





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_lowerCAmelCase							:					int		    =		logging.get_logger(__name__)

_lowerCAmelCase							:					List[str]		    =		{
    '''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							:					Union[str, Any]		    =		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def    __snake_case  (    _lowerCAmelCase       :       Any , _lowerCAmelCase       :       Optional[int] , _lowerCAmelCase       :       List[str] , _lowerCAmelCase       :       Tuple , _lowerCAmelCase       :       int		)       ->  Union[str, Any]:
   for attribute in key.split("."		):
      A_    : Optional[Any]								=	getattr(lowerCAmelCase__ , lowerCAmelCase__		)

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

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

def    __snake_case  (    _lowerCAmelCase       :       List[str] , _lowerCAmelCase       :       int		)       ->  int:
   A_    : List[Any]								=	[]
   A_    : List[str]								=	fairseq_model.state_dict()

   A_    : int								=	hf_model.feature_extractor

   # if encoder has different dim to decoder -> use proj_weight
   A_    : Tuple								=	None

   for name, value in fairseq_dict.items():
      A_    : Dict								=	False
      if "conv_layers" in name:
         load_conv_layer(
             lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , hf_model.config.feat_extract_norm == "group" , )
         A_    : Any								=	True
      elif name.split("."		)[0] == "proj":
         A_    : Any								=	fairseq_model.proj
         A_    : List[str]								=	True
      else:
         for key, mapped_key in MAPPING.items():
            if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
               A_    : str								=	True
               if "*" in mapped_key:
                  A_    : Tuple								=	name.split(lowerCAmelCase__		)[0].split("."		)[-2]
                  A_    : Any								=	mapped_key.replace("*" , lowerCAmelCase__		)
               if "weight_g" in name:
                  A_    : Tuple								=	"weight_g"
               elif "weight_v" in name:
                  A_    : Dict								=	"weight_v"
               elif "bias" in name:
                  A_    : str								=	"bias"
               elif "weight" in name:
                  A_    : List[Any]								=	"weight"
               else:
                  A_    : Dict								=	None
               set_recursively(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__		)
            continue
      if not is_used:
         unused_weights.append(lowerCAmelCase__		)

   logger.warning(f"Unused weights: {unused_weights}"		)

   return proj_weight

def    __snake_case  (    _lowerCAmelCase       :       str , _lowerCAmelCase       :       Optional[Any] , _lowerCAmelCase       :       str , _lowerCAmelCase       :       str , _lowerCAmelCase       :       Any		)       ->  str:
   A_    : Dict								=	full_name.split("conv_layers."		)[-1]
   A_    : Union[str, Any]								=	name.split("."		)
   A_    : str								=	int(items[0]		)
   A_    : Dict								=	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."
         )
         A_    : Tuple								=	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."
         )
         A_    : List[Any]								=	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."
         )
         A_    : 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."
         )
         A_    : 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(lowerCAmelCase__		)

def    __snake_case  (    _lowerCAmelCase       :       int		)       ->  List[str]:
   A_					,  A_    : Dict								=	emb.weight.shape
   A_    : Any								=	nn.Linear(lowerCAmelCase__ , lowerCAmelCase__ , bias=lowerCAmelCase__		)
   A_    : Optional[Any]								=	emb.weight.data
   return lin_layer

def    __snake_case  (    _lowerCAmelCase       :       Optional[Any]		)       ->  Tuple:
   with open(lowerCAmelCase__ , "r" , encoding="utf-8"		) as f:
      A_    : Any								=	f.readlines()
      A_    : Optional[int]								=	[line.split(" "		)[0] for line in lines]

   A_    : List[Any]								=	len(lowerCAmelCase__		)

   A_    : Optional[int]								=	{
       "<s>": 0,
       "<pad>": 1,
       "</s>": 2,
       "<unk>": 3,
   }

   vocab_dict.update(dict(zip(lowerCAmelCase__ , range(4 , num_words + 4		)		)		)		)
   return vocab_dict

