

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__			:  str					     =     logging.get_logger(__name__)

UpperCAmelCase__			:  int					     =     {
    """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        __lowercase   (  _A    ,     _A    ,     _A    ,     _A    ,     _A      )  ->    Optional[Any]:
      for attribute in key.split("""."""      ):
            SCREAMING_SNAKE_CASE			:   int	  = getattr(_A    ,     _A      )

      if weight_type is not None:
            SCREAMING_SNAKE_CASE			:   List[Any]	  = getattr(_A    ,     _A      ).shape
      else:
            SCREAMING_SNAKE_CASE			:   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":
            SCREAMING_SNAKE_CASE			:   Tuple	  = value
      elif weight_type == "weight_g":
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = value
      elif weight_type == "weight_v":
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = value
      elif weight_type == "bias":
            SCREAMING_SNAKE_CASE			:   List[Any]	  = value
      else:
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = value

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






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = []
      SCREAMING_SNAKE_CASE			:   int	  = fairseq_model.state_dict()

      SCREAMING_SNAKE_CASE			:   List[Any]	  = hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            SCREAMING_SNAKE_CASE			:   List[Any]	  = False
            if "conv_layers" in name:
                  load_conv_layer(
                      _A    ,     _A    ,     _A    ,     _A    ,     hf_model.config.feat_extract_norm == """group"""    ,     )
                  SCREAMING_SNAKE_CASE			:   List[Any]	  = True
            else:
                  for key, mapped_key in MAPPING.items():
                        SCREAMING_SNAKE_CASE			:   List[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]:
                              SCREAMING_SNAKE_CASE			:   str	  = True
                              if "*" in mapped_key:
                                    SCREAMING_SNAKE_CASE			:   Any	  = name.split(_A      )[0].split("""."""      )[-2]
                                    if not layer_index.isnumeric():
                                          continue
                                    SCREAMING_SNAKE_CASE			:   str	  = mapped_key.replace("""*"""    ,     _A      )
                              if "weight_g" in name:
                                    SCREAMING_SNAKE_CASE			:   List[str]	  = """weight_g"""
                              elif "weight_v" in name:
                                    SCREAMING_SNAKE_CASE			:   Tuple	  = """weight_v"""
                              elif "weight" in name:
                                    SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = """weight"""
                              elif "bias" in name:
                                    SCREAMING_SNAKE_CASE			:   List[str]	  = """bias"""
                              else:
                                    SCREAMING_SNAKE_CASE			:   Dict	  = 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        __lowercase   (  _A    ,     _A    ,     _A    ,     _A    ,     _A      )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   List[Any]	  = full_name.split("""conv_layers."""      )[-1]
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = name.split("""."""      )
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = int(items[0]      )
      SCREAMING_SNAKE_CASE			:   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."
                  )
                  SCREAMING_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."
                  )
                  SCREAMING_SNAKE_CASE			:   Optional[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."
                  )
                  SCREAMING_SNAKE_CASE			:   List[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."
                  )
                  SCREAMING_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(_A      )






def        __lowercase   (  _A    ,     _A      )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = SEWDConfig()
      if is_finetuned:
            SCREAMING_SNAKE_CASE			:   str	  = model.wav_encoder.wav_model.cfg
      else:
            SCREAMING_SNAKE_CASE			:   Tuple	  = model.cfg

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

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

      SCREAMING_SNAKE_CASE			:   Optional[int]	  = """Wav2Vec2FeatureExtractor"""
      SCREAMING_SNAKE_CASE			:   List[Any]	  = """Wav2Vec2CTCTokenizer"""

      return config






@torch.no_grad()
def        __lowercase   (  _A    ,     _A    ,     _A=None    ,     _A=None    ,     _A=True      )  ->    Dict:

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

      if config_path is not None:
            SCREAMING_SNAKE_CASE			:   Dict	  = SEWDConfig.from_pretrained(_A      )
      else:
            SCREAMING_SNAKE_CASE			:   int	  = convert_config(model[0]    ,     _A      )
      SCREAMING_SNAKE_CASE			:   Tuple	  = model[0].eval()

      SCREAMING_SNAKE_CASE			:   int	  = True if config.feat_extract_norm == """layer""" else False
      SCREAMING_SNAKE_CASE			:   Tuple	  = WavaVecaFeatureExtractor(
          feature_size=1    ,     sampling_rate=16000    ,     padding_value=0    ,     do_normalize=_A    ,     return_attention_mask=_A    ,     )

      if is_finetuned:
            if dict_path:
                  SCREAMING_SNAKE_CASE			:   str	  = Dictionary.load(_A      )

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

            SCREAMING_SNAKE_CASE			:   int	  = SEWDForCTC(_A      )
      else:
            SCREAMING_SNAKE_CASE			:   Tuple	  = SEWDModel(_A      )
            feature_extractor.save_pretrained(_A      )

      recursively_load_weights(_A    ,     _A    ,     _A      )

      hf_model.save_pretrained(_A      )


if __name__ == "__main__":
  UpperCAmelCase__			:  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"""
  )
  UpperCAmelCase__			:  List[str]					     =     parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )







from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

UpperCAmelCase__			:  int					     =     logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def        __lowercase   (  cls      )  ->    Any:
        return cls






def        __lowercase   (  _A    ,     _A = None    ,     _A = None    ,     _A = None    ,     _A = None    ,     )  ->    Tuple:
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = device
      SCREAMING_SNAKE_CASE			:   Tuple	  = shape[0]

      SCREAMING_SNAKE_CASE			:   Optional[int]	  = layout or torch.strided
      SCREAMING_SNAKE_CASE			:   Dict	  = device or torch.device("""cpu"""      )

      if generator is not None:
            SCREAMING_SNAKE_CASE			:   Optional[Any]	  = generator.device.type if not isinstance(_A    ,     _A      ) else generator[0].device.type
            if gen_device_type != device.type and gen_device_type == "cpu":
                  SCREAMING_SNAKE_CASE			:   Tuple	  = """cpu"""
                  if device != "mps":
                        logger.info(
                            F"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
                            F" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
                            F" slighly speed up this function by passing a generator that was created on the {device} device."      )
            elif gen_device_type != device.type and gen_device_type == "cuda":
                  raise ValueError(F"Cannot generate a {device} tensor from a generator of type {gen_device_type}."      )

      if isinstance(_A    ,     _A      ):
            SCREAMING_SNAKE_CASE			:   int	  = (1,) + shape[1:]
            SCREAMING_SNAKE_CASE			:   Any	  = [
                torch.randn(_A    ,     generator=generator[i]    ,     device=_A    ,     dtype=_A    ,     layout=_A      )
                for i in range(_A      )
            ]
            SCREAMING_SNAKE_CASE			:   Any	  = torch.cat(_A    ,     dim=0      ).to(_A      )
      else:
            SCREAMING_SNAKE_CASE			:   int	  = torch.randn(_A    ,     generator=_A    ,     device=_A    ,     dtype=_A    ,     layout=_A      ).to(_A      )

      return latents







def        __lowercase   (  _A      )  ->    Dict:
      if is_torch_version("""<"""    ,     """2.0.0"""      ) or not hasattr(_A    ,     """_dynamo"""      ):
            return False
      return isinstance(_A    ,     torch._dynamo.eval_frame.OptimizedModule      )




