"""simple docstring"""




import pickle

import numpy as np
from matplotlib import pyplot as plt
class 						__A							:






     def __init__(		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__=0.2    ,					a__=0.2  ):
       _lowerCAmelCase						:	Union[str, Any]        =					bp_numa
       _lowerCAmelCase						:	Tuple        =					bp_numa
       _lowerCAmelCase						:	Union[str, Any]        =					bp_numa
       _lowerCAmelCase						:	List[str]        =					conva_get[:2]
       _lowerCAmelCase						:	List[Any]        =					conva_get[2]
       _lowerCAmelCase						:	Union[str, Any]        =					size_pa
       _lowerCAmelCase						:	List[Any]        =					rate_w
       _lowerCAmelCase						:	int        =					rate_t
       _lowerCAmelCase						:	List[str]        =					[
           np.mat(-1 * np.random.rand(self.conva[0]    ,					self.conva[0]  ) + 0.5  )
           for i in range(self.conva[1]  )
       ]
       _lowerCAmelCase						:	List[str]        =					np.mat(-1 * np.random.rand(self.num_bpa    ,					self.num_bpa  ) + 0.5  )
       _lowerCAmelCase						:	Optional[Any]        =					np.mat(-1 * np.random.rand(self.num_bpa    ,					self.num_bpa  ) + 0.5  )
       _lowerCAmelCase						:	int        =					-2 * np.random.rand(self.conva[1]  ) + 1
       _lowerCAmelCase						:	List[str]        =					-2 * np.random.rand(self.num_bpa  ) + 1
       _lowerCAmelCase						:	Any        =					-2 * np.random.rand(self.num_bpa  ) + 1






     def    __A  (		self    ,					a__  ):
       # save model dict with pickle
       _lowerCAmelCase						:	Tuple        =					{
           """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(a__    ,					"""wb"""  ) as f:
         pickle.dump(a__    ,					a__  )

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






     @classmethod
     def    __A  (		cls    ,					a__  ):
       # read saved model
       with open(a__    ,					"""rb"""  ) as f:
         _lowerCAmelCase						:	str        =					pickle.load(a__  )  # noqa: S301

       _lowerCAmelCase						:	Dict        =					model_dic.get("""conv1"""  )
       conv_get.append(model_dic.get("""step_conv1"""  )  )
       _lowerCAmelCase						:	Optional[int]        =					model_dic.get("""size_pooling1"""  )
       _lowerCAmelCase						:	List[Any]        =					model_dic.get("""num_bp1"""  )
       _lowerCAmelCase						:	List[Any]        =					model_dic.get("""num_bp2"""  )
       _lowerCAmelCase						:	Union[str, Any]        =					model_dic.get("""num_bp3"""  )
       _lowerCAmelCase						:	Optional[Any]        =					model_dic.get("""rate_weight"""  )
       _lowerCAmelCase						:	Any        =					model_dic.get("""rate_thre"""  )
       # create model instance
       _lowerCAmelCase						:	Any        =					CNN(a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  )
       # modify model parameter
       _lowerCAmelCase						:	Dict        =					model_dic.get("""w_conv1"""  )
       _lowerCAmelCase						:	Optional[Any]        =					model_dic.get("""wkj"""  )
       _lowerCAmelCase						:	List[Any]        =					model_dic.get("""vji"""  )
       _lowerCAmelCase						:	List[Any]        =					model_dic.get("""thre_conv1"""  )
       _lowerCAmelCase						:	Union[str, Any]        =					model_dic.get("""thre_bp2"""  )
       _lowerCAmelCase						:	Union[str, Any]        =					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(a__    ,					3  )






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

       # expanding the data slice to One dimenssion
       _lowerCAmelCase						:	List[Any]        =					[]
       for each_focus in data_focus:
         focusa_list.extend(self.Expand_Mat(a__  )  )
       _lowerCAmelCase						:	Optional[int]        =					np.asarray(a__  )
       return focus_list, data_featuremap






