





'''simple docstring'''







import pickle

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







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







     def     UpperCamelCase			( self    ,   A__    ) ->	Tuple:
      # save model dict with pickle
      snake_case				=			{
          '''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     UpperCamelCase			( cls    ,   A__    ) ->	Optional[Any]:
      # read saved model
      with open(A__    ,   '''rb'''    ) as f:
       snake_case				=			pickle.load(A__    )  # noqa: S301

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







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







     def     UpperCamelCase			( self    ,   A__    ) ->	Any:
      return round(A__    ,   3    )







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

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







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







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







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







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Optional[Any]:
      snake_case				=			[]
      snake_case				=			0
      for i_map in range(A__    ):
       snake_case				=			np.ones((size_map, size_map)    )
       for i in range(0    ,   A__    ,   A__    ):
        for j in range(0    ,   A__    ,   A__    ):
         snake_case				=			pd_pool[
             i_pool
         ]
         snake_case				=			i_pool + 1
       snake_case				=			np.multiply(
           A__    ,   np.multiply(out_map[i_map]    ,   (1 - out_map[i_map])    )    )
       pd_all.append(A__    )
      return pd_all







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__=bool    ) ->	List[str]:
      # model traning
      print('''----------------------Start Training-------------------------'''    )
      print((''' - - Shape: Train_Data  ''', np.shape(A__    ))    )
      print((''' - - Shape: Teach_Data  ''', np.shape(A__    ))    )
      snake_case				=			0
      snake_case				=			[]
      snake_case				=			1_00_00
      while rp < n_repeat and mse >= error_accuracy:
       snake_case				=			0
       print(F"""-------------Learning Time {rp}--------------"""    )
       for p in range(len(A__    )    ):
        # print('------------Learning Image: %d--------------'%p)
        snake_case				=			np.asmatrix(datas_train[p]    )
        snake_case				=			np.asarray(datas_teach[p]    )
        snake_case				,		snake_case				=			self.convolute(
            A__    ,   self.conva    ,   self.w_conva    ,   self.thre_conva    ,   conv_step=self.step_conva    ,   )
        snake_case				=			self.pooling(A__    ,   self.size_poolinga    )
        snake_case				=			np.shape(A__    )
        snake_case				=			self._expand(A__    )
        snake_case				=			data_bp_input

        snake_case				=			np.dot(A__    ,   self.vji.T    ) - self.thre_bpa
        snake_case				=			self.sig(A__    )
        snake_case				=			np.dot(A__    ,   self.wkj.T    ) - self.thre_bpa
        snake_case				=			self.sig(A__    )

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

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

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

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

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

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







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

      return data_conveda, data_pooleda


if __name__ == "__main__":
   pass








'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor
class 			_lowercase						:







     def __init__( self    ,   A__    ,   A__=13    ,   A__=30    ,   A__=2    ,   A__=3    ,   A__=True    ,   A__=True    ,   A__=32    ,   A__=2    ,   A__=4    ,   A__=37    ,   A__="gelu"    ,   A__=0.1    ,   A__=0.1    ,   A__=10    ,   A__=0.0_2    ,   A__=3    ,   A__=None    ,   ) ->	List[Any]:
      snake_case				=			parent
      snake_case				=			batch_size
      snake_case				=			image_size
      snake_case				=			patch_size
      snake_case				=			num_channels
      snake_case				=			is_training
      snake_case				=			use_labels
      snake_case				=			hidden_size
      snake_case				=			num_hidden_layers
      snake_case				=			num_attention_heads
      snake_case				=			intermediate_size
      snake_case				=			hidden_act
      snake_case				=			hidden_dropout_prob
      snake_case				=			attention_probs_dropout_prob
      snake_case				=			type_sequence_label_size
      snake_case				=			initializer_range
      snake_case				=			scope

      # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
      snake_case				=			(image_size // patch_size) ** 2
      snake_case				=			num_patches + 1







