


"""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


UpperCAmelCase__  							=version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: tuple				,   UpperCamelCase__	: Path				,   UpperCamelCase__	: List[Any]				,   UpperCamelCase__	: List[str]				,   UpperCamelCase__	: List[str]				,   UpperCamelCase__	: Dict				,   UpperCamelCase__	: Any=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__	: str				,   UpperCamelCase__	: str				,   UpperCamelCase__	: int				,   UpperCamelCase__	: bool = False ):






 """simple docstring"""







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

 # TEXT ENCODER
 __lowercase									=	pipeline.text_encoder.config.max_position_embeddings
 __lowercase									=	pipeline.text_encoder.config.hidden_size
 __lowercase									=	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									=	pipeline.unet.config.in_channels
 __lowercase									=	pipeline.unet.config.sample_size
 __lowercase									=	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									=	str(unet_path.absolute().as_posix() )
 __lowercase									=	os.path.dirname(UpperCamelCase__ )
 __lowercase									=	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									=	pipeline.vae
 __lowercase									=	vae_encoder.config.in_channels
 __lowercase									=	vae_encoder.config.sample_size
 # need to get the raw tensor output (sample) from the encoder
 __lowercase									=	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									=	pipeline.vae
 __lowercase									=	vae_decoder.config.latent_channels
 __lowercase									=	vae_decoder.config.out_channels
 # forward only through the decoder part
 __lowercase									=	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									=	pipeline.safety_checker
  __lowercase									=	safety_checker.config.vision_config.num_channels
  __lowercase									=	safety_checker.config.vision_config.image_size
  __lowercase									=	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									=	OnnxRuntimeModel.from_pretrained(output_path / """safety_checker""" )
  __lowercase									=	pipeline.feature_extractor
 else:
  __lowercase									=	None
  __lowercase									=	None

 __lowercase									=	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									=	OnnxStableDiffusionPipeline.from_pretrained(UpperCamelCase__				,   provider="""CPUExecutionProvider""" )
 print("""ONNX pipeline is loadable""" )


if __name__ == "__main__":
       UpperCAmelCase__  							=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")

       UpperCAmelCase__  							=parser.parse_args()

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






"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: int				,   UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	[False] * len(UpperCamelCase__ )
 __lowercase									=	[]
 queue.append(UpperCamelCase__ )
 __lowercase									=	True

 while queue:
  __lowercase									=	queue.pop(0 )
  for ind in range(len(graph[u] ) ):
   if visited[ind] is False and graph[u][ind] > 0:
    queue.append(UpperCamelCase__ )
    __lowercase									=	True
    __lowercase									=	u

 return visited[t]


def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: Any				,   UpperCamelCase__	: Optional[int] ):






 """simple docstring"""







 __lowercase									=	[-1] * (len(UpperCamelCase__ ))
 __lowercase									=	0
 while bfs(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ ):
  __lowercase									=	float("""Inf""" )
  __lowercase									=	sink

  while s != source:
   # Find the minimum value in select path
   __lowercase									=	min(UpperCamelCase__				,   graph[parent[s]][s] )
   __lowercase									=	parent[s]

  max_flow += path_flow
  __lowercase									=	sink

  while v != source:
   __lowercase									=	parent[v]
   graph[u][v] -= path_flow
   graph[v][u] += path_flow
   __lowercase									=	parent[v]
 return max_flow


UpperCAmelCase__  							=[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCAmelCase__							,      UpperCAmelCase__  							=0, 5

print(ford_fulkerson(graph, source, sink))
