


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCAmelCase	: Union[str, Any]  				=version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__=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        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__ = False      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		torch.floataa if fpaa else torch.floataa
						if fpaa and torch.cuda.is_available():
												__SCREAMING_SNAKE_CASE						:     Tuple					=		"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						:     Union[str, Any]					=		"cpu"
						__SCREAMING_SNAKE_CASE						:     Any					=		Path(_lowerCamelCase      )

						# VAE DECODER
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		AutoencoderKL.from_pretrained(model_path + '''/vae'''      )
						__SCREAMING_SNAKE_CASE						:     str					=		vae_decoder.config.latent_channels
						# forward only through the decoder part
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		vae_decoder.decode
						onnx_export(
						    _lowerCamelCase    ,       model_args=(
						        torch.randn(1    ,       _lowerCamelCase    ,       25    ,       25      ).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 vae_decoder


if __name__ == "__main__":
						__lowerCAmelCase	: Union[str, 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=1_4,
						    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	: Optional[Any]  				=parser.parse_args()
						print(args.output_path)
						convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
						print('SD: Done: ONNX')

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCamelCase  							=						TypeVar('''T''')



class   snake_case_       (		Generic[T]       ):
   __A	:		deque[T]  # Cache store of keys
   __A	:		set[T]  # References of the keys in cache
   __A	:		int         =				10  # Maximum capacity of cache






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     int  )					->						None:
       lowercase__ :   int    					=				deque()
       lowercase__ :   str    					=				set()
       if not n:
           lowercase__ :   str    					=				sys.maxsize
       elif n < 0:
           raise ValueError("n should be an integer greater than 0."  )
       else:
           lowercase__ :   List[Any]    					=				n






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     T  )					->						None:
       if x not in self.key_reference:
           if len(self.dq_store  ) == LRUCache._MAX_CAPACITY:
               lowercase__ :   Dict    					=				self.dq_store.pop()
               self.key_reference.remove(lowercase_  )
       else:
           self.dq_store.remove(lowercase_  )

       self.dq_store.appendleft(lowercase_  )
       self.key_reference.add(lowercase_  )






   def       __UpperCamelCase     (      self				:     Dict  )					->						None:
       for k in self.dq_store:
           print(lowercase_  )







   def __repr__(      self				:     Optional[int]  )					->						str:
       return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store  )}'''


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase  							=						LRUCache(4)
      lru_cache.refer('''A''')
      lru_cache.refer(2)
      lru_cache.refer(3)
      lru_cache.refer('''A''')
      lru_cache.refer(4)
      lru_cache.refer(5)
      lru_cache.display()

      print(lru_cache)
      assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"