     def    __A  (		self    ,					a__    ,					a__    ,					a__="average_pool"  ):
       # pooling process
       _lowerCAmelCase						:	str        =					len(featuremaps[0]  )
       _lowerCAmelCase						:	Optional[int]        =					int(size_map / size_pooling  )
       _lowerCAmelCase						:	List[str]        =					[]
       for i_map in range(len(a__  )  ):
         _lowerCAmelCase						:	Tuple        =					featuremaps[i_map]
         _lowerCAmelCase						:	Union[str, Any]        =					[]
         for i_focus in range(0    ,					a__    ,					a__  ):
           for j_focus in range(0    ,					a__    ,					a__  ):
             _lowerCAmelCase						:	Tuple        =					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(a__  )  )
             elif pooling_type == "max_pooling":
               # max pooling
               map_pooled.append(np.max(a__  )  )
         _lowerCAmelCase						:	List[str]        =					np.asmatrix(a__  ).reshape(a__    ,					a__  )
         featuremap_pooled.append(a__  )
       return featuremap_pooled






     def    __A  (		self    ,					a__  ):
       # expanding three dimension data to one dimension list
       _lowerCAmelCase						:	List[Any]        =					[]
       for i in range(len(a__  )  ):
         _lowerCAmelCase						:	Tuple        =					np.shape(data[i]  )
         _lowerCAmelCase						:	Tuple        =					data[i].reshape(1    ,					shapes[0] * shapes[1]  )
         _lowerCAmelCase						:	List[Any]        =					data_listed.getA().tolist()[0]
         data_expanded.extend(a__  )
       _lowerCAmelCase						:	Tuple        =					np.asarray(a__  )
       return data_expanded






     def    __A  (		self    ,					a__  ):
       # expanding matrix to one dimension list
       _lowerCAmelCase						:	Tuple        =					np.asarray(a__  )
       _lowerCAmelCase						:	Tuple        =					np.shape(a__  )
       _lowerCAmelCase						:	Tuple        =					data_mat.reshape(1    ,					shapes[0] * shapes[1]  )
       return data_expanded






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Optional[int]        =					[]
       _lowerCAmelCase						:	Union[str, Any]        =					0
       for i_map in range(a__  ):
         _lowerCAmelCase						:	Optional[int]        =					np.ones((size_map, size_map)  )
         for i in range(0    ,					a__    ,					a__  ):
           for j in range(0    ,					a__    ,					a__  ):
             _lowerCAmelCase						:	Tuple        =					pd_pool[
                 i_pool
             ]
             _lowerCAmelCase						:	Optional[int]        =					i_pool + 1
         _lowerCAmelCase						:	Dict        =					np.multiply(
             a__    ,					np.multiply(out_map[i_map]    ,					(1 - out_map[i_map])  )  )
         pd_all.append(a__  )
       return pd_all






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__=bool  ):
       # model traning
       print("""----------------------Start Training-------------------------"""  )
       print((""" - - Shape: Train_Data  """, np.shape(a__  ))  )
       print((""" - - Shape: Teach_Data  """, np.shape(a__  ))  )
       _lowerCAmelCase						:	List[str]        =					0
       _lowerCAmelCase						:	Any        =					[]
       _lowerCAmelCase						:	int        =					10000
       while rp < n_repeat and mse >= error_accuracy:
         _lowerCAmelCase						:	str        =					0
         print(F"-------------Learning Time {rp}--------------"  )
         for p in range(len(a__  )  ):
           # print('------------Learning Image: %d--------------'%p)
           _lowerCAmelCase						:	List[Any]        =					np.asmatrix(datas_train[p]  )
           _lowerCAmelCase						:	Union[str, Any]        =					np.asarray(datas_teach[p]  )
           _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[Any]        =					self.convolute(
               a__    ,					self.conva    ,					self.w_conva    ,					self.thre_conva    ,					conv_step=self.step_conva    ,					)
           _lowerCAmelCase						:	Dict        =					self.pooling(a__    ,					self.size_poolinga  )
           _lowerCAmelCase						:	Any        =					np.shape(a__  )
           _lowerCAmelCase						:	Optional[int]        =					self._expand(a__  )
           _lowerCAmelCase						:	Any        =					data_bp_input

           _lowerCAmelCase						:	Any        =					np.dot(a__    ,					self.vji.T  ) - self.thre_bpa
           _lowerCAmelCase						:	int        =					self.sig(a__  )
           _lowerCAmelCase						:	Union[str, Any]        =					np.dot(a__    ,					self.wkj.T  ) - self.thre_bpa
           _lowerCAmelCase						:	List[str]        =					self.sig(a__  )

