





"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__A											=						{
    """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""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->List[Any]:





  """simple docstring"""


  for attribute in key.split('.'     ):
    lowerCAmelCase__     :List[str]											=				getattr(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

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

  logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__     :Any											=				[]
  lowerCAmelCase__     :List[Any]											=				fairseq_model.state_dict()

  lowerCAmelCase__     :List[str]											=				hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    lowerCAmelCase__     :int											=				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__     :Optional[Any]											=				True
    else:
      for key, mapped_key in MAPPING.items():
        lowerCAmelCase__     :Any											=				'hubert.' + 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] and not is_finetuned):
          lowerCAmelCase__     :List[Any]											=				True
          if "*" in mapped_key:
            lowerCAmelCase__     :List[Any]											=				name.split(_SCREAMING_SNAKE_CASE     )[0].split('.'     )[-2]
            lowerCAmelCase__     :Dict											=				mapped_key.replace('*'	,  _SCREAMING_SNAKE_CASE     )
          if "weight_g" in name:
            lowerCAmelCase__     :Dict											=				'weight_g'
          elif "weight_v" in name:
            lowerCAmelCase__     :Union[str, Any]											=				'weight_v'
          elif "weight" in name:
            lowerCAmelCase__     :Optional[int]											=				'weight'
          elif "bias" in name:
            lowerCAmelCase__     :List[Any]											=				'bias'
          else:
            lowerCAmelCase__     :Tuple											=				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	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->List[Any]:





  """simple docstring"""


  lowerCAmelCase__     :int											=				full_name.split('conv_layers.'     )[-1]
  lowerCAmelCase__     :Any											=				name.split('.'     )
  lowerCAmelCase__     :List[Any]											=				int(items[0]     )
  lowerCAmelCase__     :Union[str, 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."
      )
      lowerCAmelCase__     :Any											=				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."
      )
      lowerCAmelCase__     :List[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."
      )
      lowerCAmelCase__     :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."
      )
      lowerCAmelCase__     :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	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=None	,  _SCREAMING_SNAKE_CASE=True     )   ->Optional[int]:





  """simple docstring"""


  if config_path is not None:
    lowerCAmelCase__     :Optional[Any]											=				HubertConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
  else:
    lowerCAmelCase__     :List[Any]											=				HubertConfig()

  if is_finetuned:
    if dict_path:
      lowerCAmelCase__     :Union[str, Any]											=				Dictionary.load(_SCREAMING_SNAKE_CASE     )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      lowerCAmelCase__     :Any											=				target_dict.pad_index
      lowerCAmelCase__     :int											=				target_dict.bos_index
      lowerCAmelCase__     :str											=				target_dict.eos_index
      lowerCAmelCase__     :Tuple											=				len(target_dict.symbols     )
      lowerCAmelCase__     :List[Any]											=				os.path.join(_SCREAMING_SNAKE_CASE	,  'vocab.json'     )
      if not os.path.isdir(_SCREAMING_SNAKE_CASE     ):
        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_SCREAMING_SNAKE_CASE     )     )
        return
      os.makedirs(_SCREAMING_SNAKE_CASE	,  exist_ok=_SCREAMING_SNAKE_CASE     )
      with open(_SCREAMING_SNAKE_CASE	,  'w'	,  encoding='utf-8'     ) as vocab_handle:
        json.dump(target_dict.indices	,  _SCREAMING_SNAKE_CASE     )
      lowerCAmelCase__     :List[Any]											=				WavaVecaCTCTokenizer(
          _SCREAMING_SNAKE_CASE	,  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=_SCREAMING_SNAKE_CASE	,  )
      lowerCAmelCase__     :Optional[Any]											=				True if config.feat_extract_norm == 'layer' else False
      lowerCAmelCase__     :Dict											=				WavaVecaFeatureExtractor(
          feature_size=1	,  sampling_rate=1_6000	,  padding_value=0	,  do_normalize=_SCREAMING_SNAKE_CASE	,  return_attention_mask=_SCREAMING_SNAKE_CASE	,  )
      lowerCAmelCase__     :Any											=				WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE	,  tokenizer=_SCREAMING_SNAKE_CASE     )
      processor.save_pretrained(_SCREAMING_SNAKE_CASE     )