@torch.no_grad()
def    __snake_case  (    _lowerCAmelCase       :       List[Any] , _lowerCAmelCase       :       int , _lowerCAmelCase       :       Optional[int] , _lowerCAmelCase       :       str , _lowerCAmelCase       :       Optional[int] , _lowerCAmelCase       :       int , _lowerCAmelCase       :       Union[str, Any] , )       ->  Union[str, Any]:
   A_    : List[Any]								=	WavaVecaConfig.from_pretrained(lowerCAmelCase__		)
   A_    : str								=	SpeechaTextaConfig.from_pretrained(
       lowerCAmelCase__ , vocab_size=lowerCAmelCase__ , decoder_layers=lowerCAmelCase__ , do_stable_layer_norm=lowerCAmelCase__		)

   A_    : List[str]								=	WavaVecaFeatureExtractor(
       feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , )

   A_					,  A_					,  A_    : List[str]								=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
       [checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
   A_    : int								=	model[0].eval()

   # set weights for wav2vec2 encoder
   A_    : Any								=	WavaVecaModel(lowerCAmelCase__		)
   A_    : Any								=	recursively_load_weights_wavaveca(model.encoder , lowerCAmelCase__		)

   A_    : List[str]								=	SpeechaTextaForCausalLM(lowerCAmelCase__		)
   A_					,  A_    : Tuple								=	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=lowerCAmelCase__		)

   # set output linear layer
   unexpected_keys.remove("embed_out"		)
   A_    : str								=	nn.Parameter(model.decoder.embed_out.detach()		)

   # layer norm is init to identity matrix so leaving it is fine
   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}"		)

   A_    : Any								=	SpeechEncoderDecoderModel(encoder=lowerCAmelCase__ , decoder=lowerCAmelCase__		)
   A_    : Dict								=	False

   # add projection layer
   A_    : int								=	nn.Parameter(projection_layer.weight		)
   A_    : Any								=	nn.Parameter(projection_layer.bias		)

   A_    : Optional[int]								=	create_vocab_dict(lowerCAmelCase__		)

   with open(os.path.join(lowerCAmelCase__ , "vocab.json"		) , "w"		) as fp:
      json.dump(lowerCAmelCase__ , lowerCAmelCase__		)

   A_    : str								=	SpeechaTextaTokenizer(os.path.join(lowerCAmelCase__ , "vocab.json"		)		)
   tokenizer.save_pretrained(lowerCAmelCase__		)

   A_    : Optional[int]								=	hf_wavavec.config.to_dict()
   A_    : Tuple								=	tokenizer.pad_token_id
   A_    : Dict								=	tokenizer.bos_token_id
   A_    : List[str]								=	tokenizer.eos_token_id
   A_    : List[Any]								=	"speech_to_text_2"
   A_    : int								=	"wav2vec2"

   A_    : int								=	SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase__		)

   hf_wavavec.save_pretrained(lowerCAmelCase__		)
   feature_extractor.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
 _lowerCAmelCase							:					List[str]		    =		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(
     '''--encoder_config_path''',
     default='''facebook/wav2vec2-large-lv60''',
     type=str,
     help='''Path to hf encoder wav2vec2 checkpoint config''',
 )
 parser.add_argument(
     '''--decoder_config_path''',
     default='''facebook/s2t-small-mustc-en-fr-st''',
     type=str,
     help='''Path to hf decoder s2t checkpoint config''',
 )
 parser.add_argument('''--vocab_size''', default=10_224, type=int, help='''Vocab size of decoder''')
 parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

 _lowerCAmelCase							:					Tuple		    =		parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     vocab_size=args.vocab_size,
     num_decoder_layers=args.num_decoder_layers,
 )




"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     list[str]			) ->      str:
		__a           =			''''''
		for word_or_phrase in separated:
				if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
						raise Exception('''join() accepts only strings to be joined'''			)
				joined += word_or_phrase + separator
		return joined.strip(lowerCAmelCase__			)


if __name__ == "__main__":
				from doctest import testmod

				testmod()






