


import numpy


class 					lowerCAmelCase_ :




						def __init__(	self						:   Optional[int]   ,     __A						:   numpy.ndarray   ,     __A						:   numpy.ndarray						)      ->None:






									"""simple docstring"""






									a__						  =    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.
									a__						  =    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.
									a__						  =    numpy.random.rand(
									    4   ,     3						)

									# Random initial values for the second hidden layer.
									# Second hidden layer has 3 nodes.
									# Output layer has 1 node.
									a__						  =    numpy.random.rand(3   ,     1						)

									# Real output values provided.
									a__						  =    output_array

									# Predicted output values by the neural network.
									# Predicted_output array initially consists of zeroes.
									a__						  =    numpy.zeros(output_array.shape						)




						def  _snake_case					(	self						:   Dict						)      ->numpy.ndarray:






									"""simple docstring"""






									a__						  =    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.
									a__						  =    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.
									a__						  =    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  _snake_case					(	self						:   Optional[int]						)      ->None:






									"""simple docstring"""






									a__						  =    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						)   ,     )
									a__						  =    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						)   ,     )
									a__						  =    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  _snake_case					(	self						:   List[Any]   ,     __A						:   numpy.ndarray   ,     __A						:   int   ,     __A						:   bool						)      ->None:






									"""simple docstring"""






									for iteration in range(1   ,     iterations + 1						):
												a__						  =    self.feedforward()
												self.back_propagation()
												if give_loss:
															a__						  =    numpy.mean(numpy.square(output - self.feedforward()						)						)
															print(F'''Iteration {iteration} Loss: {loss}'''						)






						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   numpy.ndarray						)      ->int:






									"""simple docstring"""






									a__						  =    input_arr

									a__						  =    sigmoid(
									    numpy.dot(self.array   ,     self.input_layer_and_first_hidden_layer_weights						)						)

									a__						  =    sigmoid(
									    numpy.dot(
									        self.layer_between_input_and_first_hidden_layer   ,     self.first_hidden_layer_and_second_hidden_layer_weights   ,     )						)

									a__						  =    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 				lowerCamelCase__	(      a	:   numpy.ndarray				)    ->	numpy.ndarray:





			"""simple docstring"""
			return 1 / (1 + numpy.exp(-value				))


def 				lowerCamelCase__	(      a	:   numpy.ndarray				)    ->	numpy.ndarray:





			"""simple docstring"""
			return (value) * (1 - (value))


def 				lowerCamelCase__	(      )    ->	int:





			"""simple docstring"""
			a__						  =    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.
			a__						  =    numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])	,  dtype=numpy.floataa				)

			# Calling neural network class.
			a__						  =    TwoHiddenLayerNeuralNetwork(
			    input_array=a	,  output_array=a				)

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

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


if __name__ == "__main__":
		example()






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


snake_case__			 =						False


class 					lowerCAmelCase_ (       unittest.TestCase):
						pass






@nightly
@require_torch_gpu
class 					lowerCAmelCase_ (       unittest.TestCase):




						def  _snake_case					(	self						:   Tuple						)      ->Optional[int]:






									"""simple docstring"""






									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




						def  _snake_case					(	self						:   Dict						)      ->Any:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :List[Any]						  =    "A painting of a squirrel eating a burger "
									a__ :Optional[Any]						  =    torch.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=2   ,     output_type="numpy"						).images

									with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(__A						)
												a__ :List[Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(__A						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Optional[int]						  =    generator.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=2   ,     output_type="numpy"						).images

									assert np.abs(image - new_image						).sum() < 1E-5, "Models don't have the same forward pass"






						def  _snake_case					(	self						:   Optional[Any]						)      ->List[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(
									    "shi-labs/versatile-diffusion"   ,     torch_dtype=torch.floataa						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Tuple						  =    "A painting of a squirrel eating a burger "
									a__ :Tuple						  =    torch.manual_seed(0						)
									a__ :Optional[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=50   ,     output_type="numpy"						).images

									a__ :Tuple						  =    image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									a__ :Tuple						  =    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2


