


import pickle

import numpy as np
from matplotlib import pyplot as plt



class 	__lowerCamelCase			:







       def __init__(			self: str,A_: Optional[Any],A_: Any,A_: Optional[Any],A_: Dict,A_: Union[str, Any],A_: Optional[int]=0.2,A_: Optional[Any]=0.2					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Union[str, Any],A_: int					):






            '''simple docstring'''




            __UpperCamelCase =			{
                '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: Union[str, Any],A_: int					):






            '''simple docstring'''




            with open(A_,'rb'					) as f:
                 __UpperCamelCase =			pickle.load(A_					)  # noqa: S301

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







       def 		snake_case_	(			self: Optional[Any],A_: str					):






            '''simple docstring'''




            return 1 / (1 + np.exp(-1 * x					))







       def 		snake_case_	(			self: Tuple,A_: Tuple					):






            '''simple docstring'''




            return round(A_,3					)







       def 		snake_case_	(			self: Dict,A_: Tuple,A_: Any,A_: Union[str, Any],A_: Optional[Any],A_: Any					):






            '''simple docstring'''




            __UpperCamelCase =			convs[0]
            __UpperCamelCase =			convs[1]
            __UpperCamelCase =			np.shape(A_					)[0]
            # get the data slice of original image data, data_focus
            __UpperCamelCase =			[]
            for i_focus in range(0,size_data - size_conv + 1,A_					):
                 for j_focus in range(0,size_data - size_conv + 1,A_					):
                      __UpperCamelCase =			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
            __UpperCamelCase =			[]
            __UpperCamelCase =			int((size_data - size_conv) / conv_step + 1					)
            for i_map in range(A_					):
                 __UpperCamelCase =			[]
                 for i_focus in range(len(A_					)					):
                      __UpperCamelCase =			(
                          np.sum(np.multiply(data_focus[i_focus],w_convs[i_map]					)					)
                          - thre_convs[i_map]
                      )
                      featuremap.append(self.sig(A_					)					)
                 __UpperCamelCase =			np.asmatrix(A_					).reshape(
                     A_,A_					)
                 data_featuremap.append(A_					)

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







       def 		snake_case_	(			self: List[str],A_: List[str],A_: Dict,A_: Any="average_pool"					):






            '''simple docstring'''




            __UpperCamelCase =			len(featuremaps[0]					)
            __UpperCamelCase =			int(size_map / size_pooling					)
            __UpperCamelCase =			[]
            for i_map in range(len(A_					)					):
                 __UpperCamelCase =			featuremaps[i_map]
                 __UpperCamelCase =			[]
                 for i_focus in range(0,A_,A_					):
                      for j_focus in range(0,A_,A_					):
                           __UpperCamelCase =			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_					)					)
                 __UpperCamelCase =			np.asmatrix(A_					).reshape(A_,A_					)
                 featuremap_pooled.append(A_					)
            return featuremap_pooled







       def 		snake_case_	(			self: List[str],A_: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			[]
            for i in range(len(A_					)					):
                 __UpperCamelCase =			np.shape(data[i]					)
                 __UpperCamelCase =			data[i].reshape(1,shapes[0] * shapes[1]					)
                 __UpperCamelCase =			data_listed.getA().tolist()[0]
                 data_expanded.extend(A_					)
            __UpperCamelCase =			np.asarray(A_					)
            return data_expanded







       def 		snake_case_	(			self: Any,A_: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			np.asarray(A_					)
            __UpperCamelCase =			np.shape(A_					)
            __UpperCamelCase =			data_mat.reshape(1,shapes[0] * shapes[1]					)
            return data_expanded







       def 		snake_case_	(			self: int,A_: str,A_: int,A_: Optional[int],A_: Any,A_: str					):






            '''simple docstring'''




            __UpperCamelCase =			[]
            __UpperCamelCase =			0
            for i_map in range(A_					):
                 __UpperCamelCase =			np.ones((size_map, size_map)					)
                 for i in range(0,A_,A_					):
                      for j in range(0,A_,A_					):
                           __UpperCamelCase =			pd_pool[
                               i_pool
                           ]
                           __UpperCamelCase =			i_pool + 1
                 __UpperCamelCase =			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: Tuple,A_: Union[str, Any],A_: str,A_: int,A_: Union[str, Any],A_: str,A_: Tuple=bool					):






