






"""simple docstring"""



import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_lowerCAmelCase	:	int				=      version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : tuple			,					snake_case       : Path			,					snake_case       : Any			,					snake_case       : int			,					snake_case       : List[str]			,					snake_case       : List[Any]			,					snake_case       : str=False			,					)->		Tuple:






     '''simple docstring'''



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


@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : str			,					snake_case       : int			,					snake_case       : bool = False				)->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Any							=							torch.floataa if fpaa else torch.floataa
     if fpaa and torch.cuda.is_available():
          UpperCAmelCase__						:    int							=							"cuda"
     elif fpaa and not torch.cuda.is_available():
          raise ValueError("`float16` model export is only supported on GPUs with CUDA"				)
     else:
          UpperCAmelCase__						:    Any							=							"cpu"
     UpperCAmelCase__						:    Union[str, Any]							=							Path(snake_case				)

     # VAE DECODER
     UpperCAmelCase__						:    Dict							=							AutoencoderKL.from_pretrained(model_path + "/vae"				)
     UpperCAmelCase__						:    Dict							=							vae_decoder.config.latent_channels
     # forward only through the decoder part
     UpperCAmelCase__						:    Tuple							=							vae_decoder.decode
     onnx_export(
         snake_case			,					model_args=(
             torch.randn(1			,					snake_case			,					25			,					25				).to(device=snake_case			,					dtype=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=snake_case			,					)
     del vae_decoder


if __name__ == "__main__":
 _lowerCAmelCase	:	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""")

 _lowerCAmelCase	:	Tuple				=      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 numpy as np


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : np.ndarray			,					snake_case       : np.ndarray			,					snake_case       : float = 1E-1_2			,					snake_case       : int = 100			,					)->		tuple[float, np.ndarray]:






     '''simple docstring'''



     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[1]
     # Ensure proper dimensionality.
     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(snake_case				) == np.iscomplexobj(snake_case				)
     UpperCAmelCase__						:    str							=							np.iscomplexobj(snake_case				)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(snake_case			,					input_matrix.conj().T				)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     UpperCAmelCase__						:    Union[str, Any]							=							False
     UpperCAmelCase__						:    List[Any]							=							0
     UpperCAmelCase__						:    Any							=							0
     UpperCAmelCase__						:    List[str]							=							1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          UpperCAmelCase__						:    Optional[Any]							=							np.dot(snake_case			,					snake_case				)
          # Normalize the resulting output vector.
          UpperCAmelCase__						:    Tuple							=							w / np.linalg.norm(snake_case				)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          UpperCAmelCase__						:    Any							=							vector.conj().T if is_complex else vector.T
          UpperCAmelCase__						:    List[str]							=							np.dot(snake_case			,					np.dot(snake_case			,					snake_case				)				)

          # Check convergence.
          UpperCAmelCase__						:    Optional[Any]							=							np.abs(lambda_ - lambda_previous				) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               UpperCAmelCase__						:    Tuple							=							True

          UpperCAmelCase__						:    Tuple							=							lambda_

     if is_complex:
          UpperCAmelCase__						:    str							=							np.real(lambda_				)

     return lambda_, vector


def 	SCREAMING_SNAKE_CASE__       (  )->		None:






     '''simple docstring'''



     UpperCAmelCase__						:    List[Any]							=							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
     UpperCAmelCase__						:    Dict							=							np.array([41, 4, 20]				)
     UpperCAmelCase__						:    Union[str, Any]							=							real_input_matrix.astype(np.complexaaa				)
     UpperCAmelCase__						:    Optional[int]							=							np.triu(1J * complex_input_matrix			,					1				)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     UpperCAmelCase__						:    int							=							np.array([41, 4, 20]				).astype(np.complexaaa				)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               UpperCAmelCase__						:    List[str]							=							real_input_matrix
               UpperCAmelCase__						:    Any							=							real_vector
          elif problem_type == "complex":
               UpperCAmelCase__						:    str							=							complex_input_matrix
               UpperCAmelCase__						:    List[Any]							=							complex_vector

          # Our implementation.
          UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							power_iteration(snake_case			,					snake_case				)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							np.linalg.eigh(snake_case				)
          # Last eigenvalue is the maximum one.
          UpperCAmelCase__						:    str							=							eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          UpperCAmelCase__						:    Optional[int]							=							eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max				) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(snake_case				) - np.abs(snake_case				)				) <= 1E-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_power_iteration()


