




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       :       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 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase						,lowercase						,lowercase						,lowercase     )  ->   List[str]:




   for attribute in key.split("""."""     ):
      snake_case      :				Dict				=       getattr(lowercase						,lowercase     )

   if weight_type is not None:
      snake_case      :				List[str]				=       getattr(lowercase						,lowercase     ).shape
   else:
      snake_case      :				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":
      snake_case      :				Optional[Any]				=       value
   elif weight_type == "weight_g":
      snake_case      :				Any				=       value
   elif weight_type == "weight_v":
      snake_case      :				Union[str, Any]				=       value
   elif weight_type == "bias":
      snake_case      :				Optional[Any]				=       value
   else:
      snake_case      :				Any				=       value

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




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase						,lowercase     )  ->   Optional[int]:
   snake_case      :				Union[str, Any]				=       []
   snake_case      :				Optional[int]				=       fairseq_model.state_dict()

   snake_case      :				List[Any]				=       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase						,lowercase						,lowercase						,lowercase     )  ->   Dict:
   snake_case      :				Dict				=       full_name.split("""conv_layers."""     )[-1]
   snake_case      :				Tuple				=       name.split("""."""     )
   snake_case      :				int				=       int(items[0]     )
   snake_case      :				Optional[int]				=       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      :				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."""
         )
         snake_case      :				str				=       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      :				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."""
         )
         snake_case      :				str				=       value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
   else:
      unused_weights.append(lowercase     )




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase     )  ->   Any:
   snake_case      :				List[Any]				=       SEWConfig()
   if is_finetuned:
      snake_case      :				Any				=       model.wav_encoder.wav_model.cfg
   else:
      snake_case      :				Union[str, Any]				=       model.cfg

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

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

   snake_case      :				Any				=       """Wav2Vec2FeatureExtractor"""
   snake_case      :				List[Any]				=       """Wav2Vec2CTCTokenizer"""

   return config




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase						,lowercase=None						,lowercase=None						,lowercase=True     )  ->   List[Any]:




   if is_finetuned:
      snake_case	,					snake_case	,					snake_case      :				Union[str, Any]				=       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      :				Dict				=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

   if config_path is not None:
      snake_case      :				int				=       SEWConfig.from_pretrained(lowercase     )
   else:
      snake_case      :				Union[str, Any]				=       convert_config(model[0]						,lowercase     )
   snake_case      :				int				=       model[0].eval()

   snake_case      :				Tuple				=       True if config.feat_extract_norm == """layer""" else False
   snake_case      :				List[Any]				=       WavaVecaFeatureExtractor(
       feature_size=1						,sampling_rate=16000						,padding_value=0						,do_normalize=lowercase						,return_attention_mask=lowercase						,)

   if is_finetuned:
      if dict_path:
         snake_case      :				str				=       Dictionary.load(lowercase     )

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

      snake_case      :				List[Any]				=       SEWForCTC(lowercase     )
   else:
      snake_case      :				Any				=       SEWModel(lowercase     )
      feature_extractor.save_pretrained(lowercase     )

   recursively_load_weights(lowercase						,lowercase						,lowercase     )

   hf_model.save_pretrained(lowercase     )


if __name__ == "__main__":
       lowerCamelCase       :       Dict        =    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
       )












"""simple docstring"""





from __future__ import annotations


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      return [ord(SCREAMING_SNAKE_CASE			) - 96 for elem in plain]


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						list[int]			):







      '''simple docstring'''




      return "".join(chr(elem + 96			) for elem in encoded			)


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       encode(input("""-> """			).strip().lower()			)
      print("""Encoded: """							,						SCREAMING_SNAKE_CASE			)
      print("""Decoded:"""							,						decode(SCREAMING_SNAKE_CASE			)			)


if __name__ == "__main__":
       main()






