

from __future__ import annotations

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

UpperCamelCase__		 =     TypeVar('''T''')

class     __snake_case    (				Generic[T]  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							42  # Cache store of keys
       __SCREAMING_SNAKE_CASE				    =							42  # References of the keys in cache
       __SCREAMING_SNAKE_CASE				    =							10  # Maximum capacity of cache


       def __init__(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							deque()
              __snake_case       	=							set()
              if not n:
                     __snake_case       	=							sys.maxsize
              elif n < 0:
                     raise ValueError("""n should be an integer greater than 0."""   )
              else:
                     __snake_case       	=							n


       def 			a		(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              if x not in self.key_reference:
                     if len(self.dq_store   ) == LRUCache._MAX_CAPACITY:
                            __snake_case       	=							self.dq_store.pop()
                            self.key_reference.remove(_UpperCamelCase   )
              else:
                     self.dq_store.remove(_UpperCamelCase   )

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


       def 			a		(		self   )      ->							None:






              """simple docstring"""


              for k in self.dq_store:
                     print(_UpperCamelCase   )






       def __repr__(		self   )      ->							str:






              """simple docstring"""


              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]"




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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







def 						lowerCamelCase__      ( __A    :Any	,__A    :tuple	,__A    :Path	,__A    :int	,__A    :Union[str, Any]	,__A    :Optional[Any]	,__A    :Optional[Any]	,__A    :List[Any]=False	,):


       """simple docstring"""




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







@torch.no_grad()
def 						lowerCamelCase__      ( __A    :str	,__A    :str	,__A    :int	,__A    :bool = 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(__A		)

       # 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(
           __A	,model_args=(
               torch.randn(1	,__A	,2_5	,2_5		).to(device=__A	,dtype=__A		),
               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=__A	,)
       del vae_decoder


if __name__ == "__main__":
    UpperCamelCase__		 =     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__		 =     parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print('''SD: Done: ONNX''')

