




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       :	List[Any]									=     version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Any			,							lowercase			: tuple			,							lowercase			: Path			,							lowercase			: int			,							lowercase			: Optional[int]			,							lowercase			: Union[str, Any]			,							lowercase			: str			,							lowercase			: List[Any]=False			,							):


      '''simple docstring'''



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

@torch.no_grad()
def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: str			,							lowercase			: int			,							lowercase			: bool = False  ):


      '''simple docstring'''



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

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

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

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

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

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

      lowerCamelCase_	  =					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=lowercase			,							feature_extractor=lowercase			,							requires_safety_checker=safety_checker is not None			,							)

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

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


if __name__ == "__main__":
 lowerCamelCase       :	Optional[Any]									=     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)









import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowerCamelCase       :	str									=     imread(r"digital_image_processing/image_data/lena_small.jpg")
lowerCamelCase       :	Optional[Any]									=     cvtColor(img, COLOR_BGR2GRAY)

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					cn.convert_to_negative(lowercase  )
      # assert negative_img array for at least one True
      assert negative_img.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      with Image.open('digital_image_processing/image_data/lena_small.jpg'  ) as img:
            # Work around assertion for response
            assert str(cc.change_contrast(lowercase			,							1_10  )  ).startswith(
                '<PIL.Image.Image image mode=RGB size=100x100 at'  )

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					canny.gen_gaussian_kernel(9			,							sigma=1.4  )
      # Assert ambiguous array
      assert resp.all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					imread('digital_image_processing/image_data/lena_small.jpg'			,							0  )
      # assert ambiguous array for all == True
      assert canny_img.all()
      lowerCamelCase_	  =					canny.canny(lowercase  )
      # assert canny array for at least one True
      assert canny_array.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert gg.gaussian_filter(lowercase			,							5			,							sigma=0.9  ).all()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]  )
      lowerCamelCase_	  =					conv.img_convolve(lowercase			,							lowercase  ).astype(lowercase  )
      assert res.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      assert med.median_filter(lowercase			,							3  ).any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_  ,       lowerCamelCase_	  =					sob.sobel_filter(lowercase  )
      assert grad.any() and theta.any()

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					sp.make_sepia(lowercase			,							20  )
      assert sepia.all()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"  ):


      '''simple docstring'''



      lowerCamelCase_	  =					bs.Burkes(imread(lowercase			,							1  )			,							1_20  )
      burkes.process()
      assert burkes.output_img.any()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = "digital_image_processing/image_data/lena_small.jpg"			,							):


      '''simple docstring'''



      lowerCamelCase_	  =					rs.NearestNeighbour(imread(lowercase			,							1  )			,							4_00			,							2_00  )
      nn.process()
      assert nn.output.any()






def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					'digital_image_processing/image_data/lena.jpg'

      # Reading the image and converting it to grayscale.
      lowerCamelCase_	  =					imread(lowercase			,							0  )

      # Test for get_neighbors_pixel function() return not None
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					0
      lowerCamelCase_	  =					image[x_coordinate][y_coordinate]

      lowerCamelCase_	  =					lbp.get_neighbors_pixel(
          lowercase			,							lowercase			,							lowercase			,							lowercase  )

      assert neighbors_pixels is not None

      # Test for local_binary_pattern function()
      # Create a numpy array as the same height and width of read image
      lowerCamelCase_	  =					np.zeros((image.shape[0], image.shape[1])  )

      # Iterating through the image and calculating the local binary pattern value
      # for each pixel.
      for i in range(0			,							image.shape[0]  ):
            for j in range(0			,							image.shape[1]  ):
                  lowerCamelCase_	  =					lbp.local_binary_value(lowercase			,							lowercase			,							lowercase  )

      assert lbp_image.any()



