






"""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


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





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase=False,						):
				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 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase = False       ):
				_SCREAMING_SNAKE_CASE   :		Dict       		=    torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    '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   :		Dict       		=    'cpu'
				_SCREAMING_SNAKE_CASE   :		str       		=    StableDiffusionPipeline.from_pretrained(lowerCamelCase,						torch_dtype=lowerCamelCase       ).to(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    Path(lowerCamelCase       )

				# TEXT ENCODER
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    pipeline.text_encoder.config.max_position_embeddings
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    pipeline.text_encoder.config.hidden_size
				_SCREAMING_SNAKE_CASE   :		str       		=    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
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    pipeline.unet.config.in_channels
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    pipeline.unet.config.sample_size
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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,						)
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    str(unet_path.absolute().as_posix()       )
				_SCREAMING_SNAKE_CASE   :		str       		=    os.path.dirname(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		int       		=    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
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    pipeline.vae
				_SCREAMING_SNAKE_CASE   :		str       		=    vae_encoder.config.in_channels
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    vae_encoder.config.sample_size
				# need to get the raw tensor output (sample) from the encoder
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    lambda lowerCamelCase,						lowerCamelCase       : 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
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    pipeline.vae
				_SCREAMING_SNAKE_CASE   :		str       		=    vae_decoder.config.latent_channels
				_SCREAMING_SNAKE_CASE   :		str       		=    vae_decoder.config.out_channels
				# forward only through the decoder part
				_SCREAMING_SNAKE_CASE   :		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:
								_SCREAMING_SNAKE_CASE   :		Any       		=    pipeline.safety_checker
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    safety_checker.config.vision_config.num_channels
								_SCREAMING_SNAKE_CASE   :		str       		=    safety_checker.config.vision_config.image_size
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    OnnxRuntimeModel.from_pretrained(output_path / 'safety_checker'       )
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    pipeline.feature_extractor
				else:
								_SCREAMING_SNAKE_CASE   :		int       		=    None
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    None

				_SCREAMING_SNAKE_CASE   :		Any       		=    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
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    OnnxStableDiffusionPipeline.from_pretrained(lowerCamelCase,						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)









"""simple docstring"""
import operator as op





def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		int       		=    []
				_SCREAMING_SNAKE_CASE   :		Dict       		=    lambda lowerCamelCase,						lowerCamelCase       : int(x / y       )  # noqa: E731 integer division operation
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    {
				    '^': op.pow,
				    '*': op.mul,
				    '/': div,
				    '+': op.add,
				    '-': op.sub,
				}  # operators & their respective operation

				# print table header
				print('Symbol'.center(8       ),						'Action'.center(12       ),						'Stack',						sep=' | '       )
				print('-' * (30 + len(lowerCamelCase       ))       )

				for x in post_fix:
								if x.isdigit():  # if x in digit
												stack.append(lowerCamelCase       )  # append x to stack
												# output in tabular format
												print(x.rjust(8       ),						('push(' + x + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )
								else:
												_SCREAMING_SNAKE_CASE   :		int       		=    stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8       ),						('pop(' + b + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )

												_SCREAMING_SNAKE_CASE   :		Tuple       		=    stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8       ),						('pop(' + a + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )

												stack.append(
												    str(opr[x](int(lowerCamelCase       ),						int(lowerCamelCase       )       )       )       )  # evaluate the 2 values popped from stack & push result to stack
												# output in tabular format
												print(
												    x.rjust(8       ),						('push(' + a + x + b + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | ',						)

				return int(stack[0]       )


if __name__ == "__main__":
			lowerCAmelCase__    						=			input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
			print('\n\tResult = ', solve(Postfix))