    lowerCAmelCase__     :List[str]											=				HubertForCTC(_SCREAMING_SNAKE_CASE     )
  else:
    lowerCAmelCase__     :Optional[Any]											=				HubertModel(_SCREAMING_SNAKE_CASE     )

  if is_finetuned:
    lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Dict											=				fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]	,  arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
  else:
    lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

  lowerCAmelCase__     :Union[str, Any]											=				model[0].eval()

  recursively_load_weights(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )

  hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
  __A											=						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"""
  )
  __A											=						parser.parse_args()
  convert_hubert_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )







"""simple docstring"""





import argparse
import os
import shutil
from pathlib import Path

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

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


__A											=						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=False	,  )   ->Tuple:





  """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 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = False     )   ->List[Any]:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				torch.floataa if fpaa else torch.floataa
  if fpaa and torch.cuda.is_available():
    lowerCAmelCase__     :Tuple											=				'cuda'
  elif fpaa and not torch.cuda.is_available():
    raise ValueError('`float16` model export is only supported on GPUs with CUDA'     )
  else:
    lowerCAmelCase__     :List[Any]											=				'cpu'
  lowerCAmelCase__     :List[str]											=				StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE	,  torch_dtype=_SCREAMING_SNAKE_CASE     ).to(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Dict											=				Path(_SCREAMING_SNAKE_CASE     )

  # TEXT ENCODER
  lowerCAmelCase__     :str											=				pipeline.text_encoder.config.max_position_embeddings
  lowerCAmelCase__     :Dict											=				pipeline.text_encoder.config.hidden_size
  lowerCAmelCase__     :List[Any]											=				pipeline.tokenizer(
      'A sample prompt'	,  padding='max_length'	,  max_length=pipeline.tokenizer.model_max_length	,  truncation=_SCREAMING_SNAKE_CASE	,  return_tensors='pt'	,  )
  onnx_export(
      pipeline.text_encoder	,  model_args=(text_input.input_ids.to(device=_SCREAMING_SNAKE_CASE	,  dtype=torch.intaa     ))	,  output_path=output_path / 'text_encoder' / 'model.onnx'	,  ordered_input_names=['input_ids']	,  output_names=['last_hidden_state', 'pooler_output']	,  dynamic_axes={
          'input_ids': {0: 'batch', 1: 'sequence'},
      }	,  opset=_SCREAMING_SNAKE_CASE	,  )
  del pipeline.text_encoder

  # UNET
  lowerCAmelCase__     :int											=				pipeline.unet.config.in_channels
  lowerCAmelCase__     :Optional[Any]											=				pipeline.unet.config.sample_size
  lowerCAmelCase__     :Dict											=				output_path / 'unet' / 'model.onnx'
  onnx_export(
      pipeline.unet	,  model_args=(
          torch.randn(2	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
          torch.randn(2     ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
          torch.randn(2	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
          False,
      )	,  output_path=_SCREAMING_SNAKE_CASE	,  ordered_input_names=['sample', 'timestep', 'encoder_hidden_states', 'return_dict']	,  output_names=['out_sample']	,  dynamic_axes={
          'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
          'timestep': {0: 'batch'},
          'encoder_hidden_states': {0: 'batch', 1: 'sequence'},
      }	,  opset=_SCREAMING_SNAKE_CASE	,  use_external_data_format=_SCREAMING_SNAKE_CASE	,  )
  lowerCAmelCase__     :List[Any]											=				str(unet_path.absolute().as_posix()     )
  lowerCAmelCase__     :int											=				os.path.dirname(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :str											=				onnx.load(_SCREAMING_SNAKE_CASE     )
  # clean up existing tensor files
  shutil.rmtree(_SCREAMING_SNAKE_CASE     )
  os.mkdir(_SCREAMING_SNAKE_CASE     )
  # collate external tensor files into one
  onnx.save_model(
      _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  save_as_external_data=_SCREAMING_SNAKE_CASE	,  all_tensors_to_one_file=_SCREAMING_SNAKE_CASE	,  location='weights.pb'	,  convert_attribute=_SCREAMING_SNAKE_CASE	,  )
  del pipeline.unet

