


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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__UpperCAmelCase      			=  {
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """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 	_lowerCamelCase  (		A_  :	Dict							,  A_  :	Optional[Any]							,  A_  :	Tuple							,  A_  :	Tuple							,  A_  :	Any      )			->			Optional[Any]:

   '''simple docstring'''






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

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

   logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''      )
def 	_lowerCamelCase  (		A_  :	List[str]							,  A_  :	str							,  A_  :	Tuple      )			->			str:

   '''simple docstring'''






   UpperCamelCase__ :			Dict 						=[]
   UpperCamelCase__ :			Optional[int] 						=fairseq_model.state_dict()

   UpperCamelCase__ :			Union[str, Any] 						=hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

   logger.warning(f'''Unused weights: {unused_weights}'''      )
def 	_lowerCamelCase  (		A_  :	Union[str, Any]							,  A_  :	List[Any]							,  A_  :	Tuple							,  A_  :	List[str]							,  A_  :	Dict      )			->			Union[str, Any]:

   '''simple docstring'''






   UpperCamelCase__ :			int 						=full_name.split("conv_layers."      )[-1]
   UpperCamelCase__ :			Union[str, Any] 						=name.split("."      )
   UpperCamelCase__ :			int 						=int(items[0]      )
   UpperCamelCase__ :			List[str] 						=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__ :			List[str] 						=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__ :			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."
         )
         UpperCamelCase__ :			Union[str, 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.'''
         )
         UpperCamelCase__ :			List[str] 						=value
         logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
   else:
      unused_weights.append(A_      )
def 	_lowerCamelCase  (		A_  :	List[str]							,  A_  :	Tuple      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__ :			Any 						=SEWDConfig()
   if is_finetuned:
      UpperCamelCase__ :			Any 						=model.wav_encoder.wav_model.cfg
   else:
      UpperCamelCase__ :			List[str] 						=model.cfg

   UpperCamelCase__ :			Optional[int] 						=fs_config.conv_bias
   UpperCamelCase__ :			Optional[int] 						=eval(fs_config.conv_feature_layers      )
   UpperCamelCase__ :			str 						=[x[0] for x in conv_layers]
   UpperCamelCase__ :			Any 						=[x[1] for x in conv_layers]
   UpperCamelCase__ :			Dict 						=[x[2] for x in conv_layers]
   UpperCamelCase__ :			Dict 						="gelu"
   UpperCamelCase__ :			Any 						="layer" if fs_config.extractor_mode == "layer_norm" else "group"
   UpperCamelCase__ :			List[Any] 						=0.0
   UpperCamelCase__ :			str 						=fs_config.activation_fn.name
   UpperCamelCase__ :			Dict 						=fs_config.encoder_embed_dim
   UpperCamelCase__ :			Optional[int] 						=0.02
   UpperCamelCase__ :			Any 						=fs_config.encoder_ffn_embed_dim
   UpperCamelCase__ :			int 						=1E-5
   UpperCamelCase__ :			int 						=fs_config.encoder_layerdrop
   UpperCamelCase__ :			Any 						=fs_config.encoder_attention_heads
   UpperCamelCase__ :			List[Any] 						=fs_config.conv_pos_groups
   UpperCamelCase__ :			List[str] 						=fs_config.conv_pos
   UpperCamelCase__ :			List[Any] 						=len(A_      )
   UpperCamelCase__ :			Optional[Any] 						=fs_config.encoder_layers
   UpperCamelCase__ :			int 						=fs_config.squeeze_factor
   # DeBERTa-specific parameters:
   UpperCamelCase__ :			Optional[int] 						=fs_config.max_position_embeddings
   UpperCamelCase__ :			int 						=fs_config.position_buckets
   UpperCamelCase__ :			str 						=fs_config.share_att_key
   UpperCamelCase__ :			Any 						=fs_config.relative_attention
   UpperCamelCase__ :			str 						=fs_config.position_biased_input
   UpperCamelCase__ :			Optional[Any] 						=tuple(fs_config.pos_att_type.split("|"      )      )
   UpperCamelCase__ :			Optional[int] 						=fs_config.norm_rel_ebd

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      UpperCamelCase__ :			Optional[int] 						=model.cfg
      UpperCamelCase__ :			int 						=fs_config.final_dropout
      UpperCamelCase__ :			Optional[int] 						=fs_config.layerdrop
   UpperCamelCase__ :			List[str] 						=fs_config.activation_dropout
   UpperCamelCase__ :			Any 						=fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   UpperCamelCase__ :			List[Any] 						=fs_config.attention_dropout
   UpperCamelCase__ :			Tuple 						=fs_config.dropout_input
   UpperCamelCase__ :			Optional[int] 						=fs_config.dropout
   UpperCamelCase__ :			List[Any] 						=fs_config.mask_channel_length
   UpperCamelCase__ :			str 						=fs_config.mask_channel_prob
   UpperCamelCase__ :			Optional[Any] 						=fs_config.mask_length
   UpperCamelCase__ :			List[Any] 						=fs_config.mask_prob

