




"""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  UpperCAmelCase__	(snake_case__							: Tuple		,       snake_case__							: tuple		,       snake_case__							: Path		,       snake_case__							: Tuple		,       snake_case__							: List[str]		,       snake_case__							: int		,       snake_case__							: Union[str, Any]		,       snake_case__							: Dict=False		,       ):
		"""simple docstring"""

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


@torch.no_grad()
def  UpperCAmelCase__	(snake_case__							: str		,       snake_case__							: str		,       snake_case__							: int		,       snake_case__							: bool = False   ):
		"""simple docstring"""

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

		# TEXT ENCODER
		_snake_case							:		Dict								=  pipeline.text_encoder.config.max_position_embeddings
		_snake_case							:		List[Any]								=  pipeline.text_encoder.config.hidden_size
		_snake_case							:		List[Any]								=  pipeline.tokenizer(
		    """A sample prompt"""		,       padding="""max_length"""		,       max_length=pipeline.tokenizer.model_max_length		,       truncation=snake_case__		,       return_tensors="""pt"""		,       )
		onnx_export(
		    pipeline.text_encoder		,       model_args=(text_input.input_ids.to(device=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=snake_case__		,       )
		del pipeline.text_encoder

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

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

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

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

		_snake_case							:		Dict								=  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=snake_case__		,       feature_extractor=snake_case__		,       requires_safety_checker=safety_checker is not None		,       )

		onnx_pipeline.save_pretrained(snake_case__   )
		print("""ONNX pipeline saved to"""		,       snake_case__   )

		del pipeline
		del onnx_pipeline
		_snake_case							:		int								=  OnnxStableDiffusionPipeline.from_pretrained(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)








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 1_00_00_00   ):
  """simple docstring"""

  _snake_case							:		Dict								=  [i - 1 for i in range(limit + 1   )]

  for i in range(2		,       limit + 1   ):
    if phi[i] == i - 1:
      for j in range(2 * i		,       limit + 1		,       snake_case__   ):
        phi[j] -= phi[j] // i

  return sum(phi[2 : limit + 1]   )


if __name__ == "__main__":
     print(solution())


