






'''simple docstring'''





import pickle

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


    '''simple docstring'''

    def __init__(					self     :     Tuple				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     List[Any]=0.2				,     __lowerCamelCase     :     List[str]=0.2     )							->							Any:

     '''simple docstring'''




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

    def    a__				(					self     :     str				,     __lowerCamelCase     :     Dict     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     {
         "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    a__				(					cls     :     int				,     __lowerCamelCase     :     Any     )							->							str:

     '''simple docstring'''




     with open(__lowerCamelCase				,     "rb"     ) as f:
      lowerCamelCase__  =     pickle.load(__lowerCamelCase     )  # noqa: S301

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

    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     int     )							->							Any:

     '''simple docstring'''




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

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Optional[int]     )							->							Optional[int]:

     '''simple docstring'''




     return round(__lowerCamelCase				,     3     )

    def    a__				(					self     :     str				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     Optional[int]     )							->							List[str]:

     '''simple docstring'''




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

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

    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Dict				,     __lowerCamelCase     :     Optional[Any]="average_pool"     )							->							Optional[Any]:

     '''simple docstring'''




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

    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     Any     )							->							Dict:

     '''simple docstring'''




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

    def    a__				(					self     :     int				,     __lowerCamelCase     :     List[Any]     )							->							Dict:

     '''simple docstring'''




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

    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     List[Any]     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     []
     lowerCamelCase__  =     0
     for i_map in range(__lowerCamelCase     ):
      lowerCamelCase__  =     np.ones((size_map, size_map)     )
      for i in range(0				,     __lowerCamelCase				,     __lowerCamelCase     ):
       for j in range(0				,     __lowerCamelCase				,     __lowerCamelCase     ):
        lowerCamelCase__  =     pd_pool[
            i_pool
        ]
        lowerCamelCase__  =     i_pool + 1
      lowerCamelCase__  =     np.multiply(
          __lowerCamelCase				,     np.multiply(out_map[i_map]				,     (1 - out_map[i_map])     )     )
      pd_all.append(__lowerCamelCase     )
     return pd_all

    def    a__				(					self     :     int				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     List[Any]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     List[Any]=bool     )							->							Optional[int]:

     '''simple docstring'''




     print("----------------------Start Training-------------------------"     )
     print((" - - Shape: Train_Data  ", np.shape(__lowerCamelCase     ))     )
     print((" - - Shape: Teach_Data  ", np.shape(__lowerCamelCase     ))     )
     lowerCamelCase__  =     0
     lowerCamelCase__  =     []
     lowerCamelCase__  =     10000
     while rp < n_repeat and mse >= error_accuracy:
      lowerCamelCase__  =     0
      print(f'''-------------Learning Time {rp}--------------'''     )
      for p in range(len(__lowerCamelCase     )     ):
       # print('------------Learning Image: %d--------------'%p)
       lowerCamelCase__  =     np.asmatrix(datas_train[p]     )
       lowerCamelCase__  =     np.asarray(datas_teach[p]     )
       lowerCamelCase__  ,	lowerCamelCase__  =     self.convolute(
           __lowerCamelCase				,     self.conva				,     self.w_conva				,     self.thre_conva				,     conv_step=self.step_conva				,     )
       lowerCamelCase__  =     self.pooling(__lowerCamelCase				,     self.size_poolinga     )
       lowerCamelCase__  =     np.shape(__lowerCamelCase     )
       lowerCamelCase__  =     self._expand(__lowerCamelCase     )
       lowerCamelCase__  =     data_bp_input

       lowerCamelCase__  =     np.dot(__lowerCamelCase				,     self.vji.T     ) - self.thre_bpa
       lowerCamelCase__  =     self.sig(__lowerCamelCase     )
       lowerCamelCase__  =     np.dot(__lowerCamelCase				,     self.wkj.T     ) - self.thre_bpa
       lowerCamelCase__  =     self.sig(__lowerCamelCase     )

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

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

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

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

     def draw_error():
      lowerCamelCase__  =     [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    a__				(					self     :     List[str]				,     __lowerCamelCase     :     Dict     )							->							int:

     '''simple docstring'''




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

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




    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Tuple     )							->							List[str]:

     '''simple docstring'''




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

     return data_conveda, data_pooleda


if __name__ == "__main__":
 pass









'''simple docstring'''





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


__A							:							Optional[int]  							=		logging.get_logger(__name__)

__A							:							Tuple  							=		{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"mobilenet_v1"

    def __init__(					self     :     Union[str, Any]				,     __lowerCamelCase     :     Tuple=3				,     __lowerCamelCase     :     Any=224				,     __lowerCamelCase     :     Tuple=1.0				,     __lowerCamelCase     :     Optional[int]=8				,     __lowerCamelCase     :     str="relu6"				,     __lowerCamelCase     :     Any=True				,     __lowerCamelCase     :     Union[str, Any]=0.9_9_9				,     __lowerCamelCase     :     List[Any]=0.0_2				,     __lowerCamelCase     :     str=0.0_0_1				,     **__lowerCamelCase     :     str				,     )							->							Dict:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase     )

     if depth_multiplier <= 0:
      raise ValueError("depth_multiplier must be greater than zero."     )

     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     image_size
     lowerCamelCase__  =     depth_multiplier
     lowerCamelCase__  =     min_depth
     lowerCamelCase__  =     hidden_act
     lowerCamelCase__  =     tf_padding
     lowerCamelCase__  =     classifier_dropout_prob
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     layer_norm_eps
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							version.parse("1.11"      )

    @property
    def    a__				(					self     :     List[str]     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     return OrderedDict([("pixel_values", {0: "batch"})]     )

    @property
    def    a__				(					self     :     str     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     if self.task == "image-classification":
      return OrderedDict([("logits", {0: "batch"})]     )
     else:
      return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]     )




    @property
    def    a__				(					self     :     List[Any]     )							->							float:

     '''simple docstring'''




     return 1E-4

