

import pickle

import numpy as np
from matplotlib import pyplot as plt





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A=0.2					,						_A=0.2							):
												__lowerCAmelCase					=							bp_numa
												__lowerCAmelCase					=							bp_numa
												__lowerCAmelCase					=							bp_numa
												__lowerCAmelCase					=							conva_get[:2]
												__lowerCAmelCase					=							conva_get[2]
												__lowerCAmelCase					=							size_pa
												__lowerCAmelCase					=							rate_w
												__lowerCAmelCase					=							rate_t
												__lowerCAmelCase					=							[
												    np.mat(-1 * np.random.rand(self.conva[0]					,						self.conva[0]							) + 0.5							)
												    for i in range(self.conva[1]							)
												]
												__lowerCAmelCase					=							np.mat(-1 * np.random.rand(self.num_bpa					,						self.num_bpa							) + 0.5							)
												__lowerCAmelCase					=							np.mat(-1 * np.random.rand(self.num_bpa					,						self.num_bpa							) + 0.5							)
												__lowerCAmelCase					=							-2 * np.random.rand(self.conva[1]							) + 1
												__lowerCAmelCase					=							-2 * np.random.rand(self.num_bpa							) + 1
												__lowerCAmelCase					=							-2 * np.random.rand(self.num_bpa							) + 1




							def        A__  ( self					,						_A							):
												# save model dict with pickle
												__lowerCAmelCase					=							{
												    "num_bp1": self.num_bpa,
												    "num_bp2": self.num_bpa,
												    "num_bp3": self.num_bpa,
												    "conv1": self.conva,
												    "step_conv1": self.step_conva,
												    "size_pooling1": self.size_poolinga,
												    "rate_weight": self.rate_weight,
												    "rate_thre": self.rate_thre,
												    "w_conv1": self.w_conva,
												    "wkj": self.wkj,
												    "vji": self.vji,
												    "thre_conv1": self.thre_conva,
												    "thre_bp2": self.thre_bpa,
												    "thre_bp3": self.thre_bpa,
												}
												with open(snake_case_					,						'wb'							) as f:
																	pickle.dump(snake_case_					,						snake_case_							)

												print(F"""Model saved： {save_path}"""							)




							@classmethod
							def        A__  ( cls					,						_A							):
												# read saved model
												with open(snake_case_					,						'rb'							) as f:
																	__lowerCAmelCase					=							pickle.load(snake_case_							)  # noqa: S301

												__lowerCAmelCase					=							model_dic.get('conv1'							)
												conv_get.append(model_dic.get('step_conv1'							)							)
												__lowerCAmelCase					=							model_dic.get('size_pooling1'							)
												__lowerCAmelCase					=							model_dic.get('num_bp1'							)
												__lowerCAmelCase					=							model_dic.get('num_bp2'							)
												__lowerCAmelCase					=							model_dic.get('num_bp3'							)
												__lowerCAmelCase					=							model_dic.get('rate_weight'							)
												__lowerCAmelCase					=							model_dic.get('rate_thre'							)
												# create model instance
												__lowerCAmelCase					=							CNN(snake_case_					,						snake_case_					,						snake_case_					,						snake_case_					,						snake_case_					,						snake_case_					,						snake_case_							)
												# modify model parameter
												__lowerCAmelCase					=							model_dic.get('w_conv1'							)
												__lowerCAmelCase					=							model_dic.get('wkj'							)
												__lowerCAmelCase					=							model_dic.get('vji'							)
												__lowerCAmelCase					=							model_dic.get('thre_conv1'							)
												__lowerCAmelCase					=							model_dic.get('thre_bp2'							)
												__lowerCAmelCase					=							model_dic.get('thre_bp3'							)
												return conv_ins




							def        A__  ( self					,						_A							):
												return 1 / (1 + np.exp(-1 * x							))




							def        A__  ( self					,						_A							):
												return round(snake_case_					,						3							)




