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






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

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

			_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=__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
			_snake_case		=			OnnxStableDiffusionPipeline.from_pretrained(__lowercase       ,					provider='CPUExecutionProvider' )
			print('ONNX pipeline is loadable' )


if __name__ == "__main__":
			_lowerCamelCase       :				List[str]       =			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)

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  tuple[int | None, int | None, float]:
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			_snake_case		=			(low + high) // 2
			_snake_case	,					_snake_case	,					_snake_case		=			max_subarray(__lowercase       ,					__lowercase       ,					__lowercase )
			_snake_case	,					_snake_case	,					_snake_case		=			max_subarray(__lowercase       ,					mid + 1       ,					__lowercase )
			_snake_case	,					_snake_case	,					_snake_case		=			max_cross_sum(__lowercase       ,					__lowercase       ,					__lowercase       ,					__lowercase )
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum






def        a_		(       __lowercase   :	Sequence[float]       ,					__lowercase   :	int       ,					__lowercase   :	int       ,					__lowercase   :	int ) ->  tuple[int, int, float]:
			_snake_case	,					_snake_case		=			float('-inf' ), -1
			_snake_case	,					_snake_case		=			float('-inf' ), -1

			_snake_case		=			0
			for i in range(__lowercase       ,					low - 1       ,					-1 ):
						summ += arr[i]
						if summ > left_sum:
									_snake_case		=			summ
									_snake_case		=			i

			_snake_case		=			0
			for i in range(mid + 1       ,					high + 1 ):
						summ += arr[i]
						if summ > right_sum:
									_snake_case		=			summ
									_snake_case		=			i

			return max_left, max_right, (left_sum + right_sum)






def        a_		(       __lowercase   :	int ) ->  float:
			_snake_case		=			[randint(1       ,					__lowercase ) for _ in range(__lowercase )]
			_snake_case		=			time.time()
			max_subarray(__lowercase       ,					0       ,					input_size - 1 )
			_snake_case		=			time.time()
			return end - start






def        a_		(       ) ->  None:
			_snake_case		=			[10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
			_snake_case		=			[time_max_subarray(__lowercase ) for input_size in input_sizes]
			print('No of Inputs\t\tTime Taken' )
			for input_size, runtime in zip(__lowercase       ,					__lowercase ):
						print(__lowercase       ,					'\t\t'       ,					__lowercase )
			plt.plot(__lowercase       ,					__lowercase )
			plt.xlabel('Number of Inputs' )
			plt.ylabel('Time taken in seconds' )
			plt.show()


if __name__ == "__main__":
			from doctest import testmod

			testmod()