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

      snake_case				=			None
      if self.use_labels:
       snake_case				=			ids_tensor([self.batch_size]    ,   self.type_sequence_label_size    )

      snake_case				=			self.get_config()

      return config, pixel_values, labels







     def     UpperCamelCase			( self    ) ->	int:
      return ViTConfig(
          image_size=self.image_size    ,   patch_size=self.patch_size    ,   num_channels=self.num_channels    ,   hidden_size=self.hidden_size    ,   num_hidden_layers=self.num_hidden_layers    ,   num_attention_heads=self.num_attention_heads    ,   intermediate_size=self.intermediate_size    ,   hidden_act=self.hidden_act    ,   hidden_dropout_prob=self.hidden_dropout_prob    ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,   is_decoder=A__    ,   initializer_range=self.initializer_range    ,   )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			TFViTModel(config=A__    )
      snake_case				=			model(A__    ,   training=A__    )
      self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)    )

      # Test with an image with different size than the one specified in config.
      snake_case				=			self.image_size // 2
      snake_case				=			pixel_values[:, :, :image_size, :image_size]
      snake_case				=			model(A__    ,   interpolate_pos_encoding=A__    ,   training=A__    )
      snake_case				=			(image_size // self.patch_size) ** 2 + 1
      self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, seq_length, self.hidden_size)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Optional[int]:
      snake_case				=			self.type_sequence_label_size
      snake_case				=			TFViTForImageClassification(A__    )
      snake_case				=			model(A__    ,   labels=A__    ,   training=A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )

      # Test with an image with different size than the one specified in config.
      snake_case				=			self.image_size // 2
      snake_case				=			pixel_values[:, :, :image_size, :image_size]
      snake_case				=			model(A__    ,   interpolate_pos_encoding=A__    ,   training=A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )

      # test greyscale images
      snake_case				=			1
      snake_case				=			TFViTForImageClassification(A__    )
      snake_case				=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
      snake_case				=			model(A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.prepare_config_and_inputs()
      snake_case				,		snake_case				,		snake_case				=			config_and_inputs
      snake_case				=			{'''pixel_values''': pixel_values}
      return config, inputs_dict




@require_tf
class 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
     _UpperCAmelCase										=					(
         {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
         if is_tf_available()
         else {}
     )

     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					False







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			TFViTModelTester(self    )
      snake_case				=			ConfigTester(self    ,   config_class=A__    ,   has_text_modality=A__    ,   hidden_size=37    )







     def     UpperCamelCase			( self    ) ->	int:
      self.config_tester.run_common_tests()







     @unittest.skip(reason='''ViT does not use inputs_embeds'''    )
     def     UpperCamelCase			( self    ) ->	int:
      pass







     @unittest.skip(reason='''ViT does not use inputs_embeds'''    )
     def     UpperCamelCase			( self    ) ->	str:
      pass







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       snake_case				=			model_class(A__    )
       self.assertIsInstance(model.get_input_embeddings()    ,   (tf.keras.layers.Layer)    )
       snake_case				=			model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(A__    ,   tf.keras.layers.Layer    )    )







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       snake_case				=			model_class(A__    )
       snake_case				=			inspect.signature(model.call    )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       snake_case				=			[*signature.parameters.keys()]

       snake_case				=			['''pixel_values''']
       self.assertListEqual(arg_names[:1]    ,   A__    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A__    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A__    )







     @slow
     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			TFViTModel.from_pretrained('''google/vit-base-patch16-224'''    )
      self.assertIsNotNone(A__    )




def    __UpperCamelCase	(	) ->Any:
 snake_case				=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
 return image






@require_tf
@require_vision
class 			_lowercase						(       unittest.TestCase					):







     @cached_property
     def     UpperCamelCase			( self    ) ->	Optional[int]:
      return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''    ) if is_vision_available() else None







     @slow
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''    )

      snake_case				=			self.default_image_processor
      snake_case				=			prepare_img()
      snake_case				=			image_processor(images=A__    ,   return_tensors='''tf'''    )

      # forward pass
      snake_case				=			model(**A__    )

      # verify the logits
      snake_case				=			tf.TensorShape((1, 10_00)    )
      self.assertEqual(outputs.logits.shape    ,   A__    )

      snake_case				=			tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]    )

      tf.debugging.assert_near(outputs.logits[0, :3]    ,   A__    ,   atol=1e-4    )