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A							):
												# convolution process
												__lowerCAmelCase					=							convs[0]
												__lowerCAmelCase					=							convs[1]
												__lowerCAmelCase					=							np.shape(snake_case_							)[0]
												# get the data slice of original image data, data_focus
												__lowerCAmelCase					=							[]
												for i_focus in range(0					,						size_data - size_conv + 1					,						snake_case_							):
																	for j_focus in range(0					,						size_data - size_conv + 1					,						snake_case_							):
																						__lowerCAmelCase					=							data[
																						    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
																						]
																						data_focus.append(snake_case_							)
																	# calculate the feature map of every single kernel, and saved as list of matrix
												__lowerCAmelCase					=							[]
												__lowerCAmelCase					=							int((size_data - size_conv) / conv_step + 1							)
												for i_map in range(snake_case_							):
																	__lowerCAmelCase					=							[]
																	for i_focus in range(len(snake_case_							)							):
																						__lowerCAmelCase					=							(
																						    np.sum(np.multiply(data_focus[i_focus]					,						w_convs[i_map]							)							)
																						    - thre_convs[i_map]
																						)
																						featuremap.append(self.sig(snake_case_							)							)
																	__lowerCAmelCase					=							np.asmatrix(snake_case_							).reshape(
																	    snake_case_					,						snake_case_							)
																	data_featuremap.append(snake_case_							)

												# expanding the data slice to One dimenssion
												__lowerCAmelCase					=							[]
												for each_focus in data_focus:
																	focusa_list.extend(self.Expand_Mat(snake_case_							)							)
												__lowerCAmelCase					=							np.asarray(snake_case_							)
												return focus_list, data_featuremap




							def        A__  ( self					,						_A					,						_A					,						_A="average_pool"							):
												# pooling process
												__lowerCAmelCase					=							len(featuremaps[0]							)
												__lowerCAmelCase					=							int(size_map / size_pooling							)
												__lowerCAmelCase					=							[]
												for i_map in range(len(snake_case_							)							):
																	__lowerCAmelCase					=							featuremaps[i_map]
																	__lowerCAmelCase					=							[]
																	for i_focus in range(0					,						snake_case_					,						snake_case_							):
																						for j_focus in range(0					,						snake_case_					,						snake_case_							):
																											__lowerCAmelCase					=							feature_map[
																											    i_focus : i_focus + size_pooling,
																											    j_focus : j_focus + size_pooling,
																											]
																											if pooling_type == "average_pool":
																																# average pooling
																																map_pooled.append(np.average(snake_case_							)							)
																											elif pooling_type == "max_pooling":
																																# max pooling
																																map_pooled.append(np.max(snake_case_							)							)
																	__lowerCAmelCase					=							np.asmatrix(snake_case_							).reshape(snake_case_					,						snake_case_							)
																	featuremap_pooled.append(snake_case_							)
												return featuremap_pooled




							def        A__  ( self					,						_A							):
												# expanding three dimension data to one dimension list
												__lowerCAmelCase					=							[]
												for i in range(len(snake_case_							)							):
																	__lowerCAmelCase					=							np.shape(data[i]							)
																	__lowerCAmelCase					=							data[i].reshape(1					,						shapes[0] * shapes[1]							)
																	__lowerCAmelCase					=							data_listed.getA().tolist()[0]
																	data_expanded.extend(snake_case_							)
												__lowerCAmelCase					=							np.asarray(snake_case_							)
												return data_expanded




							def        A__  ( self					,						_A							):
												# expanding matrix to one dimension list
												__lowerCAmelCase					=							np.asarray(snake_case_							)
												__lowerCAmelCase					=							np.shape(snake_case_							)
												__lowerCAmelCase					=							data_mat.reshape(1					,						shapes[0] * shapes[1]							)
												return data_expanded




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							[]
												__lowerCAmelCase					=							0
												for i_map in range(snake_case_							):
																	__lowerCAmelCase					=							np.ones((size_map, size_map)							)
																	for i in range(0					,						snake_case_					,						snake_case_							):
																						for j in range(0					,						snake_case_					,						snake_case_							):
																											__lowerCAmelCase					=							pd_pool[
																											    i_pool
																											]
																											__lowerCAmelCase					=							i_pool + 1
																	__lowerCAmelCase					=							np.multiply(
																	    snake_case_					,						np.multiply(out_map[i_map]					,						(1 - out_map[i_map])							)							)
																	pd_all.append(snake_case_							)
												return pd_all