           # --------------Model Leaning ------------------------
           # calculate error and gradient---------------
           _lowerCAmelCase						:	Dict        =					np.multiply(
               (data_teach - bp_outa)    ,					np.multiply(a__    ,					(1 - bp_outa)  )  )
           _lowerCAmelCase						:	Tuple        =					np.multiply(
               np.dot(a__    ,					self.wkj  )    ,					np.multiply(a__    ,					(1 - bp_outa)  )  )
           _lowerCAmelCase						:	Optional[int]        =					np.dot(a__    ,					self.vji  )

           _lowerCAmelCase						:	int        =					pd_i_all / (self.size_poolinga * self.size_poolinga)
           _lowerCAmelCase						:	List[Any]        =					pd_conva_pooled.T.getA().tolist()
           _lowerCAmelCase						:	Any        =					self._calculate_gradient_from_pool(
               a__    ,					a__    ,					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						:	Tuple        =					self._expand_mat(pd_conva_all[k_conv]  )
             _lowerCAmelCase						:	Optional[Any]        =					self.rate_weight * np.dot(a__    ,					a__  )

             _lowerCAmelCase						:	Tuple        =					self.w_conva[k_conv] + delta_w.reshape(
                 (self.conva[0], self.conva[0])  )

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

       def draw_error():
         _lowerCAmelCase						:	str        =					[error_accuracy for i in range(int(n_repeat * 1.2  )  )]
         plt.plot(a__    ,					"""+-"""  )
         plt.plot(a__    ,					"""r--"""  )
         plt.xlabel("""Learning Times"""  )
         plt.ylabel("""All_mse"""  )
         plt.grid(a__    ,					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						:	List[Any]        =					[]
       print("""-------------------Start Testing-------------------------"""  )
       print((""" - - Shape: Test_Data  """, np.shape(a__  ))  )
       for p in range(len(a__  )  ):
         _lowerCAmelCase						:	Optional[int]        =					np.asmatrix(datas_test[p]  )
         _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[Any]        =					self.convolute(
             a__    ,					self.conva    ,					self.w_conva    ,					self.thre_conva    ,					conv_step=self.step_conva    ,					)
         _lowerCAmelCase						:	Dict        =					self.pooling(a__    ,					self.size_poolinga  )
         _lowerCAmelCase						:	str        =					self._expand(a__  )

         _lowerCAmelCase						:	Optional[int]        =					data_bp_input
         _lowerCAmelCase						:	Optional[int]        =					bp_outa * self.vji.T - self.thre_bpa
         _lowerCAmelCase						:	Tuple        =					self.sig(a__  )
         _lowerCAmelCase						:	Any        =					bp_outa * self.wkj.T - self.thre_bpa
         _lowerCAmelCase						:	Dict        =					self.sig(a__  )
         produce_out.extend(bp_outa.getA().tolist()  )
       _lowerCAmelCase						:	int        =					[list(map(self.do_round    ,					a__  )  ) for each in produce_out]
       return np.asarray(a__  )






     def    __A  (		self    ,					a__  ):
       # return the data of image after convoluting process so we can check it out
       _lowerCAmelCase						:	List[Any]        =					np.asmatrix(a__  )
       _lowerCAmelCase      ,     _lowerCAmelCase						:	str        =					self.convolute(
           a__    ,					self.conva    ,					self.w_conva    ,					self.thre_conva    ,					conv_step=self.step_conva    ,					)
       _lowerCAmelCase						:	Tuple        =					self.pooling(a__    ,					self.size_poolinga  )

       return data_conveda, data_pooleda


if __name__ == "__main__":
     pass





"""simple docstring"""




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		):






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






     def    __A  (		self  ):
       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							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[str]		 =		PoolFormerImageProcessor if is_vision_available() else None






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					PoolFormerImageProcessingTester(self  )






     @property
     def    __A  (		self  ):
       return self.image_processor_tester.prepare_image_processor_dict()






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__    ,					"""do_resize_and_center_crop"""  )  )
       self.assertTrue(hasattr(a__    ,					"""size"""  )  )
       self.assertTrue(hasattr(a__    ,					"""crop_pct"""  )  )
       self.assertTrue(hasattr(a__    ,					"""do_normalize"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_mean"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_std"""  )  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					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}  )

       _lowerCAmelCase						:	List[Any]        =					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  ):
       pass






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:	Tuple        =					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						:	Tuple        =					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						:	List[str]        =					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    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Union[str, Any]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:	Union[str, Any]        =					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						:	Dict        =					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						:	int        =					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    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:	Any        =					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						:	str        =					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						:	List[str]        =					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"""],
           )    ,					)