   UpperCamelCase__ :			List[Any] 						="Wav2Vec2FeatureExtractor"
   UpperCamelCase__ :			str 						="Wav2Vec2CTCTokenizer"

   return config
@torch.no_grad()
def 	_lowerCamelCase  (		A_  :	Tuple							,  A_  :	Dict							,  A_  :	Tuple=None							,  A_  :	Tuple=None							,  A_  :	int=True      )			->			Union[str, Any]:

   '''simple docstring'''






   if is_finetuned:
      UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ :			List[Any] 						=fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]							,  arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
   else:
      UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ :			Union[str, Any] 						=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

   if config_path is not None:
      UpperCamelCase__ :			Union[str, Any] 						=SEWDConfig.from_pretrained(A_      )
   else:
      UpperCamelCase__ :			Tuple 						=convert_config(model[0]							,  A_      )
   UpperCamelCase__ :			Any 						=model[0].eval()

   UpperCamelCase__ :			Optional[Any] 						=True if config.feat_extract_norm == "layer" else False
   UpperCamelCase__ :			List[str] 						=WavaVecaFeatureExtractor(
       feature_size=1							,  sampling_rate=1_6_0_0_0							,  padding_value=0							,  do_normalize=A_							,  return_attention_mask=A_							,  )

   if is_finetuned:
      if dict_path:
         UpperCamelCase__ :			Optional[int] 						=Dictionary.load(A_      )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         UpperCamelCase__ :			List[str] 						=target_dict.pad_index
         UpperCamelCase__ :			Any 						=target_dict.bos_index
         UpperCamelCase__ :			str 						=target_dict.pad_index
         UpperCamelCase__ :			Optional[Any] 						=target_dict.bos_index
         UpperCamelCase__ :			List[Any] 						=target_dict.eos_index
         UpperCamelCase__ :			Any 						=len(target_dict.symbols      )
         UpperCamelCase__ :			Optional[int] 						=os.path.join(A_							,  "vocab.json"      )
         if not os.path.isdir(A_      ):
            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A_      )      )
            return
         os.makedirs(A_							,  exist_ok=A_      )
         with open(A_							,  "w"							,  encoding="utf-8"      ) as vocab_handle:
            json.dump(target_dict.indices							,  A_      )
         UpperCamelCase__ :			Tuple 						=WavaVecaCTCTokenizer(
             A_							,  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=A_							,  )
         UpperCamelCase__ :			Union[str, Any] 						=WavaVecaProcessor(feature_extractor=A_							,  tokenizer=A_      )
         processor.save_pretrained(A_      )

      UpperCamelCase__ :			Optional[int] 						=SEWDForCTC(A_      )
   else:
      UpperCamelCase__ :			List[str] 						=SEWDModel(A_      )
      feature_extractor.save_pretrained(A_      )

   recursively_load_weights(A_							,  A_							,  A_      )

   hf_model.save_pretrained(A_      )


if __name__ == "__main__":
     __UpperCAmelCase      			=  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      			=  parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )





def 	_lowerCamelCase  (		A_  :	Union[str, Any]							,  A_  :	Optional[int]      )			->			Dict:

   '''simple docstring'''






   if len(str(A_      )      ) == 1 or len(str(A_      )      ) == 1:
      return a * b

   UpperCamelCase__ :			Any 						=max(len(str(A_      )      )							,  len(str(A_      )      )      )
   UpperCamelCase__ :			Optional[int] 						=ma // 2

   UpperCamelCase__						,	UpperCamelCase__ :			Dict 						=divmod(A_							,  1_0**ma      )
   UpperCamelCase__						,	UpperCamelCase__ :			Union[str, Any] 						=divmod(A_							,  1_0**ma      )

   UpperCamelCase__ :			List[Any] 						=karatsuba(A_							,  A_      )
   UpperCamelCase__ :			Dict 						=karatsuba((aa + aa)							,  (ba + ba)      )
   UpperCamelCase__ :			Optional[int] 						=karatsuba(A_							,  A_      )

   return (z * 1_0 ** (2 * ma)) + ((y - z - x) * 1_0 ** (ma)) + (x)
def 	_lowerCamelCase  (		)			->			Any:

   '''simple docstring'''






   print(karatsuba(1_5_4_6_3							,  2_3_4_8_9      )      )


if __name__ == "__main__":
     main()

