




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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BlipImageProcessor







class       UpperCAmelCase(     unittest.TestCase     ):



	"""simple docstring"""






	def __init__(     self       ,      lowerCamelCase       ,      lowerCamelCase=7       ,      lowerCamelCase=3       ,      lowerCamelCase=18       ,      lowerCamelCase=30       ,      lowerCamelCase=400       ,      lowerCamelCase=True       ,      lowerCamelCase=None       ,      lowerCamelCase=True       ,      lowerCamelCase=False       ,      lowerCamelCase=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,      lowerCamelCase=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,      lowerCamelCase=True       ,      )					->			Optional[Any]:





								"""simple docstring"""


								lowercase__  :       Any   					=							size if size is not None else {"height": 20, "width": 20}
								lowercase__  :       int   					=							parent
								lowercase__  :       Union[str, Any]   					=							batch_size
								lowercase__  :       Any   					=							num_channels
								lowercase__  :       Dict   					=							image_size
								lowercase__  :       Union[str, Any]   					=							min_resolution
								lowercase__  :       List[str]   					=							max_resolution
								lowercase__  :       int   					=							do_resize
								lowercase__  :       Optional[int]   					=							size
								lowercase__  :       str   					=							do_normalize
								lowercase__  :       Tuple   					=							image_mean
								lowercase__  :       Tuple   					=							image_std
								lowercase__  :       List[Any]   					=							do_pad
								lowercase__  :       Union[str, Any]   					=							do_convert_rgb

	def     __a	(     self				)					->			List[Any]:





								"""simple docstring"""


								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								    "do_pad": self.do_pad,
								}







	def     __a	(     self       ,      lowerCamelCase=False       ,      lowerCamelCase=False       ,      lowerCamelCase=False				)					->			List[str]:





								"""simple docstring"""


								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
															lowercase__  :       int   					=							[]
															for i in range(self.batch_size				):
																						image_inputs.append(
																						    np.random.randint(
																						        255       ,      size=(self.num_channels, self.max_resolution, self.max_resolution)       ,      dtype=np.uinta				)				)
								else:
															lowercase__  :       int   					=							[]
															for i in range(self.batch_size				):
																						lowercase__					,   lowercase__  :       Union[str, Any]   					=							np.random.choice(np.arange(self.min_resolution       ,      self.max_resolution				)       ,      2				)
																						image_inputs.append(np.random.randint(255       ,      size=(self.num_channels, width, height)       ,      dtype=np.uinta				)				)

								if not numpify and not torchify:
															# PIL expects the channel dimension as last dimension
															lowercase__  :       int   					=							[Image.fromarray(np.moveaxis(lowerCamelCase__       ,      0       ,      -1				)				) for x in image_inputs]

								if torchify:
															lowercase__  :       Tuple   					=							[torch.from_numpy(lowerCamelCase__				) for x in image_inputs]

								return image_inputs







@require_torch
@require_vision
class       UpperCAmelCase(     A__    ,	unittest.TestCase     ):



	"""simple docstring"""






	a	:					Any         =     BlipImageProcessor if is_vision_available() else None

	def     __a	(     self				)					->			List[str]:





								"""simple docstring"""


								lowercase__  :       int   					=							BlipImageProcessingTester(self				)

	@property
	def     __a	(     self				)					->			Tuple:





								"""simple docstring"""


								return self.image_processor_tester.prepare_image_processor_dict()

	def     __a	(     self				)					->			Dict:





								"""simple docstring"""


								lowercase__  :       Union[str, Any]   					=							self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(lowerCamelCase__       ,      "do_resize"				)				)
								self.assertTrue(hasattr(lowerCamelCase__       ,      "size"				)				)
								self.assertTrue(hasattr(lowerCamelCase__       ,      "do_normalize"				)				)
								self.assertTrue(hasattr(lowerCamelCase__       ,      "image_mean"				)				)
								self.assertTrue(hasattr(lowerCamelCase__       ,      "image_std"				)				)
								self.assertTrue(hasattr(lowerCamelCase__       ,      "do_convert_rgb"				)				)

	def     __a	(     self				)					->			List[Any]:





								"""simple docstring"""


								pass

	def     __a	(     self				)					->			int:





								"""simple docstring"""


								lowercase__  :       List[str]   					=							self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								lowercase__  :       Union[str, Any]   					=							self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__				)
								for image in image_inputs:
															self.assertIsInstance(lowerCamelCase__       ,      Image.Image				)

								# Test not batched input
								lowercase__  :       List[str]   					=							image_processor(image_inputs[0]       ,      return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,      (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )

								# Test batched
								lowercase__  :       List[str]   					=							image_processor(lowerCamelCase__       ,      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.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )

	def     __a	(     self				)					->			Tuple:





								"""simple docstring"""


								lowercase__  :       int   					=							self.image_processing_class(**self.image_processor_dict				)
								# create random numpy tensors
								lowercase__  :       Optional[int]   					=							self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__       ,      numpify=lowerCamelCase__				)
								for image in image_inputs:
															self.assertIsInstance(lowerCamelCase__       ,      np.ndarray				)

								# Test not batched input
								lowercase__  :       Union[str, Any]   					=							image_processor(image_inputs[0]       ,      return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,      (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )

								# Test batched
								lowercase__  :       List[Any]   					=							image_processor(lowerCamelCase__       ,      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.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )







	def     __a	(     self				)					->			int:





								"""simple docstring"""


								lowercase__  :       Union[str, Any]   					=							self.image_processing_class(**self.image_processor_dict				)
								# create random PyTorch tensors
								lowercase__  :       Optional[Any]   					=							self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__       ,      torchify=lowerCamelCase__				)
								for image in image_inputs:
															self.assertIsInstance(lowerCamelCase__       ,      torch.Tensor				)

								# Test not batched input
								lowercase__  :       int   					=							image_processor(image_inputs[0]       ,      return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,      (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )

								# Test batched
								lowercase__  :       Tuple   					=							image_processor(lowerCamelCase__       ,      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.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )







@require_torch
@require_vision
class       UpperCAmelCase(     A__    ,	unittest.TestCase     ):



	"""simple docstring"""






	a	:					Tuple         =     BlipImageProcessor if is_vision_available() else None

	def     __a	(     self				)					->			int:





								"""simple docstring"""


								lowercase__  :       Optional[int]   					=							BlipImageProcessingTester(self       ,      num_channels=4				)
								lowercase__  :       Union[str, Any]   					=							3

	@property
	def     __a	(     self				)					->			Optional[Any]:





								"""simple docstring"""


								return self.image_processor_tester.prepare_image_processor_dict()

	def     __a	(     self				)					->			Any:





								"""simple docstring"""


								lowercase__  :       Optional[Any]   					=							self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(lowerCamelCase__       ,      "do_resize"				)				)
								self.assertTrue(hasattr(lowerCamelCase__       ,      "size"				)				)
								self.assertTrue(hasattr(lowerCamelCase__       ,      "do_normalize"				)				)
								self.assertTrue(hasattr(lowerCamelCase__       ,      "image_mean"				)				)
								self.assertTrue(hasattr(lowerCamelCase__       ,      "image_std"				)				)
								self.assertTrue(hasattr(lowerCamelCase__       ,      "do_convert_rgb"				)				)

	def     __a	(     self				)					->			List[Any]:





								"""simple docstring"""


								pass







	def     __a	(     self				)					->			Optional[Any]:





								"""simple docstring"""


								lowercase__  :       str   					=							self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								lowercase__  :       Optional[int]   					=							self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__				)
								for image in image_inputs:
															self.assertIsInstance(lowerCamelCase__       ,      Image.Image				)

								# Test not batched input
								lowercase__  :       List[str]   					=							image_processor(image_inputs[0]       ,      return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,      (
								        1,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )

								# Test batched
								lowercase__  :       Any   					=							image_processor(lowerCamelCase__       ,      return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape       ,      (
								        self.image_processor_tester.batch_size,
								        self.expected_encoded_image_num_channels,
								        self.image_processor_tester.size["height"],
								        self.image_processor_tester.size["width"],
								    )       ,      )




"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt



def 				lowerCAmelCase_(      lowercase_   :							List[str]							)		->				List[Any]:
				return 1 / (1 + np.exp(-1 * x							))


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=False      ):
									_lowerCamelCase					     = units
									_lowerCamelCase					     = None
									_lowerCamelCase					     = None
									_lowerCamelCase					     = activation
									if learning_rate is None:
													_lowerCamelCase					     = 0.3
									_lowerCamelCase					     = learning_rate
									_lowerCamelCase					     = is_input_layer

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = np.asmatrix(np.random.normal(0 ,				0.5 ,				(self.units, back_units)      )      )
									_lowerCamelCase					     = np.asmatrix(np.random.normal(0 ,				0.5 ,				self.units      )      ).T
									if self.activation is None:
													_lowerCamelCase					     = sigmoid

					def 	snake_case__					(			self      ):
									# activation function may be sigmoid or linear
									if self.activation == sigmoid:
													_lowerCamelCase					     = np.dot(self.output ,				(1 - self.output).T      )
													_lowerCamelCase					     = np.diag(np.diag(lowerCamelCase__      )      )
									else:
													_lowerCamelCase					     = 1
									return gradient_activation