							def        A__  ( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A=bool							):
												# model traning
												print('----------------------Start Training-------------------------'							)
												print((' - - Shape: Train_Data  ', np.shape(snake_case_							))							)
												print((' - - Shape: Teach_Data  ', np.shape(snake_case_							))							)
												__lowerCAmelCase					=							0
												__lowerCAmelCase					=							[]
												__lowerCAmelCase					=							1_0_0_0_0
												while rp < n_repeat and mse >= error_accuracy:
																	__lowerCAmelCase					=							0
																	print(F"""-------------Learning Time {rp}--------------"""							)
																	for p in range(len(snake_case_							)							):
																						# print('------------Learning Image: %d--------------'%p)
																						__lowerCAmelCase					=							np.asmatrix(datas_train[p]							)
																						__lowerCAmelCase					=							np.asarray(datas_teach[p]							)
																						__lowerCAmelCase					=							self.convolute(
																						    snake_case_					,						self.conva					,						self.w_conva					,						self.thre_conva					,						conv_step=self.step_conva					,						)
																						__lowerCAmelCase					=							self.pooling(snake_case_					,						self.size_poolinga							)
																						__lowerCAmelCase					=							np.shape(snake_case_							)
																						__lowerCAmelCase					=							self._expand(snake_case_							)
																						__lowerCAmelCase					=							data_bp_input

																						__lowerCAmelCase					=							np.dot(snake_case_					,						self.vji.T							) - self.thre_bpa
																						__lowerCAmelCase					=							self.sig(snake_case_							)
																						__lowerCAmelCase					=							np.dot(snake_case_					,						self.wkj.T							) - self.thre_bpa
																						__lowerCAmelCase					=							self.sig(snake_case_							)

																						# --------------Model Leaning ------------------------
																						# calculate error and gradient---------------
																						__lowerCAmelCase					=							np.multiply(
																						    (data_teach - bp_outa)					,						np.multiply(snake_case_					,						(1 - bp_outa)							)							)
																						__lowerCAmelCase					=							np.multiply(
																						    np.dot(snake_case_					,						self.wkj							)					,						np.multiply(snake_case_					,						(1 - bp_outa)							)							)
																						__lowerCAmelCase					=							np.dot(snake_case_					,						self.vji							)

																						__lowerCAmelCase					=							pd_i_all / (self.size_poolinga * self.size_poolinga)
																						__lowerCAmelCase					=							pd_conva_pooled.T.getA().tolist()
																						__lowerCAmelCase					=							self._calculate_gradient_from_pool(
																						    snake_case_					,						snake_case_					,						shape_featuremapa[0]					,						shape_featuremapa[1]					,						self.size_poolinga					,						)
																						# weight and threshold learning process---------
																						# convolution layer
																						for k_conv in range(self.conva[1]							):
																											__lowerCAmelCase					=							self._expand_mat(pd_conva_all[k_conv]							)
																											__lowerCAmelCase					=							self.rate_weight * np.dot(snake_case_					,						snake_case_							)

																											__lowerCAmelCase					=							self.w_conva[k_conv] + delta_w.reshape(
																											    (self.conva[0], self.conva[0])							)

																											__lowerCAmelCase					=							(
																											    self.thre_conva[k_conv]
																											    - np.sum(pd_conva_all[k_conv]							) * self.rate_thre
																											)
																						# all connected layer
																						__lowerCAmelCase					=							self.wkj + pd_k_all.T * bp_outa * self.rate_weight
																						__lowerCAmelCase					=							self.vji + pd_j_all.T * bp_outa * self.rate_weight
																						__lowerCAmelCase					=							self.thre_bpa - pd_k_all * self.rate_thre
																						__lowerCAmelCase					=							self.thre_bpa - pd_j_all * self.rate_thre
																						# calculate the sum error of all single image
																						__lowerCAmelCase					=							np.sum(abs(data_teach - bp_outa							)							)
																						error_count += errors
																						# print('   ----Teach      ',data_teach)
																						# print('   ----BP_output  ',bp_out3)
																	__lowerCAmelCase					=							rp + 1
																	__lowerCAmelCase					=							error_count / patterns
																	all_mse.append(snake_case_							)

												def draw_error():
																	__lowerCAmelCase					=							[error_accuracy for i in range(int(n_repeat * 1.2							)							)]
																	plt.plot(snake_case_					,						'+-'							)
																	plt.plot(snake_case_					,						'r--'							)
																	plt.xlabel('Learning Times'							)
																	plt.ylabel('All_mse'							)
																	plt.grid(snake_case_					,						alpha=0.5							)
																	plt.show()

												print('------------------Training Complished---------------------'							)
												print((' - - Training epoch: ', rp, F"""     - - Mse: {mse:.6f}""")							)
												if draw_e:
																	draw_error()
												return mse




							def        A__  ( self					,						_A							):
												# model predict
												__lowerCAmelCase					=							[]
												print('-------------------Start Testing-------------------------'							)
												print((' - - Shape: Test_Data  ', np.shape(snake_case_							))							)
												for p in range(len(snake_case_							)							):
																	__lowerCAmelCase					=							np.asmatrix(datas_test[p]							)
																	__lowerCAmelCase					=							self.convolute(
																	    snake_case_					,						self.conva					,						self.w_conva					,						self.thre_conva					,						conv_step=self.step_conva					,						)
																	__lowerCAmelCase					=							self.pooling(snake_case_					,						self.size_poolinga							)
																	__lowerCAmelCase					=							self._expand(snake_case_							)

