



'''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


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







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    ,						A__    ,						A__    ,						A__=False    ,						)       ->	List[str]:






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







@torch.no_grad()
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__ = False    )       ->	List[str]:






   """simple docstring"""
   UpperCamelCase					=  torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      UpperCamelCase					=  'cuda'
   elif fpaa and not torch.cuda.is_available():
      raise ValueError('`float16` model export is only supported on GPUs with CUDA'    )
   else:
      UpperCamelCase					=  'cpu'
   UpperCamelCase					=  StableDiffusionPipeline.from_pretrained(A__    ,						torch_dtype=A__    ).to(A__    )
   UpperCamelCase					=  Path(A__    )

   # TEXT ENCODER
   UpperCamelCase					=  pipeline.text_encoder.config.max_position_embeddings
   UpperCamelCase					=  pipeline.text_encoder.config.hidden_size
   UpperCamelCase					=  pipeline.tokenizer(
       'A sample prompt'    ,						padding='max_length'    ,						max_length=pipeline.tokenizer.model_max_length    ,						truncation=A__    ,						return_tensors='pt'    ,						)
   onnx_export(
       pipeline.text_encoder    ,						model_args=(text_input.input_ids.to(device=A__    ,						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=A__    ,						)
   del pipeline.text_encoder

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

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

   # VAE DECODER
   UpperCamelCase					=  pipeline.vae
   UpperCamelCase					=  vae_decoder.config.latent_channels
   UpperCamelCase					=  vae_decoder.config.out_channels
   # forward only through the decoder part
   UpperCamelCase					=  vae_encoder.decode
   onnx_export(
       A__    ,						model_args=(
           torch.randn(1    ,						A__    ,						A__    ,						A__    ).to(device=A__    ,						dtype=A__    ),
           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=A__    ,						)
   del pipeline.vae

   # SAFETY CHECKER
   if pipeline.safety_checker is not None:
      UpperCamelCase					=  pipeline.safety_checker
      UpperCamelCase					=  safety_checker.config.vision_config.num_channels
      UpperCamelCase					=  safety_checker.config.vision_config.image_size
      UpperCamelCase					=  safety_checker.forward_onnx
      onnx_export(
          pipeline.safety_checker    ,						model_args=(
              torch.randn(
                  1    ,						A__    ,						A__    ,						A__    ,						).to(device=A__    ,						dtype=A__    ),
              torch.randn(1    ,						A__    ,						A__    ,						A__    ).to(device=A__    ,						dtype=A__    ),
          )    ,						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=A__    ,						)
      del pipeline.safety_checker
      UpperCamelCase					=  OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'    )
      UpperCamelCase					=  pipeline.feature_extractor
   else:
      UpperCamelCase					=  None
      UpperCamelCase					=  None

   UpperCamelCase					=  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=A__    ,						feature_extractor=A__    ,						requires_safety_checker=safety_checker is not None    ,						)

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

   del pipeline
   del onnx_pipeline
   UpperCamelCase					=  OnnxStableDiffusionPipeline.from_pretrained(A__    ,						provider='CPUExecutionProvider'    )
   print('ONNX pipeline is loadable'    )


if __name__ == "__main__":
   _lowerCamelCase	:       Dict      	=	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")

   _lowerCamelCase	:       Union[str, Any]      	=	parser.parse_args()

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






'''simple docstring'''


def 				__lowerCamelCase				(   A__    )       ->	list:






   """simple docstring"""
   UpperCamelCase					=  len(A__    )
   for i in range(1    ,						A__    ):
      UpperCamelCase					=  collection[i]
      UpperCamelCase					=  0
      UpperCamelCase					=  i - 1

      while low <= high:
         UpperCamelCase					=  (low + high) // 2
         if val < collection[mid]:
            UpperCamelCase					=  mid - 1
         else:
            UpperCamelCase					=  mid + 1
      for j in range(A__    ,						A__    ,						-1    ):
         UpperCamelCase					=  collection[j - 1]
      UpperCamelCase					=  val
   return collection


if __name__ == "__main__":
   _lowerCamelCase	:       int      	=	input("Enter numbers separated by a comma:\n").strip()
   _lowerCamelCase	:       Union[str, Any]      	=	[int(item) for item in user_input.split(",")]
   print(binary_insertion_sort(unsorted))

