import pickle

import numpy as np
from matplotlib import pyplot as plt



class 				a							:







 """simple docstring"""






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



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					List[Any]:
        # save model dict with pickle
        _A        =  {
            """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(lowerCAmelCase_		,				"""wb"""							) as f:
               pickle.dump(lowerCAmelCase_		,				lowerCAmelCase_							)

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



 @classmethod
 def      UpperCAmelCase							(					cls		,				lowerCAmelCase_							)  ->					str:
        # read saved model
        with open(lowerCAmelCase_		,				"""rb"""							) as f:
               _A        =  pickle.load(lowerCAmelCase_							)  # noqa: S301

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



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



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					Dict:
        return round(lowerCAmelCase_		,				3							)



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

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



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_="average_pool"							)  ->					Any:
        # pooling process
        _A        =  len(featuremaps[0]							)
        _A        =  int(size_map / size_pooling							)
        _A        =  []
        for i_map in range(len(lowerCAmelCase_							)							):
               _A        =  featuremaps[i_map]
               _A        =  []
               for i_focus in range(0		,				lowerCAmelCase_		,				lowerCAmelCase_							):
                      for j_focus in range(0		,				lowerCAmelCase_		,				lowerCAmelCase_							):
                             _A        =  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(lowerCAmelCase_							)							)
                             elif pooling_type == "max_pooling":
                                    # max pooling
                                    map_pooled.append(np.max(lowerCAmelCase_							)							)
               _A        =  np.asmatrix(lowerCAmelCase_							).reshape(lowerCAmelCase_		,				lowerCAmelCase_							)
               featuremap_pooled.append(lowerCAmelCase_							)
        return featuremap_pooled



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



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					List[Any]:
        # expanding matrix to one dimension list
        _A        =  np.asarray(lowerCAmelCase_							)
        _A        =  np.shape(lowerCAmelCase_							)
        _A        =  data_mat.reshape(1		,				shapes[0] * shapes[1]							)
        return data_expanded



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Union[str, Any]:
        _A        =  []
        _A        =  0
        for i_map in range(lowerCAmelCase_							):
               _A        =  np.ones((size_map, size_map)							)
               for i in range(0		,				lowerCAmelCase_		,				lowerCAmelCase_							):
                      for j in range(0		,				lowerCAmelCase_		,				lowerCAmelCase_							):
                             _A        =  pd_pool[
                                 i_pool
                             ]
                             _A        =  i_pool + 1
               _A        =  np.multiply(
                   lowerCAmelCase_		,				np.multiply(out_map[i_map]		,				(1 - out_map[i_map])							)							)
               pd_all.append(lowerCAmelCase_							)
        return pd_all



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

                      _A        =  np.dot(lowerCAmelCase_		,				self.vji.T							) - self.thre_bpa
                      _A        =  self.sig(lowerCAmelCase_							)
                      _A        =  np.dot(lowerCAmelCase_		,				self.wkj.T							) - self.thre_bpa
                      _A        =  self.sig(lowerCAmelCase_							)

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

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

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

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

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

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



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

        return data_conveda, data_pooleda


if __name__ == "__main__":
    pass





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE										=						{
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}



class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Any    			=		'''resnet'''
 lowerCamelCase	:Any    			=		['''basic''', '''bottleneck''']



 def __init__(					self		,				lowerCAmelCase_=3		,				lowerCAmelCase_=64		,				lowerCAmelCase_=[2_56, 5_12, 10_24, 20_48]		,				lowerCAmelCase_=[3, 4, 6, 3]		,				lowerCAmelCase_="bottleneck"		,				lowerCAmelCase_="relu"		,				lowerCAmelCase_=False		,				lowerCAmelCase_=None		,				lowerCAmelCase_=None		,				**lowerCAmelCase_		,				)  ->					Union[str, Any]:
        super().__init__(**lowerCAmelCase_							)
        if layer_type not in self.layer_types:
               raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types							)}'''							)
        _A        =  num_channels
        _A        =  embedding_size
        _A        =  hidden_sizes
        _A        =  depths
        _A        =  layer_type
        _A        =  hidden_act
        _A        =  downsample_in_first_stage
        _A        =  ["""stem"""] + [F'''stage{idx}''' for idx in range(1		,				len(lowerCAmelCase_							) + 1							)]
        _A   ,  _A        =  get_aligned_output_features_output_indices(
            out_features=lowerCAmelCase_		,				out_indices=lowerCAmelCase_		,				stage_names=self.stage_names							)



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Optional[Any]    			=		version.parse('''1.11'''       )



 @property
 def      UpperCAmelCase							(					self							)  ->					Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ]							)



 @property
 def      UpperCAmelCase							(					self							)  ->					float:
        return 1E-3




