




'''simple docstring'''


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_lowercase  =			version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a    ,	a    ,	a    ,	a    ,	a=False    ,	):
						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__   (				a    ,	a    ,	a    ,	a = False							):
						__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__":
	_lowercase  =			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""")

	_lowercase  =			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'''



from timeit import timeit

__UpperCamelCase   :							int         =   {
    """MALAYALAM""": True,
    """String""": False,
    """rotor""": True,
    """level""": True,
    """A""": True,
    """BB""": True,
    """ABC""": False,
    """amanaplanacanalpanama""": True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   __a					  =     0
   __a					  =     len(SCREAMING_SNAKE_CASE__     ) - 1
   while start_i < end_i:
      if s[start_i] == s[end_i]:
         start_i += 1
         end_i -= 1
      else:
         return False
   return True



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   __a					  =     len(SCREAMING_SNAKE_CASE__     ) // 2
   __a					  =     len(SCREAMING_SNAKE_CASE__     )

   # We need to traverse till half of the length of string
   # as we can get access of the i'th last element from
   # i'th index.
   # eg: [0,1,2,3,4,5] => 4th index can be accessed
   # with the help of 1st index (i==n-i-1)
   # where n is length of string
   return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   if len(SCREAMING_SNAKE_CASE__     ) <= 2:
      return True
   if s[0] == s[len(SCREAMING_SNAKE_CASE__     ) - 1]:
      return is_palindrome_recursive(s[1:-1]     )
   else:
      return False



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   return s == s[::-1]



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      None:

   """simple docstring"""
   __a					  =     f"""all({name}(key) is value for key, value in test_data.items())"""
   __a					  =     f"""from __main__ import test_data, {name}"""
   __a					  =     500000
   __a					  =     timeit(stmt=SCREAMING_SNAKE_CASE__,       setup=SCREAMING_SNAKE_CASE__,       number=SCREAMING_SNAKE_CASE__     )
   print(f"""{name:<35} finished {number:,} runs in {result:.5f} seconds"""     )


if __name__ == "__main__":
 for key, value in test_data.items():
  assert is_palindrome(key) is is_palindrome_recursive(key)
  assert is_palindrome(key) is is_palindrome_slice(key)
  print(f"""{key:21} {value}""")
 print("""a man a plan a canal panama""")

 # finished 500,000 runs in 0.46793 seconds
 benchmark_function("""is_palindrome_slice""")
 # finished 500,000 runs in 0.85234 seconds
 benchmark_function("""is_palindrome""")
 # finished 500,000 runs in 1.32028 seconds
 benchmark_function("""is_palindrome_recursive""")
 # finished 500,000 runs in 2.08679 seconds
 benchmark_function("""is_palindrome_traversal""")