




'''simple docstring'''







import pickle

import numpy as np
from matplotlib import pyplot as plt







class 					_a :





     '''simple docstring'''



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







     def 			snake_case_      (				self							,__a							)      ->				str:
          # save model dict with pickle
          snake_case			:      List[str]        =			{
              "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 			snake_case_      (				cls							,__a							)      ->				Dict:
          # read saved model
          with open(__a							,"""rb"""							) as f:
               snake_case			:      Optional[int]        =			pickle.load(__a							)  # noqa: S301

          snake_case			:      Optional[Any]        =			model_dic.get("""conv1"""							)
          conv_get.append(model_dic.get("""step_conv1"""							)							)
          snake_case			:      Tuple        =			model_dic.get("""size_pooling1"""							)
          snake_case			:      Optional[Any]        =			model_dic.get("""num_bp1"""							)
          snake_case			:      str        =			model_dic.get("""num_bp2"""							)
          snake_case			:      Optional[int]        =			model_dic.get("""num_bp3"""							)
          snake_case			:      Union[str, Any]        =			model_dic.get("""rate_weight"""							)
          snake_case			:      List[str]        =			model_dic.get("""rate_thre"""							)
          # create model instance
          snake_case			:      Optional[int]        =			CNN(__a							,__a							,__a							,__a							,__a							,__a							,__a							)
          # modify model parameter
          snake_case			:      str        =			model_dic.get("""w_conv1"""							)
          snake_case			:      Optional[Any]        =			model_dic.get("""wkj"""							)
          snake_case			:      Tuple        =			model_dic.get("""vji"""							)
          snake_case			:      str        =			model_dic.get("""thre_conv1"""							)
          snake_case			:      Union[str, Any]        =			model_dic.get("""thre_bp2"""							)
          snake_case			:      Union[str, Any]        =			model_dic.get("""thre_bp3"""							)
          return conv_ins







     def 			snake_case_      (				self							,__a							)      ->				Tuple:
          return 1 / (1 + np.exp(-1 * x							))







     def 			snake_case_      (				self							,__a							)      ->				Tuple:
          return round(__a							,3							)







     def 			snake_case_      (				self							,__a							,__a							,__a							,__a							,__a							)      ->				List[Any]:
          # convolution process
          snake_case			:      Optional[int]        =			convs[0]
          snake_case			:      Dict        =			convs[1]
          snake_case			:      Tuple        =			np.shape(__a							)[0]
          # get the data slice of original image data, data_focus
          snake_case			:      List[Any]        =			[]
          for i_focus in range(0							,size_data - size_conv + 1							,__a							):
               for j_focus in range(0							,size_data - size_conv + 1							,__a							):
                    snake_case			:      Optional[int]        =			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
          snake_case			:      List[Any]        =			[]
          snake_case			:      Any        =			int((size_data - size_conv) / conv_step + 1							)
          for i_map in range(__a							):
               snake_case			:      List[Any]        =			[]
               for i_focus in range(len(__a							)							):
                    snake_case			:      Optional[Any]        =			(
                        np.sum(np.multiply(data_focus[i_focus]							,w_convs[i_map]							)							)
                        - thre_convs[i_map]
                    )
                    featuremap.append(self.sig(__a							)							)
               snake_case			:      Union[str, Any]        =			np.asmatrix(__a							).reshape(
                   __a							,__a							)
               data_featuremap.append(__a							)

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







     def 			snake_case_      (				self							,__a							,__a							,__a="average_pool"							)      ->				Any:
          # pooling process
          snake_case			:      Tuple        =			len(featuremaps[0]							)
          snake_case			:      int        =			int(size_map / size_pooling							)
          snake_case			:      Union[str, Any]        =			[]
          for i_map in range(len(__a							)							):
               snake_case			:      Union[str, Any]        =			featuremaps[i_map]
               snake_case			:      int        =			[]
               for i_focus in range(0							,__a							,__a							):
                    for j_focus in range(0							,__a							,__a							):
                         snake_case			:      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							)							)
               snake_case			:      List[Any]        =			np.asmatrix(__a							).reshape(__a							,__a							)
               featuremap_pooled.append(__a							)
          return featuremap_pooled







