




"""simple docstring"""







import numpy

# List of input, output pairs
a  =				(
    ((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),
)
a  =				(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
a  =				[2, 4, 1, 5]
a  =				len(train_data)
a  =				0.009





def 				lowercase       (snake_case__ :			int   ,							snake_case__ :			Optional[int]="train"			)  ->	Any:


				'''simple docstring'''






				return calculate_hypothesis_value(snake_case__   ,							snake_case__			) - output(
				    snake_case__   ,							snake_case__			)





def 				lowercase       (snake_case__ :			Tuple			)  ->	str:


				'''simple docstring'''






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





def 				lowercase       (snake_case__ :			str   ,							snake_case__ :			Optional[Any]			)  ->	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 				lowercase       (snake_case__ :			Tuple   ,							snake_case__ :			Dict			)  ->	Dict:


				'''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 				lowercase       (snake_case__ :			Dict   ,							snake_case__ :			Optional[Any]=m			)  ->	Dict:


				'''simple docstring'''






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





def 				lowercase       (snake_case__ :			str			)  ->	Dict:


				'''simple docstring'''






				lowerCAmelCase						=					summation_of_cost_derivative(snake_case__   ,							snake_case__			) / m
				return cost_derivative_value





def 				lowercase       ()  ->	Optional[Any]:


				'''simple docstring'''






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





def 				lowercase       ()  ->	Union[str, Any]:


				'''simple docstring'''






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


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













import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCAmelCase__							  =						version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 		_a       ( a					:Union[str, Any]      ,				a					:tuple      ,				a					:Path      ,				a					:List[Any]      ,				a					:Union[str, Any]      ,				a					:Optional[int]      ,				a					:List[Any]      ,				a					:Union[str, Any]=False      ,				)				->     Any:
 output_path.parent.mkdir(parents=a      ,				exist_ok=a					)
 # 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(
      a      ,				a      ,				f=output_path.as_posix()      ,				input_names=a      ,				output_names=a      ,				dynamic_axes=a      ,				do_constant_folding=a      ,				use_external_data_format=a      ,				enable_onnx_checker=a      ,				opset_version=a      ,				)
 else:
  export(
      a      ,				a      ,				f=output_path.as_posix()      ,				input_names=a      ,				output_names=a      ,				dynamic_axes=a      ,				do_constant_folding=a      ,				opset_version=a      ,				)
@torch.no_grad()
def 		_a       ( a					:str      ,				a					:str      ,				a					:int      ,				a					:bool = False					)				->     Union[str, Any]:
 a						    = torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  a						    = '''cuda'''
 elif fpaa and not torch.cuda.is_available():
  raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''					)
 else:
  a						    = '''cpu'''
 a						    = Path(a					)

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


if __name__ == "__main__":
     UpperCAmelCase__							  =						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")

     UpperCAmelCase__							  =						parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")


