

import unittest

import numpy as np

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 PoolFormerImageProcessor

class    _a (       unittest.TestCase							):




      """simple docstring"""

      def __init__(					self				:							Optional[Any]		,       UpperCAmelCase				:							Dict		,       UpperCAmelCase				:							Any=7		,       UpperCAmelCase				:							Dict=3		,       UpperCAmelCase				:							Tuple=30		,       UpperCAmelCase				:							Any=400		,       UpperCAmelCase				:							List[str]=True		,       UpperCAmelCase				:							Optional[int]=None		,       UpperCAmelCase				:							Optional[Any]=0.9		,       UpperCAmelCase				:							Any=None		,       UpperCAmelCase				:							Optional[Any]=True		,       UpperCAmelCase				:							Dict=[0.5, 0.5, 0.5]		,       UpperCAmelCase				:							Union[str, Any]=[0.5, 0.5, 0.5]		,       ):
            A_		   =							size if size is not None else {"shortest_edge": 30}
            A_		   =							crop_size if crop_size is not None else {"height": 30, "width": 30}
            A_		   =							parent
            A_		   =							batch_size
            A_		   =							num_channels
            A_		   =							min_resolution
            A_		   =							max_resolution
            A_		   =							do_resize_and_center_crop
            A_		   =							size
            A_		   =							crop_pct
            A_		   =							crop_size
            A_		   =							do_normalize
            A_		   =							image_mean
            A_		   =							image_std







      def 						__A     (					self				:							Optional[Any]     ):
            return {
                "size": self.size,
                "do_resize_and_center_crop": self.do_resize_and_center_crop,
                "crop_pct": self.crop_pct,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }




@require_torch
@require_vision
class    _a (       UpperCAmelCase__			,							unittest.TestCase							):




      """simple docstring"""

      _lowerCamelCase    :       int							=    PoolFormerImageProcessor if is_vision_available() else None





      def 						__A     (					self				:							Optional[int]     ):
            A_		   =							PoolFormerImageProcessingTester(self     )





      @property
      def 						__A     (					self				:							Optional[Any]     ):
            return self.image_processor_tester.prepare_image_processor_dict()





      def 						__A     (					self				:							int     ):
            A_		   =							self.image_processing_class(**self.image_processor_dict     )
            self.assertTrue(hasattr(__lowercase		,       "do_resize_and_center_crop"     )     )
            self.assertTrue(hasattr(__lowercase		,       "size"     )     )
            self.assertTrue(hasattr(__lowercase		,       "crop_pct"     )     )
            self.assertTrue(hasattr(__lowercase		,       "do_normalize"     )     )
            self.assertTrue(hasattr(__lowercase		,       "image_mean"     )     )
            self.assertTrue(hasattr(__lowercase		,       "image_std"     )     )





      def 						__A     (					self				:							Any     ):
            A_		   =							self.image_processing_class.from_dict(self.image_processor_dict     )
            self.assertEqual(image_processor.size		,       {"shortest_edge": 30}     )
            self.assertEqual(image_processor.crop_size		,       {"height": 30, "width": 30}     )

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





      def 						__A     (					self				:							Any     ):
            pass





      def 						__A     (					self				:							Union[str, Any]     ):
            A_		   =							self.image_processing_class(**self.image_processor_dict     )
            # create random PIL images
            A_		   =							prepare_image_inputs(self.image_processor_tester		,       equal_resolution=__lowercase     )
            for image in image_inputs:
                  self.assertIsInstance(__lowercase		,       Image.Image     )

            # Test not batched input
            A_		   =							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
            A_		   =							image_processing(__lowercase		,       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 						__A     (					self				:							Union[str, Any]     ):
            A_		   =							self.image_processing_class(**self.image_processor_dict     )
            # create random numpy tensors
            A_		   =							prepare_image_inputs(self.image_processor_tester		,       equal_resolution=__lowercase		,       numpify=__lowercase     )
            for image in image_inputs:
                  self.assertIsInstance(__lowercase		,       np.ndarray     )

            # Test not batched input
            A_		   =							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
            A_		   =							image_processing(__lowercase		,       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 						__A     (					self				:							List[str]     ):
            A_		   =							self.image_processing_class(**self.image_processor_dict     )
            # create random PyTorch tensors
            A_		   =							prepare_image_inputs(self.image_processor_tester		,       equal_resolution=__lowercase		,       torchify=__lowercase     )
            for image in image_inputs:
                  self.assertIsInstance(__lowercase		,       torch.Tensor     )

            # Test not batched input
            A_		   =							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
            A_		   =							image_processing(__lowercase		,       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"],
                )		,       )





import numpy
class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							snake_case_		    =     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_		    =     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_		    =     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_		    =     numpy.random.rand(3       ,	1						)

							# Real output values provided.
							snake_case_		    =     output_array

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






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     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_		    =     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_		    =     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							:     Any						):






							"""simple docstring"""






							snake_case_		    =     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_		    =     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_		    =     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							:     Optional[Any]       ,	__lowercase							:     numpy.ndarray       ,	__lowercase							:     int       ,	__lowercase							:     bool						):






							"""simple docstring"""






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






						def        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     numpy.ndarray						):






							"""simple docstring"""






							snake_case_		    =     input_arr

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

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

							snake_case_		    =     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							):







	'''simple docstring'''





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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     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_		    =     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,	dtype=numpy.floataa							)

	# Calling neural network class.
	snake_case_		    =     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()
