





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()
lowerCAmelCase_        =			logging.get_logger(__name__)

lowerCAmelCase_        =			{
    """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_		(					lowerCAmelCase:		List[str]			,							lowerCAmelCase:		Any			,							lowerCAmelCase:		Optional[int]			,							lowerCAmelCase:		int			,							lowerCAmelCase:		Optional[Any] )->						Optional[Any]:
  for attribute in key.split('.' ):
    _snake_case      : int														=		getattr(lowerCAmelCase			,							lowerCAmelCase )

  if weight_type is not None:
    _snake_case      : Union[str, Any]														=		getattr(lowerCAmelCase			,							lowerCAmelCase ).shape
  else:
    _snake_case      : Optional[int]														=		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      : int														=		value
  elif weight_type == "weight_g":
    _snake_case      : List[str]														=		value
  elif weight_type == "weight_v":
    _snake_case      : Optional[Any]														=		value
  elif weight_type == "bias":
    _snake_case      : Tuple														=		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 			lowerCamelCase_		(					lowerCAmelCase:		List[str]			,							lowerCAmelCase:		Union[str, Any]			,							lowerCAmelCase:		Optional[int] )->						int:
  _snake_case      : Any														=		[]
  _snake_case      : str														=		fairseq_model.state_dict()

  _snake_case      : List[str]														=		hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    _snake_case      : List[str]														=		False
    if "conv_layers" in name:
      load_conv_layer(
          lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase			,							hf_model.config.feat_extract_norm == 'group'			,							)
      _snake_case      : Tuple														=		True
    else:
      for key, mapped_key in MAPPING.items():
        _snake_case      : Optional[Any]														=		'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]:
          _snake_case      : str														=		True
          if "*" in mapped_key:
            _snake_case      : List[str]														=		name.split(lowerCAmelCase )[0].split('.' )[-2]
            if not layer_index.isnumeric():
              continue
            _snake_case      : Union[str, Any]														=		mapped_key.replace('*'			,							lowerCAmelCase )
          if "weight_g" in name:
            _snake_case      : int														=		'weight_g'
          elif "weight_v" in name:
            _snake_case      : Optional[int]														=		'weight_v'
          elif "weight" in name:
            _snake_case      : Dict														=		'weight'
          elif "bias" in name:
            _snake_case      : List[Any]														=		'bias'
          else:
            _snake_case      : Tuple														=		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 			lowerCamelCase_		(					lowerCAmelCase:		Union[str, Any]			,							lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		Dict			,							lowerCAmelCase:		Union[str, Any]			,							lowerCAmelCase:		str )->						Optional[int]:
  _snake_case      : Dict														=		full_name.split('conv_layers.' )[-1]
  _snake_case      : List[str]														=		name.split('.' )
  _snake_case      : Any														=		int(items[0] )
  _snake_case      : 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."""
      )
      _snake_case      : 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."""
      )
      _snake_case      : 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."
      )
      _snake_case      : 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."""
      )
      _snake_case      : Optional[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 			lowerCamelCase_		(					lowerCAmelCase:		List[str]			,							lowerCAmelCase:		int )->						Any:
  _snake_case      : Dict														=		SEWDConfig()
  if is_finetuned:
    _snake_case      : Dict														=		model.wav_encoder.wav_model.cfg
  else:
    _snake_case      : Optional[int]														=		model.cfg

