monolingual_vocab_file






import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

lowerCamelCase		   =			{
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
lowerCamelCase		   =			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def       a_ (       SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : Dict						,     SCREAMING_SNAKE_CASE__   : List[str]						,     SCREAMING_SNAKE_CASE__   : Optional[Any]						,     SCREAMING_SNAKE_CASE__   : Union[str, Any]	):
       '''simple docstring'''





       for attribute in key.split('.'	):
              _lowerCamelCase			:						Optional[Any]            =getattr(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)

       if weight_type is not None:
              _lowerCamelCase			:						str            =getattr(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	).shape
       else:
              _lowerCamelCase			:						Optional[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":
              _lowerCamelCase			:						Tuple            =value
       elif weight_type == "weight_g":
              _lowerCamelCase			:						Any            =value
       elif weight_type == "weight_v":
              _lowerCamelCase			:						Any            =value
       elif weight_type == "bias":
              _lowerCamelCase			:						Dict            =value
       else:
              _lowerCamelCase			:						Union[str, Any]            =value

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




def       a_ (       SCREAMING_SNAKE_CASE__   : int						,     SCREAMING_SNAKE_CASE__   : Optional[Any]	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =[]
       _lowerCamelCase			:						Optional[Any]            =fairseq_model.state_dict()

       _lowerCamelCase			:						Tuple            =hf_model.feature_extractor
       _lowerCamelCase			:						int            =hf_model.adapter

       for name, value in fairseq_dict.items():
              _lowerCamelCase			:						Optional[Any]            =False
              if "conv_layers" in name:
                     load_conv_layer(
                         SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     hf_model.config.feat_extract_norm == 'group'						,     )
                     _lowerCamelCase			:						Optional[int]            =True
              elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']	):
                     load_adapter(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
                     _lowerCamelCase			:						int            =True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split('w2v_model.'	)[-1] == name.split('.'	)[0]:
                                   _lowerCamelCase			:						Dict            =True
                                   if "*" in mapped_key:
                                          _lowerCamelCase			:						List[str]            =name.split(SCREAMING_SNAKE_CASE__	)[0].split('.'	)[-2]
                                          _lowerCamelCase			:						Dict            =mapped_key.replace('*'						,     SCREAMING_SNAKE_CASE__	)
                                   if "weight_g" in name:
                                          _lowerCamelCase			:						List[Any]            ='weight_g'
                                   elif "weight_v" in name:
                                          _lowerCamelCase			:						int            ='weight_v'
                                   elif "bias" in name:
                                          _lowerCamelCase			:						int            ='bias'
                                   elif "weight" in name:
                                          _lowerCamelCase			:						Optional[int]            ='weight'
                                   else:
                                          _lowerCamelCase			:						Any            =None
                                   set_recursively(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
                            continue
              if not is_used:
                     unused_weights.append(SCREAMING_SNAKE_CASE__	)

       logger.warning(F'''Unused weights: {unused_weights}'''	)




def       a_ (       SCREAMING_SNAKE_CASE__   : Optional[int]						,     SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : List[Any]						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Any	):
       '''simple docstring'''





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




def       a_ (       SCREAMING_SNAKE_CASE__   : Optional[Any]						,     SCREAMING_SNAKE_CASE__   : List[str]						,     SCREAMING_SNAKE_CASE__   : Optional[int]						,     SCREAMING_SNAKE_CASE__   : Dict	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =full_name.split('adaptor.'	)[-1]
       _lowerCamelCase			:						str            =name.split('.'	)

       if items[1].isdigit():
              _lowerCamelCase			:						Any            =int(items[1]	)
       else:
              _lowerCamelCase			:						int            =None

       if "adaptor" not in full_name:
              if "proj_ln" in full_name:
                     # has to be layer norm
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.bias.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
                            _lowerCamelCase			:						str            =value
                            logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.'''	)
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.weight.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
                            _lowerCamelCase			:						str            =value
              else:
                     # has to be projection layer
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj.bias.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
                            _lowerCamelCase			:						Optional[Any]            =value
                            logger.info(F'''Adapter proj layer bias was initialized from {full_name}.'''	)
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj.weight.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
                            _lowerCamelCase			:						Optional[Any]            =value
                            logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''	)
       elif isinstance(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	):
              if "bias" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.bias.data.shape
                     ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
                     _lowerCamelCase			:						List[Any]            =value
                     logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''	)
              elif "weight" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.weight.data.shape
                     ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
                     _lowerCamelCase			:						Dict            =value
                     logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''	)
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE__	)




def       a_ (       SCREAMING_SNAKE_CASE__   : Tuple	):
       '''simple docstring'''





