




'''simple docstring'''

import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import BeitImageProcessor

class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=7     ,	A_=3     ,	A_=18     ,	A_=30     ,	A_=400     ,	A_=True     ,	A_=None     ,	A_=True     ,	A_=None     ,	A_=True     ,	A_=[0.5, 0.5, 0.5]     ,	A_=[0.5, 0.5, 0.5]     ,	A_=False     ,	)     ->     int:
							lowerCAmelCase   =     size if size is not None else {'''height''': 20, '''width''': 20}
							lowerCAmelCase   =     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     batch_size
							lowerCAmelCase   =     num_channels
							lowerCAmelCase   =     image_size
							lowerCAmelCase   =     min_resolution
							lowerCAmelCase   =     max_resolution
							lowerCAmelCase   =     do_resize
							lowerCAmelCase   =     size
							lowerCAmelCase   =     do_center_crop
							lowerCAmelCase   =     crop_size
							lowerCAmelCase   =     do_normalize
							lowerCAmelCase   =     image_mean
							lowerCAmelCase   =     image_std
							lowerCAmelCase   =     do_reduce_labels




						def     __snake_case   ( self				)     ->     str:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_reduce_labels": self.do_reduce_labels,
							}







def 		_snake_case		( )      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     load_dataset("""hf-internal-testing/fixtures_ade20k"""							,		split="""test"""							)

	lowerCAmelCase   =     Image.open(dataset[0]["""file"""]							)
	lowerCAmelCase   =     Image.open(dataset[1]["""file"""]							)

	return image, map






def 		_snake_case		( )      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     load_dataset("""hf-internal-testing/fixtures_ade20k"""							,		split="""test"""							)

	lowerCAmelCase   =     Image.open(ds[0]["""file"""]							)
	lowerCAmelCase   =     Image.open(ds[1]["""file"""]							)
	lowerCAmelCase   =     Image.open(ds[2]["""file"""]							)
	lowerCAmelCase   =     Image.open(ds[3]["""file"""]							)

	return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class __snake_case(				_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   int									=		BeitImageProcessor if is_vision_available() else None


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     BeitImageProcessingTester(self				)


						@property
						def     __snake_case   ( self				)     ->     Any:
							return self.image_processor_tester.prepare_image_processor_dict()


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(A_     ,	"""do_resize"""				)				)
							self.assertTrue(hasattr(A_     ,	"""size"""				)				)
							self.assertTrue(hasattr(A_     ,	"""do_center_crop"""				)				)
							self.assertTrue(hasattr(A_     ,	"""center_crop"""				)				)
							self.assertTrue(hasattr(A_     ,	"""do_normalize"""				)				)
							self.assertTrue(hasattr(A_     ,	"""image_mean"""				)				)
							self.assertTrue(hasattr(A_     ,	"""image_std"""				)				)


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size     ,	{"""height""": 20, """width""": 20}				)
							self.assertEqual(image_processor.crop_size     ,	{"""height""": 18, """width""": 18}				)
							self.assertEqual(image_processor.do_reduce_labels     ,	A_				)

							lowerCAmelCase   =     self.image_processing_class.from_dict(
							    self.image_processor_dict     ,	size=42     ,	crop_size=84     ,	reduce_labels=A_				)
							self.assertEqual(image_processor.size     ,	{"""height""": 42, """width""": 42}				)
							self.assertEqual(image_processor.crop_size     ,	{"""height""": 84, """width""": 84}				)
							self.assertEqual(image_processor.do_reduce_labels     ,	A_				)


						def     __snake_case   ( self				)     ->     List[Any]:
							pass


						def     __snake_case   ( self				)     ->     List[str]:
							# Initialize image_processing
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							lowerCAmelCase   =     prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=A_				)
							for image in image_inputs:
								self.assertIsInstance(A_     ,	Image.Image				)

							# Test not batched input
							lowerCAmelCase   =     image_processing(image_inputs[0]     ,	return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape     ,	(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)

							# Test batched
							lowerCAmelCase   =     image_processing(A_     ,	return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape     ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)


						def     __snake_case   ( self				)     ->     Dict:
							# Initialize image_processing
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							lowerCAmelCase   =     prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=A_     ,	numpify=A_				)
							for image in image_inputs:
								self.assertIsInstance(A_     ,	np.ndarray				)

							# Test not batched input
							lowerCAmelCase   =     image_processing(image_inputs[0]     ,	return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape     ,	(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)

							# Test batched
							lowerCAmelCase   =     image_processing(A_     ,	return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape     ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)


						def     __snake_case   ( self				)     ->     Optional[Any]:
							# Initialize image_processing
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							lowerCAmelCase   =     prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=A_     ,	torchify=A_				)
							for image in image_inputs:
								self.assertIsInstance(A_     ,	torch.Tensor				)

							# Test not batched input
							lowerCAmelCase   =     image_processing(image_inputs[0]     ,	return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape     ,	(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)