     def 			snake_case_      (				self							,__a							)      ->				int:
          # expanding three dimension data to one dimension list
          snake_case			:      Optional[int]        =			[]
          for i in range(len(__a							)							):
               snake_case			:      str        =			np.shape(data[i]							)
               snake_case			:      List[Any]        =			data[i].reshape(1							,shapes[0] * shapes[1]							)
               snake_case			:      List[str]        =			data_listed.getA().tolist()[0]
               data_expanded.extend(__a							)
          snake_case			:      List[Any]        =			np.asarray(__a							)
          return data_expanded







     def 			snake_case_      (				self							,__a							)      ->				Union[str, Any]:
          # expanding matrix to one dimension list
          snake_case			:      List[Any]        =			np.asarray(__a							)
          snake_case			:      Dict        =			np.shape(__a							)
          snake_case			:      List[str]        =			data_mat.reshape(1							,shapes[0] * shapes[1]							)
          return data_expanded







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







     def 			snake_case_      (				self							,__a							,__a							,__a							,__a							,__a							,__a=bool							)      ->				str:
          # model traning
          print("""----------------------Start Training-------------------------"""							)
          print((""" - - Shape: Train_Data  """, np.shape(__a							))							)
          print((""" - - Shape: Teach_Data  """, np.shape(__a							))							)
          snake_case			:      Tuple        =			0
          snake_case			:      Tuple        =			[]
          snake_case			:      int        =			10_000
          while rp < n_repeat and mse >= error_accuracy:
               snake_case			:      str        =			0
               print(F'''-------------Learning Time {rp}--------------'''							)
               for p in range(len(__a							)							):
                    # print('------------Learning Image: %d--------------'%p)
                    snake_case			:      List[str]        =			np.asmatrix(datas_train[p]							)
                    snake_case			:      Optional[int]        =			np.asarray(datas_teach[p]							)
                    snake_case			:      str        =			self.convolute(
                        __a							,self.conva							,self.w_conva							,self.thre_conva							,conv_step=self.step_conva							,)
                    snake_case			:      Optional[Any]        =			self.pooling(__a							,self.size_poolinga							)
                    snake_case			:      Any        =			np.shape(__a							)
                    snake_case			:      int        =			self._expand(__a							)
                    snake_case			:      List[Any]        =			data_bp_input

                    snake_case			:      Tuple        =			np.dot(__a							,self.vji.T							) - self.thre_bpa
                    snake_case			:      Dict        =			self.sig(__a							)
                    snake_case			:      List[Any]        =			np.dot(__a							,self.wkj.T							) - self.thre_bpa
                    snake_case			:      int        =			self.sig(__a							)

                    # --------------Model Leaning ------------------------
                    # calculate error and gradient---------------
                    snake_case			:      Any        =			np.multiply(
                        (data_teach - bp_outa)							,np.multiply(__a							,(1 - bp_outa)							)							)
                    snake_case			:      Optional[int]        =			np.multiply(
                        np.dot(__a							,self.wkj							)							,np.multiply(__a							,(1 - bp_outa)							)							)
                    snake_case			:      Optional[int]        =			np.dot(__a							,self.vji							)

                    snake_case			:      Dict        =			pd_i_all / (self.size_poolinga * self.size_poolinga)
                    snake_case			:      int        =			pd_conva_pooled.T.getA().tolist()
                    snake_case			:      Dict        =			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]							):
                         snake_case			:      int        =			self._expand_mat(pd_conva_all[k_conv]							)
                         snake_case			:      List[str]        =			self.rate_weight * np.dot(__a							,__a							)

                         snake_case			:      Optional[Any]        =			self.w_conva[k_conv] + delta_w.reshape(
                             (self.conva[0], self.conva[0])							)

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

          def draw_error():
               snake_case			:      int        =			[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 			snake_case_      (				self							,__a							)      ->				Optional[Any]:
          # model predict
          snake_case			:      Union[str, Any]        =			[]
          print("""-------------------Start Testing-------------------------"""							)
          print((""" - - Shape: Test_Data  """, np.shape(__a							))							)
          for p in range(len(__a							)							):
               snake_case			:      Any        =			np.asmatrix(datas_test[p]							)
               snake_case			:      Tuple        =			self.convolute(
                   __a							,self.conva							,self.w_conva							,self.thre_conva							,conv_step=self.step_conva							,)
               snake_case			:      List[str]        =			self.pooling(__a							,self.size_poolinga							)
               snake_case			:      str        =			self._expand(__a							)

