"""simple docstring"""






import numpy

# List of input, output pairs
__lowerCAmelCase    :       Dict					     =(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
__lowerCAmelCase    :       Optional[Any]					     =(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
__lowerCAmelCase    :       str					     =[2, 4, 1, 5]
__lowerCAmelCase    :       Tuple					     =len(train_data)
__lowerCAmelCase    :       str					     =0.009


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :List[Any]="train"		) ->    List[Any]:
      '''simple docstring'''


      return calculate_hypothesis_value(lowerCAmelCase__					,		lowerCAmelCase__		) - output(
          lowerCAmelCase__					,		lowerCAmelCase__		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any		) ->    Optional[Any]:
      '''simple docstring'''


      lowercase														=	0
      for i in range(len(lowerCAmelCase__		) - 1		):
            hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
      hyp_val += parameter_vector[0]
      return hyp_val


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Any		) ->    List[str]:
      '''simple docstring'''


      if data_set == "train":
            return train_data[example_no][1]
      elif data_set == "test":
            return test_data[example_no][1]
      return None


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :Union[str, Any]		) ->    List[Any]:
      '''simple docstring'''


      if data_set == "train":
            return _hypothesis_value(train_data[example_no][0]		)
      elif data_set == "test":
            return _hypothesis_value(test_data[example_no][0]		)
      return None


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :List[str]=m		) ->    Any:
      '''simple docstring'''


      lowercase														=	0
      for i in range(lowerCAmelCase__		):
            if index == -1:
                  summation_value += _error(lowerCAmelCase__		)
            else:
                  summation_value += _error(lowerCAmelCase__		) * train_data[i][0][index]
      return summation_value


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Union[str, Any]		) ->    Any:
      '''simple docstring'''


      lowercase														=	summation_of_cost_derivative(lowerCAmelCase__					,		lowerCAmelCase__		) / m
      return cost_derivative_value


def 			UpperCAmelCase__       (				) ->    Dict:
      '''simple docstring'''


      global parameter_vector
      # Tune these values to set a tolerance value for predicted output
      lowercase														=	0.000_002
      lowercase														=	0
      lowercase														=	0
      while True:
            j += 1
            lowercase														=	[0, 0, 0, 0]
            for i in range(0					,		len(lowerCAmelCase__		)		):
                  lowercase														=	get_cost_derivative(i - 1		)
                  lowercase														=	(
                      parameter_vector[i] - LEARNING_RATE * cost_derivative
                  )
            if numpy.allclose(
                lowerCAmelCase__					,		lowerCAmelCase__					,		atol=lowerCAmelCase__					,		rtol=lowerCAmelCase__					,		):
                  break
            lowercase														=	temp_parameter_vector
      print(("""Number of iterations:""", j)		)


def 			UpperCAmelCase__       (				) ->    Optional[Any]:
      '''simple docstring'''


      for i in range(len(lowerCAmelCase__		)		):
            print(("""Actual output value:""", output(lowerCAmelCase__					,		"""test"""		))		)
            print(("""Hypothesis output:""", calculate_hypothesis_value(lowerCAmelCase__					,		"""test"""		))		)


if __name__ == "__main__":
       run_gradient_descent()
       print("""\nTesting gradient descent for a linear hypothesis function.\n""")
       test_gradient_descent()




"""simple docstring"""






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCAmelCase    :       Dict					     =version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :tuple					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :List[str]=False					,		) ->    Union[str, Any]:
      '''simple docstring'''


      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		) ->    str:
      '''simple docstring'''


      lowercase														=	torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            lowercase														=	"""cuda"""
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
      else:
            lowercase														=	"""cpu"""
      lowercase														=	Path(lowerCAmelCase__		)

      # VAE DECODER
      lowercase														=	AutoencoderKL.from_pretrained(model_path + """/vae"""		)
      lowercase														=	vae_decoder.config.latent_channels
      # forward only through the decoder part
      lowercase														=	vae_decoder.decode
      onnx_export(
          lowerCAmelCase__					,		model_args=(
              torch.randn(1					,		lowerCAmelCase__					,		2_5					,		2_5		).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    :       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=1_4,
           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    :       Dict					     =parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("""SD: Done: ONNX""")