							# Test batched
							lowerCAmelCase   =     image_processing(A_     ,	return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape     ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							# Initialize image_processing
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							lowerCAmelCase   =     prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=A_     ,	torchify=A_				)
							lowerCAmelCase   =     []
							for image in image_inputs:
								self.assertIsInstance(A_     ,	torch.Tensor				)
								maps.append(torch.zeros(image.shape[-2:]				).long()				)

							# Test not batched input
							lowerCAmelCase   =     image_processing(image_inputs[0]     ,	maps[0]     ,	return_tensors="""pt"""				)
							self.assertEqual(
							    encoding["""pixel_values"""].shape     ,	(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)
							self.assertEqual(
							    encoding["""labels"""].shape     ,	(
							        1,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)
							self.assertEqual(encoding["""labels"""].dtype     ,	torch.long				)
							self.assertTrue(encoding["""labels"""].min().item() >= 0				)
							self.assertTrue(encoding["""labels"""].max().item() <= 255				)

							# Test batched
							lowerCAmelCase   =     image_processing(A_     ,	A_     ,	return_tensors="""pt"""				)
							self.assertEqual(
							    encoding["""pixel_values"""].shape     ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)
							self.assertEqual(
							    encoding["""labels"""].shape     ,	(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)
							self.assertEqual(encoding["""labels"""].dtype     ,	torch.long				)
							self.assertTrue(encoding["""labels"""].min().item() >= 0				)
							self.assertTrue(encoding["""labels"""].max().item() <= 255				)

							# Test not batched input (PIL images)
							lowerCAmelCase   =     prepare_semantic_single_inputs()

							lowerCAmelCase   =     image_processing(A_     ,	A_     ,	return_tensors="""pt"""				)
							self.assertEqual(
							    encoding["""pixel_values"""].shape     ,	(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)
							self.assertEqual(
							    encoding["""labels"""].shape     ,	(
							        1,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)
							self.assertEqual(encoding["""labels"""].dtype     ,	torch.long				)
							self.assertTrue(encoding["""labels"""].min().item() >= 0				)
							self.assertTrue(encoding["""labels"""].max().item() <= 255				)

							# Test batched input (PIL images)
							lowerCAmelCase   =     prepare_semantic_batch_inputs()

							lowerCAmelCase   =     image_processing(A_     ,	A_     ,	return_tensors="""pt"""				)
							self.assertEqual(
							    encoding["""pixel_values"""].shape     ,	(
							        2,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)
							self.assertEqual(
							    encoding["""labels"""].shape     ,	(
							        2,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )     ,	)
							self.assertEqual(encoding["""labels"""].dtype     ,	torch.long				)
							self.assertTrue(encoding["""labels"""].min().item() >= 0				)
							self.assertTrue(encoding["""labels"""].max().item() <= 255				)




						def     __snake_case   ( self				)     ->     Union[str, Any]:
							# Initialize image_processing
							lowerCAmelCase   =     self.image_processing_class(**self.image_processor_dict				)

							# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
							lowerCAmelCase   =     prepare_semantic_single_inputs()
							lowerCAmelCase   =     image_processing(A_     ,	A_     ,	return_tensors="""pt"""				)
							self.assertTrue(encoding["""labels"""].min().item() >= 0				)
							self.assertTrue(encoding["""labels"""].max().item() <= 150				)

							lowerCAmelCase   =     True
							lowerCAmelCase   =     image_processing(A_     ,	A_     ,	return_tensors="""pt"""				)
							self.assertTrue(encoding["""labels"""].min().item() >= 0				)
							self.assertTrue(encoding["""labels"""].max().item() <= 255				)




'''simple docstring'''

import numpy

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_				)     ->     None:
							lowerCAmelCase   =     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.
							lowerCAmelCase   =     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.
							lowerCAmelCase   =     numpy.random.rand(
							    4     ,	3				)

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

							# Real output values provided.
							lowerCAmelCase   =     output_array

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


						def     __snake_case   ( self				)     ->     numpy.ndarray:
							lowerCAmelCase   =     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.
							lowerCAmelCase   =     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.
							lowerCAmelCase   =     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				)     ->     None:
							lowerCAmelCase   =     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				)     ,	)
							lowerCAmelCase   =     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				)     ,	)
							lowerCAmelCase   =     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     ,	A_     ,	A_     ,	A_				)     ->     None:
							for iteration in range(1     ,	iterations + 1				):
								lowerCAmelCase   =     self.feedforward()
								self.back_propagation()
								if give_loss:
									lowerCAmelCase   =     numpy.mean(numpy.square(output - self.feedforward()				)				)
									print(f'Iteration {iteration} Loss: {loss}'				)




						def     __snake_case   ( self     ,	A_				)     ->     int:
							lowerCAmelCase   =     input_arr

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

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

							lowerCAmelCase   =     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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						numpy.ndarray							)      ->						numpy.ndarray:







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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						numpy.ndarray							)      ->						numpy.ndarray:







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






def 		_snake_case		( )      ->						int:







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

	# Calling neural network class.
	lowerCAmelCase   =     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()