'''simple docstring'''






def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			list[list]				):
     _A    		=  current_set.copy()
     for row_index, row in enumerate(__snake_case				):
          _A    		=  row[0]
          for column_index, column in enumerate(__snake_case				):
               if magnitude == 0:
                    _A    		=  column
                    continue
               _A    		=  column / magnitude
    # Subtract to cancel term
     _A    		=  current_set[0]
     _A    		=  [first_row]
     _A    		=  current_set[1::]
     for row in current_set:
          _A    		=  []
          # If first term is 0, it is already in form we want, so we preserve it
          if row[0] == 0:
               final_set.append(__snake_case				)
               continue
          for column_index in range(len(__snake_case				)				):
               temp_row.append(first_row[column_index] - row[column_index]				)
          final_set.append(__snake_case				)
     # Create next recursion iteration set
     if len(final_set[0]				) != 3:
          _A    		=  final_set[0]
          _A    		=  []
          _A    		=  []
          for row in final_set[1::]:
               current_first_column.append(row[0]				)
               next_iteration.append(row[1::]				)
          _A    		=  simplify(__snake_case				)
          for i in range(len(__snake_case				)				):
               resultant[i].insert(0   ,		current_first_column[i]				)
          resultant.insert(0   ,		__snake_case				)
          _A    		=  resultant
     return final_set





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			list[list]				):
     if len(__snake_case				) == 0:
          raise IndexError('solve_simultaneous() requires n lists of length n+1'				)
     _A    		=  len(__snake_case				) + 1
     if any(len(__snake_case				) != _length for item in equations				):
          raise IndexError('solve_simultaneous() requires n lists of length n+1'				)
     for row in equations:
          if any(not isinstance(__snake_case   ,		(int, float)				) for column in row				):
               raise ValueError('solve_simultaneous() requires lists of integers'				)
     if len(__snake_case				) == 1:
          return [equations[0][-1] / equations[0][0]]
     _A    		=  equations.copy()
     if any(0 in row for row in data_set				):
          _A    		=  data_set.copy()
          _A    		=  []
          for row_index, row in enumerate(__snake_case				):
               if 0 not in row:
                    _A    		=  data_set.pop(__snake_case				)
                    break
          if not full_row:
               raise ValueError('solve_simultaneous() requires at least 1 full equation'				)
          data_set.insert(0   ,		__snake_case				)
     _A    		=  data_set.copy()
     _A    		=  simplify(__snake_case				)
     _A    		=  simplified[::-1]
     _A    		=  []
     for row in simplified:
          _A    		=  row[-1]
          if not solutions:
               if row[-2] == 0:
                    solutions.append(0				)
                    continue
               solutions.append(current_solution / row[-2]				)
               continue
          _A    		=  row.copy()[: len(__snake_case				) - 1 :]
          while temp_row[0] == 0:
               temp_row.pop(0				)
          if len(__snake_case				) == 0:
               solutions.append(0				)
               continue
          _A    		=  temp_row[1::]
          _A    		=  temp_row[::-1]
          for column_index, column in enumerate(__snake_case				):
               current_solution -= column * solutions[column_index]
          solutions.append(__snake_case				)
     _A    		=  []
     for item in solutions:
          final.append(float(round(__snake_case   ,		5				)				)				)
     return final[::-1]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _UpperCAmelCase    :     str         =       [
       [2, 1, 1, 1, 1, 4],
       [1, 2, 1, 1, 1, 5],
       [1, 1, 2, 1, 1, 6],
       [1, 1, 1, 2, 1, 7],
       [1, 1, 1, 1, 2, 8],
   ]
   print(solve_simultaneous(eq))
   print(solve_simultaneous([[4, 2]]))





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCamelCase				=			version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					tuple							,   __lowerCamelCase	:					Path							,   __lowerCamelCase	:					Tuple							,   __lowerCamelCase	:					Any							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					Optional[Any]=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   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					bool = False       ):
       snake_case			:  Optional[int]     							= torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              snake_case			:  str     							= "cuda"
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("`float16` model export is only supported on GPUs with CUDA"       )
       else:
              snake_case			:  Tuple     							= "cpu"
       snake_case			:  Union[str, Any]     							= Path(__lowerCamelCase       )

       # VAE DECODER
       snake_case			:  Any     							= AutoencoderKL.from_pretrained(model_path + "/vae"       )
       snake_case			:  Tuple     							= vae_decoder.config.latent_channels
       # forward only through the decoder part
       snake_case			:  Tuple     							= 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				=			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				=			parser.parse_args()
   print(args.output_path)
   convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
   print("""SD: Done: ONNX""")