  # VAE ENCODER
  lowerCAmelCase__     :int											=				pipeline.vae
  lowerCAmelCase__     :Optional[Any]											=				vae_encoder.config.in_channels
  lowerCAmelCase__     :int											=				vae_encoder.config.sample_size
  # need to get the raw tensor output (sample) from the encoder
  lowerCAmelCase__     :str											=				lambda _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     : vae_encoder.encode(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )[0].sample()
  onnx_export(
      _SCREAMING_SNAKE_CASE	,  model_args=(
          torch.randn(1	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
          False,
      )	,  output_path=output_path / 'vae_encoder' / 'model.onnx'	,  ordered_input_names=['sample', 'return_dict']	,  output_names=['latent_sample']	,  dynamic_axes={
          'sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
      }	,  opset=_SCREAMING_SNAKE_CASE	,  )

  # VAE DECODER
  lowerCAmelCase__     :int											=				pipeline.vae
  lowerCAmelCase__     :List[Any]											=				vae_decoder.config.latent_channels
  lowerCAmelCase__     :Optional[int]											=				vae_decoder.config.out_channels
  # forward only through the decoder part
  lowerCAmelCase__     :Any											=				vae_encoder.decode
  onnx_export(
      _SCREAMING_SNAKE_CASE	,  model_args=(
          torch.randn(1	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ).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 pipeline.vae

  # SAFETY CHECKER
  if pipeline.safety_checker is not None:
    lowerCAmelCase__     :Optional[int]											=				pipeline.safety_checker
    lowerCAmelCase__     :Optional[int]											=				safety_checker.config.vision_config.num_channels
    lowerCAmelCase__     :Any											=				safety_checker.config.vision_config.image_size
    lowerCAmelCase__     :List[str]											=				safety_checker.forward_onnx
    onnx_export(
        pipeline.safety_checker	,  model_args=(
            torch.randn(
                1	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
            torch.randn(1	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     ).to(device=_SCREAMING_SNAKE_CASE	,  dtype=_SCREAMING_SNAKE_CASE     ),
        )	,  output_path=output_path / 'safety_checker' / 'model.onnx'	,  ordered_input_names=['clip_input', 'images']	,  output_names=['out_images', 'has_nsfw_concepts']	,  dynamic_axes={
            'clip_input': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
            'images': {0: 'batch', 1: 'height', 2: 'width', 3: 'channels'},
        }	,  opset=_SCREAMING_SNAKE_CASE	,  )
    del pipeline.safety_checker
    lowerCAmelCase__     :Union[str, Any]											=				OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'     )
    lowerCAmelCase__     :Dict											=				pipeline.feature_extractor
  else:
    lowerCAmelCase__     :Tuple											=				None
    lowerCAmelCase__     :Optional[int]											=				None

  lowerCAmelCase__     :List[str]											=				OnnxStableDiffusionPipeline(
      vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_encoder'     )	,  vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / 'vae_decoder'     )	,  text_encoder=OnnxRuntimeModel.from_pretrained(output_path / 'text_encoder'     )	,  tokenizer=pipeline.tokenizer	,  unet=OnnxRuntimeModel.from_pretrained(output_path / 'unet'     )	,  scheduler=pipeline.scheduler	,  safety_checker=_SCREAMING_SNAKE_CASE	,  feature_extractor=_SCREAMING_SNAKE_CASE	,  requires_safety_checker=safety_checker is not None	,  )

  onnx_pipeline.save_pretrained(_SCREAMING_SNAKE_CASE     )
  print('ONNX pipeline saved to'	,  _SCREAMING_SNAKE_CASE     )

  del pipeline
  del onnx_pipeline
  lowerCAmelCase__     :Dict											=				OnnxStableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE	,  provider='CPUExecutionProvider'     )
  print('ONNX pipeline is loadable'     )


if __name__ == "__main__":
  __A											=						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""")

  __A											=						parser.parse_args()

  convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