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







     def 			snake_case_      (				self							,__a							)      ->				List[Any]:
          # return the data of image after convoluting process so we can check it out
          snake_case			:      Optional[Any]        =			np.asmatrix(__a							)
          snake_case			:      Dict        =			self.convolute(
              __a							,self.conva							,self.w_conva							,self.thre_conva							,conv_step=self.step_conva							,)
          snake_case			:      str        =			self.pooling(__a							,self.size_poolinga							)

          return data_conveda, data_pooleda


if __name__ == "__main__":
      pass








"""simple docstring"""




import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:






 class       UpperCAmelCase_    :



       @staticmethod
       def 		snake_case_   (	*A	:      Union[str, Any]	,      **A	:      Dict		):
            pass






@is_pipeline_test
@require_vision
class       UpperCAmelCase_    (							unittest.TestCase		):



      @require_torch
      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       pipeline(
               model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"	,      )
           _UpperCAmelCase     :      Union[str, Any]														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
           _UpperCAmelCase     :      Tuple														=       image_classifier(A	,      candidate_labels=["a", "b", "c"]		)

           # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
           # python and torch versions.
           self.assertIn(
               nested_simplify(A		)	,      [
                   [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
                   [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
               ]	,      )

           _UpperCAmelCase     :      str														=       image_classifier([image] * 5	,      candidate_labels=["A", "B", "C"]	,      batch_size=2		)
           self.assertEqual(
               nested_simplify(A		)	,      [
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
               ]	,      )



      @require_tf
      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      List[str]														=       pipeline(
               model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"	,      framework="tf"		)
           _UpperCAmelCase     :      int														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
           _UpperCAmelCase     :      Union[str, Any]														=       image_classifier(A	,      candidate_labels=["a", "b", "c"]		)

           self.assertEqual(
               nested_simplify(A		)	,      [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}]	,      )

           _UpperCAmelCase     :      Optional[Any]														=       image_classifier([image] * 5	,      candidate_labels=["A", "B", "C"]	,      batch_size=2		)
           self.assertEqual(
               nested_simplify(A		)	,      [
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
                   [
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                       {"score": 0.333, "label": ANY(A		)},
                   ],
               ]	,      )



      @slow
      @require_torch
      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Optional[Any]														=       pipeline(
               task="zero-shot-image-classification"	,      model="openai/clip-vit-base-patch32"	,      )
           # This is an image of 2 cats with remotes and no planes
           _UpperCAmelCase     :      Dict														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
           _UpperCAmelCase     :      List[Any]														=       image_classifier(A	,      candidate_labels=["cat", "plane", "remote"]		)

           self.assertEqual(
               nested_simplify(A		)	,      [
                   {"score": 0.511, "label": "remote"},
                   {"score": 0.485, "label": "cat"},
                   {"score": 0.004, "label": "plane"},
               ]	,      )

           _UpperCAmelCase     :      Tuple														=       image_classifier([image] * 5	,      candidate_labels=["cat", "plane", "remote"]	,      batch_size=2		)
           self.assertEqual(
               nested_simplify(A		)	,      [
                   [
                       {"score": 0.511, "label": "remote"},
                       {"score": 0.485, "label": "cat"},
                       {"score": 0.004, "label": "plane"},
                   ],
               ]
               * 5	,      )



      @slow
      @require_tf
      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      List[Any]														=       pipeline(
               task="zero-shot-image-classification"	,      model="openai/clip-vit-base-patch32"	,      framework="tf"		)
           # This is an image of 2 cats with remotes and no planes
           _UpperCAmelCase     :      Union[str, Any]														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
           _UpperCAmelCase     :      Union[str, Any]														=       image_classifier(A	,      candidate_labels=["cat", "plane", "remote"]		)
           self.assertEqual(
               nested_simplify(A		)	,      [
                   {"score": 0.511, "label": "remote"},
                   {"score": 0.485, "label": "cat"},
                   {"score": 0.004, "label": "plane"},
               ]	,      )

           _UpperCAmelCase     :      Union[str, Any]														=       image_classifier([image] * 5	,      candidate_labels=["cat", "plane", "remote"]	,      batch_size=2		)
           self.assertEqual(
               nested_simplify(A		)	,      [
                   [
                       {"score": 0.511, "label": "remote"},
                       {"score": 0.485, "label": "cat"},
                       {"score": 0.004, "label": "plane"},
                   ],
               ]
               * 5	,      )
