





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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							tuple					,   UpperCamelCase__:							Path					,   UpperCamelCase__:							List[Any]					,   UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							Any					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Optional[int]=False					,   ):
		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 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							str					,   UpperCamelCase__:							str					,   UpperCamelCase__:							int					,   UpperCamelCase__:							bool = False    ):
		SCREAMING_SNAKE_CASE__			=      torch.floataa if fpaa else torch.floataa
		if fpaa and torch.cuda.is_available():
				SCREAMING_SNAKE_CASE__			=      '''cuda'''
		elif fpaa and not torch.cuda.is_available():
				raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""    )
		else:
				SCREAMING_SNAKE_CASE__			=      '''cpu'''
		SCREAMING_SNAKE_CASE__			=      StableDiffusionPipeline.from_pretrained(UpperCamelCase__					,   torch_dtype=UpperCamelCase__    ).to(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      Path(UpperCamelCase__    )

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

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


if __name__ == "__main__":
	_lowerCamelCase   				=			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   				=			parser.parse_args()

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






import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCamelCase   				=			logging.get_logger(__name__)  # pylint: disable=invalid-name






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[List, PIL.Image.Image, torch.Tensor]    ):
		warnings.warn(
		    """The preprocess method is deprecated and will be removed in a future version. Please"""
		    """ use VaeImageProcessor.preprocess instead"""					,   UpperCamelCase__					,   )
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return image
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [image]

		if isinstance(image[0]					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      image[0].size
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				SCREAMING_SNAKE_CASE__			=      [np.array(i.resize((w, h)					,   resample=PIL_INTERPOLATION["""lanczos"""]    )    )[None, :] for i in image]
				SCREAMING_SNAKE_CASE__			=      np.concatenate(UpperCamelCase__					,   axis=0    )
				SCREAMING_SNAKE_CASE__			=      np.array(UpperCamelCase__    ).astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE__			=      image.transpose(0					,   3					,   1					,   2    )
				SCREAMING_SNAKE_CASE__			=      2.0 * image - 1.0
				SCREAMING_SNAKE_CASE__			=      torch.from_numpy(UpperCamelCase__    )
		elif isinstance(image[0]					,   torch.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__					,   dim=0    )
		return image






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[List, PIL.Image.Image, torch.Tensor]    ):
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return mask
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [mask]

		if isinstance(mask[0]					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      mask[0].size
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				SCREAMING_SNAKE_CASE__			=      [np.array(m.convert("""L"""    ).resize((w, h)					,   resample=PIL_INTERPOLATION["""nearest"""]    )    )[None, :] for m in mask]
				SCREAMING_SNAKE_CASE__			=      np.concatenate(UpperCamelCase__					,   axis=0    )
				SCREAMING_SNAKE_CASE__			=      mask.astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE__			=      0
				SCREAMING_SNAKE_CASE__			=      1
				SCREAMING_SNAKE_CASE__			=      torch.from_numpy(UpperCamelCase__    )
		elif isinstance(mask[0]					,   torch.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__					,   dim=0    )
		return mask




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42


							def __init__(							self    :Any							,				__A    :List[Any]							,				__A    :Optional[Any]		)   ->   Union[str, Any]:



									"""simple docstring"""



									super().__init__()
									self.register_modules(unet=__A							,				scheduler=__A		)


							@torch.no_grad()
							def __call__(							self    :str							,				__A    :Union[torch.Tensor, PIL.Image.Image]							,				__A    :Union[torch.Tensor, PIL.Image.Image]							,				__A    :int = 250							,				__A    :float = 0.0							,				__A    :int = 10							,				__A    :int = 10							,				__A    :Optional[Union[torch.Generator, List[torch.Generator]]] = None							,				__A    :Optional[str] = "pil"							,				__A    :bool = True							,				)   ->   Union[ImagePipelineOutput, Tuple]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      image

									SCREAMING_SNAKE_CASE__			=      _preprocess_image(__A		)
									SCREAMING_SNAKE_CASE__			=      original_image.to(device=self.device							,				dtype=self.unet.dtype		)
									SCREAMING_SNAKE_CASE__			=      _preprocess_mask(__A		)
									SCREAMING_SNAKE_CASE__			=      mask_image.to(device=self.device							,				dtype=self.unet.dtype		)

									SCREAMING_SNAKE_CASE__			=      original_image.shape[0]

									# sample gaussian noise to begin the loop
									if isinstance(__A							,				__A		) and len(__A		) != batch_size:
											raise ValueError(
											    f'''You have passed a list of generators of length {len(__A		)}, but requested an effective batch'''
											    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

									SCREAMING_SNAKE_CASE__			=      original_image.shape
									SCREAMING_SNAKE_CASE__			=      randn_tensor(__A							,				generator=__A							,				device=self.device							,				dtype=self.unet.dtype		)

									# set step values
									self.scheduler.set_timesteps(__A							,				__A							,				__A							,				self.device		)
									SCREAMING_SNAKE_CASE__			=      eta

									SCREAMING_SNAKE_CASE__			=      self.scheduler.timesteps[0] + 1
									SCREAMING_SNAKE_CASE__			=      generator[0] if isinstance(__A							,				__A		) else generator
									for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											if t < t_last:
													# predict the noise residual
													SCREAMING_SNAKE_CASE__			=      self.unet(__A							,				__A		).sample
													# compute previous image: x_t -> x_t-1
													SCREAMING_SNAKE_CASE__			=      self.scheduler.step(__A							,				__A							,				__A							,				__A							,				__A							,				__A		).prev_sample

											else:
													# compute the reverse: x_t-1 -> x_t
													SCREAMING_SNAKE_CASE__			=      self.scheduler.undo_step(__A							,				__A							,				__A		)
											SCREAMING_SNAKE_CASE__			=      t

									SCREAMING_SNAKE_CASE__			=      (image / 2 + 0.5).clamp(0							,				1		)
									SCREAMING_SNAKE_CASE__			=      image.cpu().permute(0							,				2							,				3							,				1		).numpy()
									if output_type == "pil":
											SCREAMING_SNAKE_CASE__			=      self.numpy_to_pil(__A		)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=__A		)