


"""simple docstring"""
import numpy



class 			_UpperCAmelCase :






		'''simple docstring'''

		def __init__(self				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Any						=				input_array

			# Random initial weights are assigned where first argument is the
			# number of nodes in previous layer and second argument is the
			# number of nodes in the next layer.

			# Random initial weights are assigned.
			# self.input_array.shape[1] is used to represent number of nodes in input layer.
			# First hidden layer consists of 4 nodes.
			__snake_case	:						List[Any]						=				numpy.random.rand(
			    self.input_array.shape[1]				,       4					)

			# Random initial values for the first hidden layer.
			# First hidden layer has 4 nodes.
			# Second hidden layer has 3 nodes.
			__snake_case	:						Optional[Any]						=				numpy.random.rand(
			    4				,       3					)

			# Random initial values for the second hidden layer.
			# Second hidden layer has 3 nodes.
			# Output layer has 1 node.
			__snake_case	:						List[Any]						=				numpy.random.rand(3				,       1					)

			# Real output values provided.
			__snake_case	:						List[str]						=				output_array

			# Predicted output values by the neural network.
			# Predicted_output array initially consists of zeroes.
			__snake_case	:						List[str]						=				numpy.zeros(output_array.shape					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				sigmoid(
			    numpy.dot(self.input_array				,       self.input_layer_and_first_hidden_layer_weights					)					)

			# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
			# connecting the first hidden set of nodes with the second hidden set of nodes.
			__snake_case	:						Dict						=				sigmoid(
			    numpy.dot(
			        self.layer_between_input_and_first_hidden_layer				,       self.first_hidden_layer_and_second_hidden_layer_weights				,       )					)

			# layer_between_second_hidden_layer_and_output is the layer connecting
			# second hidden layer with the output node.
			__snake_case	:						Optional[int]						=				sigmoid(
			    numpy.dot(
			        self.layer_between_first_hidden_layer_and_second_hidden_layer				,       self.second_hidden_layer_and_output_layer_weights				,       )					)

			return self.layer_between_second_hidden_layer_and_output



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				numpy.dot(
			    self.layer_between_first_hidden_layer_and_second_hidden_layer.T				,       2
			    * (self.output_array - self.predicted_output)
			    * sigmoid_derivative(self.predicted_output					)				,       )
			__snake_case	:						Tuple						=				numpy.dot(
			    self.layer_between_input_and_first_hidden_layer.T				,       numpy.dot(
			        2
			        * (self.output_array - self.predicted_output)
			        * sigmoid_derivative(self.predicted_output					)				,       self.second_hidden_layer_and_output_layer_weights.T				,       )
			    * sigmoid_derivative(
			        self.layer_between_first_hidden_layer_and_second_hidden_layer					)				,       )
			__snake_case	:						List[str]						=				numpy.dot(
			    self.input_array.T				,       numpy.dot(
			        numpy.dot(
			            2
			            * (self.output_array - self.predicted_output)
			            * sigmoid_derivative(self.predicted_output					)				,       self.second_hidden_layer_and_output_layer_weights.T				,       )
			        * sigmoid_derivative(
			            self.layer_between_first_hidden_layer_and_second_hidden_layer					)				,       self.first_hidden_layer_and_second_hidden_layer_weights.T				,       )
			    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer					)				,       )

			self.input_layer_and_first_hidden_layer_weights += (
			    updated_input_layer_and_first_hidden_layer_weights
			)
			self.first_hidden_layer_and_second_hidden_layer_weights += (
			    updated_first_hidden_layer_and_second_hidden_layer_weights
			)
			self.second_hidden_layer_and_output_layer_weights += (
			    updated_second_hidden_layer_and_output_layer_weights
			)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			for iteration in range(1				,       iterations + 1					):
				__snake_case	:						Union[str, Any]						=				self.feedforward()
				self.back_propagation()
				if give_loss:
					__snake_case	:						str						=				numpy.mean(numpy.square(output - self.feedforward()					)					)
					print(f"""Iteration {iteration} Loss: {loss}"""					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				input_arr

			__snake_case	:						Dict						=				sigmoid(
			    numpy.dot(self.array				,       self.input_layer_and_first_hidden_layer_weights					)					)

