





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
UpperCAmelCase	:	Union[str, Any]		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Union[str, Any]		     =       {
    """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	:	List[Any]		     =       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]
def      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Any       ,       SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   List[str]						):





  """simple docstring"""



  for attribute in key.split("."						):
    a__	:					Optional[Any]										=getattr(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

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

  logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''						)
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   Any						):





  """simple docstring"""



  a__	:					int										=[]
  a__	:					Optional[Any]										=fairseq_model.state_dict()

  a__	:					Any										=hf_model.unispeech_sat.feature_extractor

  for name, value in fairseq_dict.items():
    a__	:					List[str]										=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"       ,       )
      a__	:					Any										=True
    else:
      for key, mapped_key in MAPPING.items():
        a__	:					str										="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
          a__	:					Tuple										=True
          if "*" in mapped_key:
            a__	:					Union[str, Any]										=name.split(SCREAMING_SNAKE_CASE						)[0].split("."						)[-2]
            a__	:					Any										=mapped_key.replace("*"       ,       SCREAMING_SNAKE_CASE						)
          if "weight_g" in name:
            a__	:					Any										="weight_g"
          elif "weight_v" in name:
            a__	:					Union[str, Any]										="weight_v"
          elif "bias" in name:
            a__	:					Tuple										="bias"
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            a__	:					int										="weight"
          else:
            a__	:					Optional[Any]										=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      _A       (		SCREAMING_SNAKE_CASE     :   Any       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   List[str]       ,       SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   Dict						):





  """simple docstring"""



  a__	:					Optional[Any]										=full_name.split("conv_layers."						)[-1]
  a__	:					Dict										=name.split("."						)
  a__	:					str										=int(items[0]						)
  a__	:					Union[str, Any]										=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.'''						)
      a__	:					Optional[Any]										=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.'''						)
      a__	:					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:
      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.'''						)
      a__	:					int										=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.'''						)
      a__	:					List[str]										=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      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   List[Any]=None       ,       SCREAMING_SNAKE_CASE     :   Optional[int]=None       ,       SCREAMING_SNAKE_CASE     :   Any=True						):





  """simple docstring"""



  if config_path is not None:
    a__	:					Optional[int]										=UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
  else:
    a__	:					Optional[Any]										=UniSpeechSatConfig()

  a__	:					List[Any]										=""

  if is_finetuned:
    a__	:					Optional[int]										=UniSpeechSatForCTC(SCREAMING_SNAKE_CASE						)
  else:
    a__	:					int										=UniSpeechSatForPreTraining(SCREAMING_SNAKE_CASE						)

  a__					,							a__					,							a__	:					str										=fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]       ,       arg_overrides={"data": "/".join(dict_path.split("/"						)[:-1]						)}						)
  a__	:					Optional[Any]										=model[0].eval()

  recursively_load_weights(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
     UpperCAmelCase	:	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(
         """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     UpperCAmelCase	:	Union[str, Any]		     =       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 argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowercase_       		=  version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	tuple    ,			SCREAMING_SNAKE_CASE__			:	Path    ,			SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	Any    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]=False    ,			)							->      Union[str, Any]:





    '''simple docstring'''






    output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE__    ,			exist_ok=SCREAMING_SNAKE_CASE__      )
    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
    # so we check the torch version for backwards compatibility
    if is_torch_less_than_1_11:
        export(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			use_external_data_format=SCREAMING_SNAKE_CASE__    ,			enable_onnx_checker=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
    else:
        export(
            SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			f=output_path.as_posix()    ,			input_names=SCREAMING_SNAKE_CASE__    ,			output_names=SCREAMING_SNAKE_CASE__    ,			dynamic_axes=SCREAMING_SNAKE_CASE__    ,			do_constant_folding=SCREAMING_SNAKE_CASE__    ,			opset_version=SCREAMING_SNAKE_CASE__    ,			)
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      Tuple:





    '''simple docstring'''






    A__          =   torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        A__          =   'cuda'
    elif fpaa and not torch.cuda.is_available():
        raise ValueError('`float16` model export is only supported on GPUs with CUDA'      )
    else:
        A__          =   'cpu'
    A__          =   Path(SCREAMING_SNAKE_CASE__      )

    # VAE DECODER
    A__          =   AutoencoderKL.from_pretrained(model_path + '/vae'      )
    A__          =   vae_decoder.config.latent_channels
    # forward only through the decoder part
    A__          =   vae_decoder.decode
    onnx_export(
        SCREAMING_SNAKE_CASE__    ,			model_args=(
            torch.randn(1    ,			SCREAMING_SNAKE_CASE__    ,			25    ,			25      ).to(device=SCREAMING_SNAKE_CASE__    ,			dtype=SCREAMING_SNAKE_CASE__      ),
            False,
        )    ,			output_path=output_path / 'vae_decoder' / 'model.onnx'    ,			ordered_input_names=['latent_sample', 'return_dict']    ,			output_names=['sample']    ,			dynamic_axes={
            'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
        }    ,			opset=SCREAMING_SNAKE_CASE__    ,			)
    del vae_decoder


if __name__ == "__main__":
     lowercase_       		=  argparse.ArgumentParser()

     parser.add_argument(
         "--model_path",
         type=str,
         required=True,
         help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
     )

     parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
     parser.add_argument(
         "--opset",
         default=14,
         type=int,
         help="The version of the ONNX operator set to use.",
     )
     parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

     lowercase_       		=  parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")