            '''simple docstring'''




            print('----------------------Start Training-------------------------'					)
            print((' - - Shape: Train_Data  ', np.shape(A_					))					)
            print((' - - Shape: Teach_Data  ', np.shape(A_					))					)
            __UpperCamelCase =			0
            __UpperCamelCase =			[]
            __UpperCamelCase =			1_0000
            while rp < n_repeat and mse >= error_accuracy:
                 __UpperCamelCase =			0
                 print(F'''-------------Learning Time {rp}--------------'''					)
                 for p in range(len(A_					)					):
                      # print('------------Learning Image: %d--------------'%p)
                      __UpperCamelCase =			np.asmatrix(datas_train[p]					)
                      __UpperCamelCase =			np.asarray(datas_teach[p]					)
                      __UpperCamelCase,    __UpperCamelCase =			self.convolute(
                          A_,self.conva,self.w_conva,self.thre_conva,conv_step=self.step_conva,)
                      __UpperCamelCase =			self.pooling(A_,self.size_poolinga					)
                      __UpperCamelCase =			np.shape(A_					)
                      __UpperCamelCase =			self._expand(A_					)
                      __UpperCamelCase =			data_bp_input

                      __UpperCamelCase =			np.dot(A_,self.vji.T					) - self.thre_bpa
                      __UpperCamelCase =			self.sig(A_					)
                      __UpperCamelCase =			np.dot(A_,self.wkj.T					) - self.thre_bpa
                      __UpperCamelCase =			self.sig(A_					)

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

                      __UpperCamelCase =			pd_i_all / (self.size_poolinga * self.size_poolinga)
                      __UpperCamelCase =			pd_conva_pooled.T.getA().tolist()
                      __UpperCamelCase =			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]					):
                           __UpperCamelCase =			self._expand_mat(pd_conva_all[k_conv]					)
                           __UpperCamelCase =			self.rate_weight * np.dot(A_,A_					)

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

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

            def draw_error():
                 __UpperCamelCase =			[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: Optional[Any],A_: List[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			[]
            print('-------------------Start Testing-------------------------'					)
            print((' - - Shape: Test_Data  ', np.shape(A_					))					)
            for p in range(len(A_					)					):
                 __UpperCamelCase =			np.asmatrix(datas_test[p]					)
                 __UpperCamelCase,    __UpperCamelCase =			self.convolute(
                     A_,self.conva,self.w_conva,self.thre_conva,conv_step=self.step_conva,)
                 __UpperCamelCase =			self.pooling(A_,self.size_poolinga					)
                 __UpperCamelCase =			self._expand(A_					)

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







       def 		snake_case_	(			self: Tuple,A_: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			np.asmatrix(A_					)
            __UpperCamelCase,    __UpperCamelCase =			self.convolute(
                A_,self.conva,self.w_conva,self.thre_conva,conv_step=self.step_conva,)
            __UpperCamelCase =			self.pooling(A_,self.size_poolinga					)

            return data_conveda, data_pooleda


if __name__ == "__main__":
    pass







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     __a			:



					@staticmethod
					def 		UpperCamelCase				(    *snake_case_				:     Any					,						**snake_case_				:     str)->				int:
										pass






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



	@require_torch
	def 		UpperCamelCase				(    self				:     Dict)->				List[str]:
						__lowerCAmelCase						 =pipeline(
						    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""					,						)
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						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(snake_case_)					,						[
						        [{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """b"""}, {"""score""": 0.3_3_3, """label""": """c"""}],
						        [{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """c"""}, {"""score""": 0.3_3_3, """label""": """b"""}],
						    ]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""A""", """B""", """C"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						    ]					,						)



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

						self.assertEqual(
						    nested_simplify(snake_case_)					,						[{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """b"""}, {"""score""": 0.3_3_3, """label""": """c"""}]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""A""", """B""", """C"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						    ]					,						)



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

						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        {"""score""": 0.5_1_1, """label""": """remote"""},
						        {"""score""": 0.4_8_5, """label""": """cat"""},
						        {"""score""": 0.0_0_4, """label""": """plane"""},
						    ]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""cat""", """plane""", """remote"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.5_1_1, """label""": """remote"""},
						            {"""score""": 0.4_8_5, """label""": """cat"""},
						            {"""score""": 0.0_0_4, """label""": """plane"""},
						        ],
						    ]
						    * 5					,						)



	@slow
	@require_tf
	def 		UpperCamelCase				(    self				:     Optional[int])->				int:
						__lowerCAmelCase						 =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
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""cat""", """plane""", """remote"""])
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        {"""score""": 0.5_1_1, """label""": """remote"""},
						        {"""score""": 0.4_8_5, """label""": """cat"""},
						        {"""score""": 0.0_0_4, """label""": """plane"""},
						    ]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""cat""", """plane""", """remote"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.5_1_1, """label""": """remote"""},
						            {"""score""": 0.4_8_5, """label""": """cat"""},
						            {"""score""": 0.0_0_4, """label""": """plane"""},
						        ],
						    ]
						    * 5					,						)




