





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 :   Any  			=  version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False      ,  )    ->    List[str]:







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



@torch.no_grad()
def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      lowerCamelCase__       :						Union[str, Any]        =							"""cuda"""
   elif fpaa and not torch.cuda.is_available():
      raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''					)
   else:
      lowerCamelCase__       :						str        =							"""cpu"""
   lowerCamelCase__       :						str        =							StableDiffusionPipeline.from_pretrained(_lowerCAmelCase      ,  torch_dtype=_lowerCAmelCase					).to(_lowerCAmelCase					)
   lowerCamelCase__       :						Union[str, Any]        =							Path(_lowerCAmelCase					)

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

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

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

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

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

   lowerCamelCase__       :						Tuple        =							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=_lowerCAmelCase      ,  feature_extractor=_lowerCAmelCase      ,  requires_safety_checker=safety_checker is not None      ,  )

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

   del pipeline
   del onnx_pipeline
   lowerCamelCase__       :						List[str]        =							OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase      ,  provider='''CPUExecutionProvider'''					)
   print('''ONNX pipeline is loadable'''					)


if __name__ == "__main__":
       _A :   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')

       _A :   Any  			=  parser.parse_args()

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













import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
       # Create universe of discourse in Python using linspace ()
       _A :   Optional[int]  			=  np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

       # Create two fuzzy sets by defining any membership function
       # (trapmf(), gbellmf(), gaussmf(), etc).
       _A :   Any  			=  [0, 25, 50]
       _A :   Dict  			=  [25, 50, 75]
       _A :   Any  			=  fuzz.membership.trimf(X, abca)
       _A :   List[Any]  			=  fuzz.membership.trimf(X, abca)

       # Compute the different operations using inbuilt functions.
       _A :   List[str]  			=  np.ones(75)
       _A :   Optional[int]  			=  np.zeros((75,))
       # 1. Union = max(µA(x), µB(x))
       _A :   Optional[Any]  			=  fuzz.fuzzy_or(X, young, X, middle_aged)[1]
       # 2. Intersection = min(µA(x), µB(x))
       _A :   Union[str, Any]  			=  fuzz.fuzzy_and(X, young, X, middle_aged)[1]
       # 3. Complement (A) = (1- min(µA(x))
       _A :   List[Any]  			=  fuzz.fuzzy_not(young)
       # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
       _A :   Tuple  			=  fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
       # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
       _A :   List[Any]  			=  young + middle_aged - (young * middle_aged)
       # 6. Algebraic Product = (µA(x) * µB(x))
       _A :   Optional[Any]  			=  young * middle_aged
       # 7. Bounded Sum = min[1,(µA(x), µB(x))]
       _A :   Dict  			=  fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
       # 8. Bounded difference = min[0,(µA(x), µB(x))]
       _A :   List[str]  			=  fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

       # max-min composition
       # max-product composition

       # Plot each set A, set B and each operation result using plot() and subplot().
       from matplotlib import pyplot as plt

       plt.figure()

       plt.subplot(4, 3, 1)
       plt.plot(X, young)
       plt.title('Young')
       plt.grid(True)

       plt.subplot(4, 3, 2)
       plt.plot(X, middle_aged)
       plt.title('Middle aged')
       plt.grid(True)

       plt.subplot(4, 3, 3)
       plt.plot(X, union)
       plt.title('union')
       plt.grid(True)

       plt.subplot(4, 3, 4)
       plt.plot(X, intersection)
       plt.title('intersection')
       plt.grid(True)

       plt.subplot(4, 3, 5)
       plt.plot(X, complement_a)
       plt.title('complement_a')
       plt.grid(True)

       plt.subplot(4, 3, 6)
       plt.plot(X, difference)
       plt.title('difference a/b')
       plt.grid(True)

       plt.subplot(4, 3, 7)
       plt.plot(X, alg_sum)
       plt.title('alg_sum')
       plt.grid(True)

       plt.subplot(4, 3, 8)
       plt.plot(X, alg_product)
       plt.title('alg_product')
       plt.grid(True)

       plt.subplot(4, 3, 9)
       plt.plot(X, bdd_sum)
       plt.title('bdd_sum')
       plt.grid(True)

       plt.subplot(4, 3, 10)
       plt.plot(X, bdd_difference)
       plt.title('bdd_difference')
       plt.grid(True)

       plt.subplots_adjust(hspace=0.5)
       plt.show()