       _lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =emb.weight.shape
       _lowerCamelCase			:						str            =nn.Linear(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__						,     bias=SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						Optional[Any]            =emb.weight.data
       return lin_layer




@torch.no_grad()
def       a_ (       SCREAMING_SNAKE_CASE__   : int						,     SCREAMING_SNAKE_CASE__   : List[str]						,     SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : List[Any]						,     SCREAMING_SNAKE_CASE__   : List[Any]						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : Dict						,     SCREAMING_SNAKE_CASE__   : str						,     SCREAMING_SNAKE_CASE__   : Any						,     SCREAMING_SNAKE_CASE__   : List[Any]						,     ):
       '''simple docstring'''





       _lowerCamelCase			:						int            =WavaVecaConfig.from_pretrained(
           SCREAMING_SNAKE_CASE__						,     add_adapter=SCREAMING_SNAKE_CASE__						,     adapter_stride=SCREAMING_SNAKE_CASE__						,     adapter_kernel_size=SCREAMING_SNAKE_CASE__						,     use_auth_token=SCREAMING_SNAKE_CASE__						,     output_hidden_size=SCREAMING_SNAKE_CASE__						,     )
       _lowerCamelCase			:						str            =MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__	)

       # load model
       _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						str            =fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]						,     arg_overrides={
               'config_yaml': config_yaml_path,
               'data': '/'.join(dict_path.split('/'	)[:-1]	),
               'w2v_path': checkpoint_path,
               'load_pretrained_decoder_from': None,
           }						,     )
       _lowerCamelCase			:						Any            =model[0].eval()

       # load feature extractor
       _lowerCamelCase			:						List[Any]            =WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__						,     use_auth_token=SCREAMING_SNAKE_CASE__	)

       # set weights for wav2vec2 encoder
       _lowerCamelCase			:						List[Any]            =WavaVecaModel(SCREAMING_SNAKE_CASE__	)

       recursively_load_weights_wavaveca(model.encoder						,     SCREAMING_SNAKE_CASE__	)

       # load decoder weights
       _lowerCamelCase			:						int            =MBartForCausalLM(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,     strict=SCREAMING_SNAKE_CASE__	)
       logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}'''	)
       logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''	)

       _lowerCamelCase			:						Dict            =SpeechEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE__						,     decoder=SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						List[Any]            =False

       _lowerCamelCase			:						Any            =MBartaaTokenizer(SCREAMING_SNAKE_CASE__	)
       tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__	)

       _lowerCamelCase			:						int            =hf_wavavec.config.to_dict()
       _lowerCamelCase			:						Optional[int]            =tokenizer.pad_token_id
       _lowerCamelCase			:						Tuple            =tokenizer.bos_token_id
       _lowerCamelCase			:						Any            =tokenizer.eos_token_id
       _lowerCamelCase			:						int            ='mbart50'
       _lowerCamelCase			:						Tuple            ='wav2vec2'

       _lowerCamelCase			:						Tuple            =tokenizer.eos_token_id
       _lowerCamelCase			:						Dict            =250_004
       _lowerCamelCase			:						List[str]            =tokenizer.eos_token_id

       _lowerCamelCase			:						Dict            =SpeechEncoderDecoderConfig.from_dict(SCREAMING_SNAKE_CASE__	)

       hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__	)
       feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
 lowerCamelCase		   =			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_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
 parser.add_argument(
     '--encoder_config_path',
     default='facebook/wav2vec2-xls-r-1b',
     type=str,
     help='Path to hf encoder wav2vec2 checkpoint config',
 )
 parser.add_argument(
     '--decoder_config_path',
     default='facebook/mbart-large-50-one-to-many-mmt',
     type=str,
     help='Path to hf decoder checkpoint config',
 )
 parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
 parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
 parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
 parser.add_argument('--encoder_output_dim', default=10_24, type=int, help='encoder output dim')
 parser.add_argument('--start_token_id', default=25_00_04, type=int, help='`decoder_start_token_id` of model config')

 lowerCamelCase		   =			parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     args.config_yaml_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     add_adapter=args.add_adapter,
     adapter_kernel_size=args.adapter_kernel_size,
     adapter_stride=args.adapter_stride,
     decoder_start_token_id=args.start_token_id,
     encoder_output_dim=args.encoder_output_dim,
 )