																	__lowerCAmelCase					=							data_bp_input
																	__lowerCAmelCase					=							bp_outa * self.vji.T - self.thre_bpa
																	__lowerCAmelCase					=							self.sig(snake_case_							)
																	__lowerCAmelCase					=							bp_outa * self.wkj.T - self.thre_bpa
																	__lowerCAmelCase					=							self.sig(snake_case_							)
																	produce_out.extend(bp_outa.getA().tolist()							)
												__lowerCAmelCase					=							[list(map(self.do_round					,						snake_case_							)							) for each in produce_out]
												return np.asarray(snake_case_							)




							def        A__  ( self					,						_A							):
												# return the data of image after convoluting process so we can check it out
												__lowerCAmelCase					=							np.asmatrix(snake_case_							)
												__lowerCAmelCase					=							self.convolute(
												    snake_case_					,						self.conva					,						self.w_conva					,						self.thre_conva					,						conv_step=self.step_conva					,						)
												__lowerCAmelCase					=							self.pooling(snake_case_					,						self.size_poolinga							)

												return data_conveda, data_pooleda


if __name__ == "__main__":
		pass









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 ViTImageProcessor





class     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A=1_3					,						_A=3					,						_A=2_2_4					,						_A=3_0					,						_A=4_0_0					,						_A=True					,						_A=None					,						_A=True					,						_A=[0.5, 0.5, 0.5]					,						_A=[0.5, 0.5, 0.5]					,						):
												__lowerCAmelCase					=							size if size is not None else {'height': 1_8, 'width': 1_8}
												__lowerCAmelCase					=							parent
												__lowerCAmelCase					=							batch_size
												__lowerCAmelCase					=							num_channels
												__lowerCAmelCase					=							image_size
												__lowerCAmelCase					=							min_resolution
												__lowerCAmelCase					=							max_resolution
												__lowerCAmelCase					=							do_resize
												__lowerCAmelCase					=							size
												__lowerCAmelCase					=							do_normalize
												__lowerCAmelCase					=							image_mean
												__lowerCAmelCase					=							image_std




							def        A__  ( self							):
												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												}





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


							"""simple docstring"""





							__UpperCAmelCase       =ViTImageProcessor if is_vision_available() else None




							def        A__  ( self							):
												__lowerCAmelCase					=							EfficientFormerImageProcessorTester(self							)




							@property
							def        A__  ( self							):
												return self.image_proc_tester.prepare_image_processor_dict()




							def        A__  ( self							):
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												self.assertTrue(hasattr(_A					,						'image_mean'							)							)
												self.assertTrue(hasattr(_A					,						'image_std'							)							)
												self.assertTrue(hasattr(_A					,						'do_normalize'							)							)
												self.assertTrue(hasattr(_A					,						'do_resize'							)							)
												self.assertTrue(hasattr(_A					,						'size'							)							)




							def        A__  ( self							):
												pass




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PIL images
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						Image.Image							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random numpy tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A					,						numpify=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						np.ndarray							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)




							def        A__  ( self							):
												# Initialize image_processor
												__lowerCAmelCase					=							self.image_processing_class(**self.image_processor_dict							)
												# create random PyTorch tensors
												__lowerCAmelCase					=							prepare_image_inputs(self.image_proc_tester					,						equal_resolution=_A					,						torchify=_A							)
												for image in image_inputs:
																	self.assertIsInstance(_A					,						torch.Tensor							)

												# Test not batched input
												__lowerCAmelCase					=							image_processor(image_inputs[0]					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        1,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)

												# Test batched
												__lowerCAmelCase					=							image_processor(_A					,						return_tensors='pt'							).pixel_values
												self.assertEqual(
												    encoded_images.shape					,						(
												        self.image_proc_tester.batch_size,
												        self.image_proc_tester.num_channels,
												        self.image_proc_tester.size['height'],
												        self.image_proc_tester.size['width'],
												    )					,						)