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = xdata
									if self.is_input_layer:
													# input layer
													_lowerCamelCase					     = xdata
													_lowerCamelCase					     = xdata
													return xdata
									else:
													_lowerCamelCase					     = np.dot(self.weight ,				self.xdata      ) - self.bias
													_lowerCamelCase					     = self.activation(self.wx_plus_b      )
													return self.output





					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.cal_gradient()  # i * i 维
									_lowerCamelCase					     = np.asmatrix(np.dot(gradient.T ,				lowerCamelCase__      )      )

									_lowerCamelCase					     = np.asmatrix(self.xdata      )
									_lowerCamelCase					     = -1
									_lowerCamelCase					     = self.weight

									_lowerCamelCase					     = np.dot(gradient.T ,				self._gradient_weight.T      )
									_lowerCamelCase					     = gradient * self._gradient_bias
									_lowerCamelCase					     = np.dot(lowerCamelCase__ ,				self._gradient_x      ).T
									# upgrade: the Negative gradient direction
									_lowerCamelCase					     = self.weight - self.learn_rate * self.gradient_weight
									_lowerCamelCase					     = self.bias - self.learn_rate * self.gradient_bias.T
									# updates the weights and bias according to learning rate (0.3 if undefined)
									return self.gradient


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self      ):
									_lowerCamelCase					     = []
									_lowerCamelCase					     = []
									_lowerCamelCase					     = plt.figure()
									_lowerCamelCase					     = self.fig_loss.add_subplot(1 ,				1 ,				1      )

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									self.layers.append(lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									for i, layer in enumerate(self.layers[:]      ):
													if i < 1:
																	_lowerCamelCase					     = True
													else:
																	layer.initializer(self.layers[i - 1].units      )

					def 	snake_case__					(			self      ):
									for i, layer in enumerate(self.layers[:]      ):
													print(F"""------- layer {i} -------"""      )
													print('''weight.shape ''' ,				np.shape(layer.weight      )      )
													print('''bias.shape ''' ,				np.shape(layer.bias      )      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = train_round
									_lowerCamelCase					     = accuracy

									self.ax_loss.hlines(self.accuracy ,				0 ,				self.train_round * 1.1      )

									_lowerCamelCase					     = np.shape(lowerCamelCase__      )
									for _ in range(lowerCamelCase__      ):
													_lowerCamelCase					     = 0
													for row in range(x_shape[0]      ):
																	_lowerCamelCase					     = np.asmatrix(xdata[row, :]      ).T
																	_lowerCamelCase					     = np.asmatrix(ydata[row, :]      ).T

																	# forward propagation
																	for layer in self.layers:
																					_lowerCamelCase					     = layer.forward_propagation(_xdata      )

																	_lowerCamelCase     ,   _lowerCamelCase					     = self.cal_loss(_ydata ,				_xdata      )
																	_lowerCamelCase					     = all_loss + loss

																	# back propagation: the input_layer does not upgrade
																	for layer in self.layers[:0:-1]:
																					_lowerCamelCase					     = layer.back_propagation(lowerCamelCase__      )

													_lowerCamelCase					     = all_loss / x_shape[0]
													self.train_mse.append(lowerCamelCase__      )

													self.plot_loss()

													if mse < self.accuracy:
																	print('''----达到精度----'''      )
																	return mse
									return None

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = np.sum(np.power((ydata - ydata_) ,				2      )      )
									_lowerCamelCase					     = 2 * (ydata_ - ydata)
									# vector (shape is the same as _ydata.shape)
									return self.loss, self.loss_gradient





					def 	snake_case__					(			self      ):
									if self.ax_loss.lines:
													self.ax_loss.lines.remove(self.ax_loss.lines[0]      )
									self.ax_loss.plot(self.train_mse ,				'''r-'''      )
									plt.ion()
									plt.xlabel('''step'''      )
									plt.ylabel('''loss'''      )
									plt.show()
									plt.pause(0.1      )



def 				lowerCAmelCase_(      )		->				Optional[Any]:
				_lowerCamelCase					     = np.random.randn(10  ,      10							)
				_lowerCamelCase					     = np.asarray(
				    [
				        [0.8, 0.4],
				        [0.4, 0.3],
				        [0.3_4, 0.4_5],
				        [0.6_7, 0.3_2],
				        [0.8_8, 0.6_7],
				        [0.7_8, 0.7_7],
				        [0.5_5, 0.6_6],
				        [0.5_5, 0.4_3],
				        [0.5_4, 0.1],
				        [0.1, 0.5],
				    ]							)
				_lowerCamelCase					     = BPNN()
				for i in (10, 20, 30, 2):
								model.add_layer(DenseLayer(lowercase_							)							)
				model.build()
				model.summary()
				model.train(xdata=lowercase_  ,      ydata=lowercase_  ,      train_round=1_00  ,      accuracy=0.0_1							)


if __name__ == "__main__":
			example()
