





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()
__lowerCamelCase       :     str   =     logging.get_logger(__name__)

__lowerCamelCase       :     Optional[Any]   =     {
    """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 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Tuple:
 for attribute in key.split("."     ):
  UpperCamelCase	:					Union[str, Any]       	=   getattr(_lowerCAmelCase							,   _lowerCAmelCase     )

 if weight_type is not None:
  UpperCamelCase	:					str       	=   getattr(_lowerCAmelCase							,   _lowerCAmelCase     ).shape
 else:
  UpperCamelCase	:					str       	=   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	:					Dict       	=   value
 elif weight_type == "weight_g":
  UpperCamelCase	:					Optional[int]       	=   value
 elif weight_type == "weight_v":
  UpperCamelCase	:					Optional[int]       	=   value
 elif weight_type == "bias":
  UpperCamelCase	:					List[str]       	=   value
 else:
  UpperCamelCase	:					Dict       	=   value

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



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 UpperCamelCase	:					Optional[Any]       	=   []
 UpperCamelCase	:					Union[str, Any]       	=   fairseq_model.state_dict()

 UpperCamelCase	:					int       	=   hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCamelCase	:					List[Any]       	=   False
  if "conv_layers" in name:
   load_conv_layer(
       _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   hf_model.config.feat_extract_norm == "group"							,   )
   UpperCamelCase	:					Dict       	=   True
  else:
   for key, mapped_key in MAPPING.items():
    UpperCamelCase	:					Any       	=   "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]:
     UpperCamelCase	:					Any       	=   True
     if "*" in mapped_key:
      UpperCamelCase	:					Tuple       	=   name.split(_lowerCAmelCase     )[0].split("."     )[-2]
      UpperCamelCase	:					Optional[int]       	=   mapped_key.replace("*"							,   _lowerCAmelCase     )
     if "weight_g" in name:
      UpperCamelCase	:					int       	=   "weight_g"
     elif "weight_v" in name:
      UpperCamelCase	:					Optional[Any]       	=   "weight_v"
     elif "weight" in name:
      UpperCamelCase	:					int       	=   "weight"
     elif "bias" in name:
      UpperCamelCase	:					str       	=   "bias"
     else:
      UpperCamelCase	:					Any       	=   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							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Any:
 UpperCamelCase	:					Any       	=   full_name.split("conv_layers."     )[-1]
 UpperCamelCase	:					Tuple       	=   name.split("."     )
 UpperCamelCase	:					Dict       	=   int(items[0]     )
 UpperCamelCase	:					Union[str, 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."""
   )
   UpperCamelCase	:					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."""
   )
   UpperCamelCase	:					Dict       	=   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	:					Optional[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	:					Tuple       	=   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							,   _lowerCAmelCase     )						-> Union[str, Any]:
 UpperCamelCase	:					List[Any]       	=   SEWConfig()
 if is_finetuned:
  UpperCamelCase	:					int       	=   model.wav_encoder.wav_model.cfg
 else:
  UpperCamelCase	:					Dict       	=   model.cfg

 UpperCamelCase	:					int       	=   fs_config.conv_bias
 UpperCamelCase	:					Dict       	=   eval(fs_config.conv_feature_layers     )
 UpperCamelCase	:					Union[str, Any]       	=   [x[0] for x in conv_layers]
 UpperCamelCase	:					Union[str, Any]       	=   [x[1] for x in conv_layers]
 UpperCamelCase	:					Union[str, Any]       	=   [x[2] for x in conv_layers]
 UpperCamelCase	:					int       	=   "gelu"
 UpperCamelCase	:					List[Any]       	=   "layer" if fs_config.extractor_mode == "layer_norm" else "group"
 UpperCamelCase	:					Union[str, Any]       	=   0.0
 UpperCamelCase	:					List[str]       	=   fs_config.activation_fn.name
 UpperCamelCase	:					Any       	=   fs_config.encoder_embed_dim
 UpperCamelCase	:					Optional[int]       	=   0.02
 UpperCamelCase	:					str       	=   fs_config.encoder_ffn_embed_dim
 UpperCamelCase	:					List[str]       	=   1e-5
 UpperCamelCase	:					Optional[Any]       	=   fs_config.encoder_layerdrop
 UpperCamelCase	:					Any       	=   fs_config.encoder_attention_heads
 UpperCamelCase	:					List[Any]       	=   fs_config.conv_pos_groups
 UpperCamelCase	:					Any       	=   fs_config.conv_pos
 UpperCamelCase	:					Dict       	=   len(_lowerCAmelCase     )
 UpperCamelCase	:					str       	=   fs_config.encoder_layers
 UpperCamelCase	:					List[str]       	=   fs_config.squeeze_factor