			__snake_case	:						Optional[int]						=				sigmoid(
			    numpy.dot(
			        self.layer_between_input_and_first_hidden_layer				,       self.first_hidden_layer_and_second_hidden_layer_weights				,       )					)

			__snake_case	:						int						=				sigmoid(
			    numpy.dot(
			        self.layer_between_first_hidden_layer_and_second_hidden_layer				,       self.second_hidden_layer_and_output_layer_weights				,       )					)

			return int(self.layer_between_second_hidden_layer_and_output > 0.6					)






def 		lowercase							(     _snake_case						:      str     )     ->numpy.ndarray:
	"""simple docstring"""





	return 1 / (1 + numpy.exp(-value     ))






def 		lowercase							(     _snake_case						:      Union[str, Any]     )     ->numpy.ndarray:
	"""simple docstring"""





	return (value) * (1 - (value))






def 		lowercase							(     )     ->int:
	"""simple docstring"""





	__snake_case	:						Union[str, Any]						=				numpy.array(
	    (
	        [0, 0, 0],
	        [0, 0, 1],
	        [0, 1, 0],
	        [0, 1, 1],
	        [1, 0, 0],
	        [1, 0, 1],
	        [1, 1, 0],
	        [1, 1, 1],
	    )     ,   dtype=numpy.floataa     ,   )

	# True output values for the given input values.
	__snake_case	:						List[Any]						=				numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,   dtype=numpy.floataa     )

	# Calling neural network class.
	__snake_case	:						Dict						=				TwoHiddenLayerNeuralNetwork(
	    input_array=UpperCamelCase_     ,   output_array=UpperCamelCase_     )

	# Calling training function.
	# Set give_loss to True if you want to see loss in every iteration.
	neural_network.train(output=UpperCamelCase_     ,   iterations=10     ,   give_loss=UpperCamelCase_     )

	return neural_network.predict(numpy.array(([1, 1, 1])     ,   dtype=numpy.floataa     )     )


if __name__ == "__main__":
			example()







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
				__snake_case								=  argparse.ArgumentParser()

				parser.add_argument(
				    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
				)
				parser.add_argument(
				    """--original_config_file""",
				    type=str,
				    required=True,
				    help="""The YAML config file corresponding to the original architecture.""",
				)
				parser.add_argument(
				    """--num_in_channels""",
				    default=None,
				    type=int,
				    help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
				)
				parser.add_argument(
				    """--image_size""",
				    default=5_12,
				    type=int,
				    help=(
				        """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
				        """ Base. Use 768 for Stable Diffusion v2."""
				    ),
				)
				parser.add_argument(
				    """--extract_ema""",
				    action="""store_true""",
				    help=(
				        """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
				        """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
				        """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
				    ),
				)
				parser.add_argument(
				    """--upcast_attention""",
				    action="""store_true""",
				    help=(
				        """Whether the attention computation should always be upcasted. This is necessary when running stable"""
				        """ diffusion 2.1."""
				    ),
				)
				parser.add_argument(
				    """--from_safetensors""",
				    action="""store_true""",
				    help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
				)
				parser.add_argument(
				    """--to_safetensors""",
				    action="""store_true""",
				    help="""Whether to store pipeline in safetensors format or not.""",
				)
				parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
				parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")






				def 							_lowercase							(     UpperCamelCase_		)		->		Dict:
									'''simple docstring'''


									if string == "True":
														return True
									elif string == "False":
														return False
									else:
														raise ValueError(F'could not parse string as bool {string}'		)

				parser.add_argument(
				    """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
				)

				parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

				__snake_case								=  parser.parse_args()

				__snake_case								=  download_controlnet_from_original_ckpt(
				    checkpoint_path=args.checkpoint_path,
				    original_config_file=args.original_config_file,
				    image_size=args.image_size,
				    extract_ema=args.extract_ema,
				    num_in_channels=args.num_in_channels,
				    upcast_attention=args.upcast_attention,
				    from_safetensors=args.from_safetensors,
				    device=args.device,
				    use_linear_projection=args.use_linear_projection,
				    cross_attention_dim=args.cross_attention_dim,
				)



				controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





