



import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

UpperCAmelCase_									=							{
    "post_extract_proj": "feature_projection.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.layer_norm": "encoder.layer_norm",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
UpperCAmelCase_									=							[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	Any				)->Optional[Any]:
      for attribute in key.split('''.'''				):
            _lowerCAmelCase 			=  getattr(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

      if weight_type is not None:
            _lowerCAmelCase 			=  getattr(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				).shape
      else:
            _lowerCAmelCase 			=  hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                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":
            _lowerCAmelCase 			=  value
      elif weight_type == "weight_g":
            _lowerCAmelCase 			=  value
      elif weight_type == "weight_v":
            _lowerCAmelCase 			=  value
      elif weight_type == "bias":
            _lowerCAmelCase 			=  value
      else:
            _lowerCAmelCase 			=  value

      logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	Any				)->List[str]:
      _lowerCAmelCase 			=  []
      _lowerCAmelCase 			=  fairseq_model.state_dict()

      _lowerCAmelCase 			=  hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            _lowerCAmelCase 			=  False
            if "conv_layers" in name:
                  load_conv_layer(
                      _SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				hf_model.config.feat_extract_norm == '''group'''     ,				)
                  _lowerCAmelCase 			=  True
            else:
                  for key, mapped_key in MAPPING.items():
                        _lowerCAmelCase 			=  '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                        if key in name or key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0]:
                              if "layer_norm_for_extract" in name and (".".join(name.split('''.'''				)[:-1]				) != key):
                                    # special case since naming is very similar
                                    continue
                              _lowerCAmelCase 			=  True
                              if "*" in mapped_key:
                                    _lowerCAmelCase 			=  name.split(_SCREAMING_SNAKE_CASE				)[0].split('''.'''				)[-2]
                                    _lowerCAmelCase 			=  mapped_key.replace('''*'''     ,				_SCREAMING_SNAKE_CASE				)
                              if "weight_g" in name:
                                    _lowerCAmelCase 			=  '''weight_g'''
                              elif "weight_v" in name:
                                    _lowerCAmelCase 			=  '''weight_v'''
                              elif "bias" in name:
                                    _lowerCAmelCase 			=  '''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    _lowerCAmelCase 			=  '''weight'''
                              else:
                                    _lowerCAmelCase 			=  None
                              set_recursively(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
                        continue
            if not is_used:
                  unused_weights.append(_SCREAMING_SNAKE_CASE				)

      logger.warning(f'''Unused weights: {unused_weights}'''				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Tuple     ,				_SCREAMING_SNAKE_CASE   :	Dict     ,				_SCREAMING_SNAKE_CASE   :	Dict     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	int				)->str:
      _lowerCAmelCase 			=  full_name.split('''conv_layers.'''				)[-1]
      _lowerCAmelCase 			=  name.split('''.'''				)
      _lowerCAmelCase 			=  int(items[0]				)
      _lowerCAmelCase 			=  int(items[1]				)

      if type_id == 0:
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            f'''{full_name} has size {value.shape}, but'''
                            f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''				)
                  _lowerCAmelCase 			=  value
                  logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''				)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            f'''{full_name} has size {value.shape}, but'''
                            f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''				)
                  _lowerCAmelCase 			=  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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            f'''{full_name} has size {value.shape}, but'''
                            f''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''				)
                  _lowerCAmelCase 			=  value
                  logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            f'''{full_name} has size {value.shape}, but'''
                            f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''				)
                  _lowerCAmelCase 			=  value
                  logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
      else:
            unused_weights.append(_SCREAMING_SNAKE_CASE				)
@torch.no_grad()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	str=None     ,				_SCREAMING_SNAKE_CASE   :	List[Any]=None     ,				_SCREAMING_SNAKE_CASE   :	List[Any]=True				)->Any:
      if config_path is not None:
            _lowerCAmelCase 			=  UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
      else:
            _lowerCAmelCase 			=  UniSpeechSatConfig()

      _lowerCAmelCase 			=  ''''''

      if is_finetuned:
            _lowerCAmelCase 			=  UniSpeechSatForCTC(_SCREAMING_SNAKE_CASE				)
      else:
            _lowerCAmelCase 			=  UniSpeechSatForPreTraining(_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]     ,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''				)[:-1]				)}				)
      _lowerCAmelCase 			=  model[0].eval()

      recursively_load_weights(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

      hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE				)


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(
        "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    UpperCAmelCase_									=							parser.parse_args()
    convert_unispeech_sat_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )




import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def 							UpperCAmelCase__		(				*_SCREAMING_SNAKE_CASE   :	Tuple				)->List[Any]:
      if not isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				):
            _lowerCAmelCase 			=  list(_SCREAMING_SNAKE_CASE				)
      for i in range(len(_SCREAMING_SNAKE_CASE				)				):
            _lowerCAmelCase 			=  None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Exception				)->bool:
      _lowerCAmelCase 			=  [
          '''CUDA out of memory.''',  # CUDA OOM
          '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
          '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
      ]
      if isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False


def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	callable = None     ,				_SCREAMING_SNAKE_CASE   :	int = 1_2_8				)->Optional[int]:
      if function is None:
            return functools.partial(_SCREAMING_SNAKE_CASE     ,				starting_batch_size=_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  starting_batch_size

      def decorator(*_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				**_SCREAMING_SNAKE_CASE   :	Optional[Any]				):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            _lowerCAmelCase 			=  list(inspect.signature(_SCREAMING_SNAKE_CASE				).parameters.keys()				)
            # Guard against user error
            if len(_SCREAMING_SNAKE_CASE				) < (len(_SCREAMING_SNAKE_CASE				) + 1):
                  _lowerCAmelCase 			=  ''', '''.join([f'''{arg}={value}''' for arg, value in zip(params[1:]     ,				args[1:]				)]				)
                  raise TypeError(
                      f'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
                      f'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''				)
            while True:
                  if batch_size == 0:
                        raise RuntimeError('''No executable batch size found, reached zero.'''				)
                  try:
                        return function(_SCREAMING_SNAKE_CASE     ,				*_SCREAMING_SNAKE_CASE     ,				**_SCREAMING_SNAKE_CASE				)
                  except Exception as e:
                        if should_reduce_batch_size(_SCREAMING_SNAKE_CASE				):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator