





"""simple docstring"""





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Any:





  """simple docstring"""


  lowerCAmelCase__     :Any											=				[
      'decoder.version',
      'decoder.output_projection.weight',
      '_float_tensor',
      'decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    state_dict.pop(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__				,							lowerCAmelCase__     :Optional[int]											=				emb.weight.shape
  lowerCAmelCase__     :Tuple											=				nn.Linear(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  bias=_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :int											=				emb.weight.data
  return lin_layer
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Tuple:





  """simple docstring"""


  lowerCAmelCase__     :Optional[int]											=				torch.load(_SCREAMING_SNAKE_CASE	,  map_location='cpu'     )
  lowerCAmelCase__     :List[Any]											=				Namespace(**checkpoint['cfg']['model']     )
  lowerCAmelCase__     :List[Any]											=				checkpoint['model']
  remove_ignore_keys_(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Optional[Any]											=				state_dict['decoder.embed_tokens.weight'].shape[0]

  lowerCAmelCase__     :Optional[int]											=				{key.replace('decoder'	,  'model'     ): val for key, val in state_dict.items()}

  lowerCAmelCase__     :Union[str, Any]											=				XGLMConfig(
      vocab_size=_SCREAMING_SNAKE_CASE	,  max_position_embeddings=args.max_target_positions	,  num_layers=args.decoder_layers	,  attention_heads=args.decoder_attention_heads	,  ffn_dim=args.decoder_ffn_embed_dim	,  d_model=args.decoder_embed_dim	,  layerdrop=args.decoder_layerdrop	,  dropout=args.dropout	,  attention_dropout=args.attention_dropout	,  activation_dropout=args.activation_dropout	,  activation_function='gelu'	,  scale_embedding=not args.no_scale_embedding	,  tie_word_embeddings=args.share_decoder_input_output_embed	,  )

  lowerCAmelCase__     :str											=				XGLMForCausalLM(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :List[str]											=				model.load_state_dict(_SCREAMING_SNAKE_CASE	,  strict=_SCREAMING_SNAKE_CASE     )
  print(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :Dict											=				make_linear_from_emb(model.model.embed_tokens     )

  return model


if __name__ == "__main__":
  __A											=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
  parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  __A											=						parser.parse_args()
  __A											=						convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
  model.save_pretrained(args.pytorch_dump_folder_path)







"""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)
