


'''simple docstring'''

import numpy





class 						__SCREAMING_SNAKE_CASE					:






					def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
										_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  a_					(			self		)       ->    Dict:
										_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  a_					(			self		)       ->    str:
										_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  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    List[str]:
										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  a_					(			self     ,							__UpperCamelCase		)       ->    Optional[Any]:
										_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 __UpperCamelCase   (			__lowerCamelCase		: Union[str, Any]				)      ->     numpy.ndarray:







					'''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: Tuple				)      ->     numpy.ndarray:







					'''simple docstring'''






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






def __UpperCamelCase   (			)      ->     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=__SCREAMING_SNAKE_CASE      ,  output_array=__SCREAMING_SNAKE_CASE				)

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

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


if __name__ == "__main__":
			example()





'''simple docstring'''

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


lowercase__				     =		False





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):
     pass







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






     def  a_					(			self		)       ->    Dict:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def  a_					(			self		)       ->    List[str]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=2     ,							output_type="numpy"		).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(__UpperCamelCase		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		generator.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							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  a_					(			self		)       ->    List[Any]:
          _a						=		VersatileDiffusionTextToImagePipeline.from_pretrained(
              "shi-labs/versatile-diffusion"     ,							torch_dtype=torch.floataa		)
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a						=		"A painting of a squirrel eating a burger "
          _a						=		torch.manual_seed(0		)
          _a						=		pipe(
              prompt=__UpperCamelCase     ,							generator=__UpperCamelCase     ,							guidance_scale=7.5     ,							num_inference_steps=50     ,							output_type="numpy"		).images

          _a						=		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a						=		np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]		)

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

