
"""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__ :       int				= version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase=False			,					):






  """simple docstring"""



  output_path.parent.mkdir(parents=_UpperCamelCase			,					exist_ok=_UpperCamelCase    )
  # 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(
        _UpperCamelCase			,					_UpperCamelCase			,					f=output_path.as_posix()			,					input_names=_UpperCamelCase			,					output_names=_UpperCamelCase			,					dynamic_axes=_UpperCamelCase			,					do_constant_folding=_UpperCamelCase			,					use_external_data_format=_UpperCamelCase			,					enable_onnx_checker=_UpperCamelCase			,					opset_version=_UpperCamelCase			,					)
  else:
    export(
        _UpperCamelCase			,					_UpperCamelCase			,					f=output_path.as_posix()			,					input_names=_UpperCamelCase			,					output_names=_UpperCamelCase			,					dynamic_axes=_UpperCamelCase			,					do_constant_folding=_UpperCamelCase			,					opset_version=_UpperCamelCase			,					)




@torch.no_grad()
def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase = False    ):






  """simple docstring"""



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

  # TEXT ENCODER
  _lowercase:      Dict   			=  pipeline.text_encoder.config.max_position_embeddings
  _lowercase:      Tuple   			=  pipeline.text_encoder.config.hidden_size
  _lowercase:      Any   			=  pipeline.tokenizer(
      '''A sample prompt'''			,					padding='''max_length'''			,					max_length=pipeline.tokenizer.model_max_length			,					truncation=_UpperCamelCase			,					return_tensors='''pt'''			,					)
  onnx_export(
      pipeline.text_encoder			,					model_args=(text_input.input_ids.to(device=_UpperCamelCase			,					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=_UpperCamelCase			,					)
  del pipeline.text_encoder

  # UNET
  _lowercase:      Dict   			=  pipeline.unet.config.in_channels
  _lowercase:      Tuple   			=  pipeline.unet.config.sample_size
  _lowercase:      int   			=  output_path / '''unet''' / '''model.onnx'''
  onnx_export(
      pipeline.unet			,					model_args=(
          torch.randn(2			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ).to(device=_UpperCamelCase			,					dtype=_UpperCamelCase    ),
          torch.randn(2    ).to(device=_UpperCamelCase			,					dtype=_UpperCamelCase    ),
          torch.randn(2			,					_UpperCamelCase			,					_UpperCamelCase    ).to(device=_UpperCamelCase			,					dtype=_UpperCamelCase    ),
          False,
      )			,					output_path=_UpperCamelCase			,					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=_UpperCamelCase			,					use_external_data_format=_UpperCamelCase			,					)
  _lowercase:      Optional[int]   			=  str(unet_path.absolute().as_posix()    )
  _lowercase:      List[str]   			=  os.path.dirname(_UpperCamelCase    )
  _lowercase:      List[str]   			=  onnx.load(_UpperCamelCase    )
  # clean up existing tensor files
  shutil.rmtree(_UpperCamelCase    )
  os.mkdir(_UpperCamelCase    )
  # collate external tensor files into one
  onnx.save_model(
      _UpperCamelCase			,					_UpperCamelCase			,					save_as_external_data=_UpperCamelCase			,					all_tensors_to_one_file=_UpperCamelCase			,					location='''weights.pb'''			,					convert_attribute=_UpperCamelCase			,					)
  del pipeline.unet

  # VAE ENCODER
  _lowercase:      Optional[Any]   			=  pipeline.vae
  _lowercase:      Tuple   			=  vae_encoder.config.in_channels
  _lowercase:      Union[str, Any]   			=  vae_encoder.config.sample_size
  # need to get the raw tensor output (sample) from the encoder
  _lowercase:      Any   			=  lambda _UpperCamelCase			,					_UpperCamelCase    : vae_encoder.encode(_UpperCamelCase			,					_UpperCamelCase    )[0].sample()
  onnx_export(
      _UpperCamelCase			,					model_args=(
          torch.randn(1			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ).to(device=_UpperCamelCase			,					dtype=_UpperCamelCase    ),
          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=_UpperCamelCase			,					)

  # VAE DECODER
  _lowercase:      Any   			=  pipeline.vae
  _lowercase:      Optional[int]   			=  vae_decoder.config.latent_channels
  _lowercase:      List[str]   			=  vae_decoder.config.out_channels
  # forward only through the decoder part
  _lowercase:      Optional[Any]   			=  vae_encoder.decode
  onnx_export(
      _UpperCamelCase			,					model_args=(
          torch.randn(1			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ).to(device=_UpperCamelCase			,					dtype=_UpperCamelCase    ),
          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=_UpperCamelCase			,					)
  del pipeline.vae

  # SAFETY CHECKER
  if pipeline.safety_checker is not None:
    _lowercase:      Optional[Any]   			=  pipeline.safety_checker
    _lowercase:      Union[str, Any]   			=  safety_checker.config.vision_config.num_channels
    _lowercase:      Tuple   			=  safety_checker.config.vision_config.image_size
    _lowercase:      Union[str, Any]   			=  safety_checker.forward_onnx
    onnx_export(
        pipeline.safety_checker			,					model_args=(
            torch.randn(
                1			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					).to(device=_UpperCamelCase			,					dtype=_UpperCamelCase    ),
            torch.randn(1			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ).to(device=_UpperCamelCase			,					dtype=_UpperCamelCase    ),
        )			,					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=_UpperCamelCase			,					)
    del pipeline.safety_checker
    _lowercase:      Tuple   			=  OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker'''    )
    _lowercase:      Tuple   			=  pipeline.feature_extractor
  else:
    _lowercase:      int   			=  None
    _lowercase:      List[str]   			=  None

  _lowercase:      Union[str, Any]   			=  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=_UpperCamelCase			,					feature_extractor=_UpperCamelCase			,					requires_safety_checker=safety_checker is not None			,					)

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

  del pipeline
  del onnx_pipeline
  _lowercase:      Optional[int]   			=  OnnxStableDiffusionPipeline.from_pretrained(_UpperCamelCase			,					provider='''CPUExecutionProvider'''    )
  print('''ONNX pipeline is loadable'''    )


if __name__ == "__main__":
  A__ :       Union[str, Any]				= 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=1_4,
      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__ :       int				= parser.parse_args()

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


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A__ :       List[str]				= {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A__ :       Dict				= [
      'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BioGptForCausalLM',
      'BioGptForTokenClassification',
      'BioGptForSequenceClassification',
      'BioGptModel',
      'BioGptPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

  A__ :       int				= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