  _snake_case      : List[str]														=		fs_config.conv_bias
  _snake_case      : Any														=		eval(fs_config.conv_feature_layers )
  _snake_case      : List[Any]														=		[x[0] for x in conv_layers]
  _snake_case      : Dict														=		[x[1] for x in conv_layers]
  _snake_case      : Optional[Any]														=		[x[2] for x in conv_layers]
  _snake_case      : Tuple														=		'gelu'
  _snake_case      : Tuple														=		'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
  _snake_case      : Any														=		0.0
  _snake_case      : int														=		fs_config.activation_fn.name
  _snake_case      : Any														=		fs_config.encoder_embed_dim
  _snake_case      : str														=		0.0_2
  _snake_case      : Optional[int]														=		fs_config.encoder_ffn_embed_dim
  _snake_case      : str														=		1E-5
  _snake_case      : List[Any]														=		fs_config.encoder_layerdrop
  _snake_case      : Optional[int]														=		fs_config.encoder_attention_heads
  _snake_case      : Tuple														=		fs_config.conv_pos_groups
  _snake_case      : str														=		fs_config.conv_pos
  _snake_case      : int														=		len(lowerCAmelCase )
  _snake_case      : int														=		fs_config.encoder_layers
  _snake_case      : Dict														=		fs_config.squeeze_factor
  # DeBERTa-specific parameters:
  _snake_case      : str														=		fs_config.max_position_embeddings
  _snake_case      : List[Any]														=		fs_config.position_buckets
  _snake_case      : Dict														=		fs_config.share_att_key
  _snake_case      : Union[str, Any]														=		fs_config.relative_attention
  _snake_case      : Optional[int]														=		fs_config.position_biased_input
  _snake_case      : Any														=		tuple(fs_config.pos_att_type.split('|' ) )
  _snake_case      : str														=		fs_config.norm_rel_ebd

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

  _snake_case      : Optional[Any]														=		'Wav2Vec2FeatureExtractor'
  _snake_case      : List[Any]														=		'Wav2Vec2CTCTokenizer'

  return config


@torch.no_grad()
def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		List[Any]			,							lowerCAmelCase:		int=None			,							lowerCAmelCase:		Optional[Any]=None			,							lowerCAmelCase:		Dict=True )->						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      : Optional[int]														=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )

  if config_path is not None:
    _snake_case      : Any														=		SEWDConfig.from_pretrained(lowerCAmelCase )
  else:
    _snake_case      : Optional[int]														=		convert_config(model[0]			,							lowerCAmelCase )
  _snake_case      : Any														=		model[0].eval()

  _snake_case      : Optional[int]														=		True if config.feat_extract_norm == 'layer' else False
  _snake_case      : Tuple														=		WavaVecaFeatureExtractor(
      feature_size=1			,							sampling_rate=1_60_00			,							padding_value=0			,							do_normalize=lowerCAmelCase			,							return_attention_mask=lowerCAmelCase			,							)

  if is_finetuned:
    if dict_path:
      _snake_case      : int														=		Dictionary.load(lowerCAmelCase )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _snake_case      : List[str]														=		target_dict.pad_index
      _snake_case      : str														=		target_dict.bos_index
      _snake_case      : int														=		target_dict.pad_index
      _snake_case      : str														=		target_dict.bos_index
      _snake_case      : int														=		target_dict.eos_index
      _snake_case      : int														=		len(target_dict.symbols )
      _snake_case      : List[str]														=		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 )
      _snake_case      : Tuple														=		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			,							)
      _snake_case      : Any														=		WavaVecaProcessor(feature_extractor=lowerCAmelCase			,							tokenizer=lowerCAmelCase )
      processor.save_pretrained(lowerCAmelCase )

    _snake_case      : Dict														=		SEWDForCTC(lowerCAmelCase )
  else:
    _snake_case      : Dict														=		SEWDModel(lowerCAmelCase )
    feature_extractor.save_pretrained(lowerCAmelCase )

  recursively_load_weights(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase )

  hf_model.save_pretrained(lowerCAmelCase )


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_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_        =			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_		(					lowerCAmelCase:		list			,							lowerCAmelCase:		list )->						list:
  _snake_case      : int														=		len(lowerCAmelCase )
  _snake_case      : int														=		len(lowerCAmelCase )
  _snake_case      : int														=		(
      first_input_list_length
      if first_input_list_length > second_input_list_length
      else second_input_list_length
  )
  _snake_case      : list														=		[]
  for char_count in range(lowerCAmelCase ):
    if char_count < first_input_list_length:
      output_result_list.append(first_input_list[char_count] )
    if char_count < second_input_list_length:
      output_result_list.append(second_input_list[char_count] )

  return output_result_list


if __name__ == "__main__":
      print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)




