

import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


_UpperCAmelCase						:  Tuple									=			version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=False  ,				):







							'''simple docstring'''






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





@torch.no_grad()
def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ = False ):







							'''simple docstring'''






							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_     							=       Path(_SCREAMING_SNAKE_CASE )

							# VAE DECODER
							snake_case_     							=       AutoencoderKL.from_pretrained(model_path + '/vae' )
							snake_case_     							=       vae_decoder.config.latent_channels
							# forward only through the decoder part
							snake_case_     							=       vae_decoder.decode
							onnx_export(
							    _SCREAMING_SNAKE_CASE  ,				model_args=(
							        torch.randn(1  ,				_SCREAMING_SNAKE_CASE  ,				25  ,				25 ).to(device=_SCREAMING_SNAKE_CASE  ,				dtype=_SCREAMING_SNAKE_CASE ),
							        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=_SCREAMING_SNAKE_CASE  ,				)
							del vae_decoder


if __name__ == "__main__":
					_UpperCAmelCase						:  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""")

					_UpperCAmelCase						:  Optional[int]									=			parser.parse_args()
					print(args.output_path)
					convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
					print("""SD: Done: ONNX""")








'''simple docstring'''





class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			Tuple			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Dict	)      ->		List[str]:
						__A=	name
						__A=	val
			def __str__(  self							:			int	)      ->		List[Any]:
						return F"""{self.__class__.__name__}({self.name}, {self.val})"""

			def __lt__(  self							:			Optional[Any]			,    lowerCAmelCase_							:			str	)      ->		List[Any]:
						return self.val < other.val


class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			Tuple			,    lowerCAmelCase_							:			int	)      ->		Any:
						__A=	{}
						__A=	{}
						__A=	self.build_heap(lowerCAmelCase_	)
			def __getitem__(  self							:			int			,    lowerCAmelCase_							:			Dict	)      ->		Tuple:
						return self.get_value(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						return (idx - 1) // 2
			def 					lowerCAmelCase     (  self							:			Union[str, Any]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		List[str]:
						return idx * 2 + 1
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Any:
						return idx * 2 + 2
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Dict:
						return self.heap_dict[key]
			def 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			List[Any]	)      ->		Tuple:
						__A=	len(lowerCAmelCase_	) - 1
						__A=	self.get_parent_idx(lowerCAmelCase_	)

						for idx, i in enumerate(lowerCAmelCase_	):
									__A=	idx
									__A=	i.val

						for i in range(lowerCAmelCase_			,    -1			,    -1	):
									self.sift_down(lowerCAmelCase_			,    lowerCAmelCase_	)
						return array
			def 					lowerCAmelCase     (  self							:			int			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			Tuple	)      ->		Dict:
						while True:
									__A=	self.get_left_child_idx(lowerCAmelCase_	)  # noqa: E741
									__A=	self.get_right_child_idx(lowerCAmelCase_	)

									__A=	idx
									if l < len(lowerCAmelCase_	) and array[l] < array[idx]:
												__A=	l
									if r < len(lowerCAmelCase_	) and array[r] < array[smallest]:
												__A=	r

									if smallest != idx:
												__A,	__A=	array[smallest], array[idx]
												(
												    (
												    __A
												),	(
												    __A
												),	
												)=	(
												    self.idx_of_element[array[smallest]],
												    self.idx_of_element[array[idx]],
												)
												__A=	smallest
									else:
												break
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			Dict	)      ->		str:
						__A=	self.get_parent_idx(lowerCAmelCase_	)
						while p >= 0 and self.heap[p] > self.heap[idx]:
									__A,	__A=	self.heap[idx], self.heap[p]
									__A,	__A=	(
									    self.idx_of_element[self.heap[idx]],
									    self.idx_of_element[self.heap[p]],
									)
									__A=	p
									__A=	self.get_parent_idx(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						return self.heap[0]
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		int:
						__A,	__A=	self.heap[-1], self.heap[0]
						__A,	__A=	(
						    self.idx_of_element[self.heap[-1]],
						    self.idx_of_element[self.heap[0]],
						)

						__A=	self.heap.pop()
						del self.idx_of_element[x]
						self.sift_down(0			,    self.heap	)
						return x
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Optional[int]:
						self.heap.append(lowerCAmelCase_	)
						__A=	len(self.heap	) - 1
						__A=	node.val
						self.sift_up(len(self.heap	) - 1	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						return len(self.heap	) == 0

			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			List[Any]	)      ->		Optional[Any]:
						assert (
						    self.heap[self.idx_of_element[node]].val > new_value
						), "newValue must be less that current value"
						__A=	new_value
						__A=	new_value
						self.sift_up(self.idx_of_element[node]	)


UpperCAmelCase__  			=				Node('''R''', -1)
UpperCAmelCase__  			=				Node('''B''', 6)
UpperCAmelCase__  			=				Node('''A''', 3)
UpperCAmelCase__  			=				Node('''X''', 1)
UpperCAmelCase__  			=				Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
UpperCAmelCase__  			=				MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
					print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
					print(i)

if __name__ == "__main__":
					import doctest

					doctest.testmod()