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

 UpperCamelCase	:					Union[str, Any]       	=   "Wav2Vec2FeatureExtractor"
 UpperCamelCase	:					Optional[int]       	=   "Wav2Vec2CTCTokenizer"

 return config



@torch.no_grad()
def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase=None							,   _lowerCAmelCase=None							,   _lowerCAmelCase=True     )						-> Optional[Any]:

 if is_finetuned:
  UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					Dict       	=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]							,   arg_overrides={"data": "/".join(dict_path.split("/"     )[:-1]     )}     )
 else:
  UpperCamelCase	,    UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

 if config_path is not None:
  UpperCamelCase	:					Dict       	=   SEWConfig.from_pretrained(_lowerCAmelCase     )
 else:
  UpperCamelCase	:					List[str]       	=   convert_config(model[0]							,   _lowerCAmelCase     )
 UpperCamelCase	:					int       	=   model[0].eval()

 UpperCamelCase	:					Union[str, Any]       	=   True if config.feat_extract_norm == "layer" else False
 UpperCamelCase	:					Union[str, Any]       	=   WavaVecaFeatureExtractor(
     feature_size=1							,   sampling_rate=1_6000							,   padding_value=0							,   do_normalize=_lowerCAmelCase							,   return_attention_mask=_lowerCAmelCase							,   )

 if is_finetuned:
  if dict_path:
   UpperCamelCase	:					int       	=   Dictionary.load(_lowerCAmelCase     )

   # 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	:					List[str]       	=   target_dict.bos_index
   UpperCamelCase	:					List[Any]       	=   target_dict.pad_index
   UpperCamelCase	:					Optional[int]       	=   target_dict.bos_index
   UpperCamelCase	:					List[Any]       	=   target_dict.eos_index
   UpperCamelCase	:					Optional[int]       	=   len(target_dict.symbols     )
   UpperCamelCase	:					Dict       	=   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     )
   UpperCamelCase	:					str       	=   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							,   )
   UpperCamelCase	:					Union[str, Any]       	=   WavaVecaProcessor(feature_extractor=_lowerCAmelCase							,   tokenizer=_lowerCAmelCase     )
   processor.save_pretrained(_lowerCAmelCase     )

  UpperCamelCase	:					Dict       	=   SEWForCTC(_lowerCAmelCase     )
 else:
  UpperCamelCase	:					Optional[int]       	=   SEWModel(_lowerCAmelCase     )
  feature_extractor.save_pretrained(_lowerCAmelCase     )

 recursively_load_weights(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )

 hf_model.save_pretrained(_lowerCAmelCase     )


if __name__ == "__main__":
    __lowerCamelCase       :     Union[str, Any]   =     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"""
    )
    __lowerCamelCase       :     Tuple   =     parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )







class 					A__   :





      def __init__( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   set_counts
       UpperCamelCase	:					int       	=   max(A_   )
       UpperCamelCase	:					Optional[Any]       	=   len(A_   )
       UpperCamelCase	:					Union[str, Any]       	=   [1] * num_sets
       UpperCamelCase	:					Union[str, Any]       	=   list(range(A_   )   )





      def        __UpperCamelCase( self       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.get_parent(A_   )
       UpperCamelCase	:					Optional[int]       	=   self.get_parent(A_   )

       if src_parent == dst_parent:
        return False

       if self.ranks[dst_parent] >= self.ranks[src_parent]:
        self.set_counts[dst_parent] += self.set_counts[src_parent]
        UpperCamelCase	:					int       	=   0
        UpperCamelCase	:					Dict       	=   dst_parent
        if self.ranks[dst_parent] == self.ranks[src_parent]:
         self.ranks[dst_parent] += 1
        UpperCamelCase	:					Optional[int]       	=   self.set_counts[dst_parent]
       else:
        self.set_counts[src_parent] += self.set_counts[dst_parent]
        UpperCamelCase	:					Any       	=   0
        UpperCamelCase	:					Optional[int]       	=   src_parent
        UpperCamelCase	:					int       	=   self.set_counts[src_parent]

       UpperCamelCase	:					Any       	=   max(self.max_set       ,							A_   )
       return True





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       if self.parents[disj_set] == disj_set:
        return disj_set
       UpperCamelCase	:					Optional[int]       	=   self.get_parent(self.parents[disj_set]   )
       return self.parents[disj_set]
