





'''simple docstring'''



import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = 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__":
 __magic_name__   :						Tuple    =			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""")

 __magic_name__   :						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""")









'''simple docstring'''



import math


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				0.0
       _snake_case     		=				0.0
       for i in range(len(lowerCamelCase	)	):
         da += math.pow((sample[i] - weights[0][i])						,   2	)
         da += math.pow((sample[i] - weights[1][i])						,   2	)
         return 0 if da > da else 1
       return 0






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       for i in range(len(lowerCamelCase	)	):
         weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights







def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

  # weight initialization ( n, C )
  _snake_case     		=				[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

  # training
  _snake_case     		=				SelfOrganizingMap()
  _snake_case     		=				3
  _snake_case     		=				0.5

  for _ in range(SCREAMING_SNAKE_CASE__       ):
    for j in range(len(SCREAMING_SNAKE_CASE__       )       ):
      # training sample
      _snake_case     		=				training_samples[j]

      # Compute the winning vector
      _snake_case     		=				self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

      # Update the winning vector
      _snake_case     		=				self_organizing_map.update(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

    # classify test sample
  _snake_case     		=				[0, 0, 0, 1]
  _snake_case     		=				self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

  # results
  print(f'''Clusters that the test sample belongs to : {winner}'''       )
  print(f'''Weights that have been trained : {weights}'''       )


# running the main() function
if __name__ == "__main__":
 main()


