
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


snake_case__      :		int				=       version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def 					lowerCamelCase__		(				_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase=False			,		)						->Tuple:
 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 					lowerCamelCase__		(				_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase = False  )						->Tuple:
 _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(SCREAMING_SNAKE_CASE__			,		torch_dtype=SCREAMING_SNAKE_CASE__  ).to(SCREAMING_SNAKE_CASE__  )
 _UpperCAmelCase 				=Path(SCREAMING_SNAKE_CASE__  )

 # 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=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
 _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			,		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__			,		)
 _UpperCAmelCase 				=str(unet_path.absolute().as_posix()  )
 _UpperCAmelCase 				=os.path.dirname(SCREAMING_SNAKE_CASE__  )
 _UpperCAmelCase 				=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
 _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 _lowerCamelCase			,		_lowerCamelCase  : 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
 _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(
     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:
  _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			,		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
  _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=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
 _UpperCAmelCase 				=OnnxStableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__			,		provider="CPUExecutionProvider"  )
 print("ONNX pipeline is loadable"  )


if __name__ == "__main__":
       snake_case__      :		int				=       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')

       snake_case__      :		Optional[int]				=       parser.parse_args()

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










"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class       UpperCAmelCase_    (							_UpperCamelCase		):



      def __lt__(	self	:      int	,      A	:      Dict		):
           return self[-1] < other[-1]



      def __eq__(	self	:      Union[str, Any]	,      A	:      Union[str, Any]		):
           return self[-1] == other[-1]







def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list							)    ->  list:





     '''simple docstring'''

     _UpperCAmelCase     :      list[Stack]														=       []
     # sort into stacks
     for element in collection:
          _UpperCAmelCase     :      Any														=       Stack([element]							)
          _UpperCAmelCase     :      str														=       bisect_left(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          if i != len(SCREAMING_SNAKE_CASE__							):
               stacks[i].append(SCREAMING_SNAKE_CASE__							)
          else:
               stacks.append(SCREAMING_SNAKE_CASE__							)

    # use a heap-based merge to merge stack efficiently
     _UpperCAmelCase     :      Optional[Any]														=       merge(*(reversed(SCREAMING_SNAKE_CASE__							) for stack in stacks)							)
     return collection


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