
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


lowerCamelCase_    :			str    =	{"tokenization_herbert": ["HerbertTokenizer"]}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Dict    =	["HerbertTokenizerFast"]


if TYPE_CHECKING:
							from .tokenization_herbert import HerbertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_herbert_fast import HerbertTokenizerFast

else:
							import sys

							lowerCamelCase_    :			Tuple    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt






class   UpperCAmelCase      :
 def __init__(							self :Dict		,			lowercase_ :Union[str, Any]		,			lowercase_ :Tuple		,			lowercase_ :List[Any]		,			lowercase_ :List[str]		,			lowercase_ :Dict		,			lowercase_ :Tuple=0.2		,			lowercase_ :List[str]=0.2				)->				Any:
     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 :Optional[Any]		,			lowercase_ :Optional[Any]				)->				Optional[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(lowercase_		,			"wb"				) as f:
         pickle.dump(lowercase_		,			lowercase_				)

     print(F"Model saved： {save_path}"				)
 @classmethod
 def 	UpperCAmelCase_				(							cls :Any		,			lowercase_ :List[Any]				)->				Optional[Any]:
     # read saved model
     with open(lowercase_		,			"rb"				) as f:
         A__							=     pickle.load(lowercase_				)  # 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(lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_				)
     # 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 :Dict		,			lowercase_ :Optional[int]				)->				Optional[Any]:
     return 1 / (1 + np.exp(-1 * x				))
 def 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :Optional[Any]				)->				Any:
     return round(lowercase_		,			3				)
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Tuple		,			lowercase_ :List[Any]		,			lowercase_ :Tuple		,			lowercase_ :Union[str, Any]		,			lowercase_ :Dict				)->				str:
     # convolution process
     A__							=     convs[0]
     A__							=     convs[1]
     A__							=     np.shape(lowercase_				)[0]
     # get the data slice of original image data, data_focus
     A__							=     []
     for i_focus in range(0		,			size_data - size_conv + 1		,			lowercase_				):
         for j_focus in range(0		,			size_data - size_conv + 1		,			lowercase_				):
             A__							=     data[
                 i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
             ]
             data_focus.append(lowercase_				)
        # 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(lowercase_				):
         A__							=     []
         for i_focus in range(len(lowercase_				)				):
             A__							=     (
                 np.sum(np.multiply(data_focus[i_focus]		,			w_convs[i_map]				)				)
                 - thre_convs[i_map]
             )
             featuremap.append(self.sig(lowercase_				)				)
         A__							=     np.asmatrix(lowercase_				).reshape(
             lowercase_		,			lowercase_				)
         data_featuremap.append(lowercase_				)

     # expanding the data slice to One dimenssion
     A__							=     []
     for each_focus in data_focus:
         focusa_list.extend(self.Expand_Mat(lowercase_				)				)
     A__							=     np.asarray(lowercase_				)
     return focus_list, data_featuremap
 def 	UpperCAmelCase_				(							self :Optional[int]		,			lowercase_ :Optional[int]		,			lowercase_ :Union[str, Any]		,			lowercase_ :Optional[Any]="average_pool"				)->				Dict:
     # pooling process
     A__							=     len(featuremaps[0]				)
     A__							=     int(size_map / size_pooling				)
     A__							=     []
     for i_map in range(len(lowercase_				)				):
         A__							=     featuremaps[i_map]
         A__							=     []
         for i_focus in range(0		,			lowercase_		,			lowercase_				):
             for j_focus in range(0		,			lowercase_		,			lowercase_				):
                 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(lowercase_				)				)
                 elif pooling_type == "max_pooling":
                     # max pooling
                     map_pooled.append(np.max(lowercase_				)				)
         A__							=     np.asmatrix(lowercase_				).reshape(lowercase_		,			lowercase_				)
         featuremap_pooled.append(lowercase_				)
     return featuremap_pooled
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :Any				)->				List[Any]:
     # expanding three dimension data to one dimension list
     A__							=     []
     for i in range(len(lowercase_				)				):
         A__							=     np.shape(data[i]				)
         A__							=     data[i].reshape(1		,			shapes[0] * shapes[1]				)
         A__							=     data_listed.getA().tolist()[0]
         data_expanded.extend(lowercase_				)
     A__							=     np.asarray(lowercase_				)
     return data_expanded
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Optional[Any]				)->				Dict:
     # expanding matrix to one dimension list
     A__							=     np.asarray(lowercase_				)
     A__							=     np.shape(lowercase_				)
     A__							=     data_mat.reshape(1		,			shapes[0] * shapes[1]				)
     return data_expanded
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :List[str]		,			lowercase_ :Any		,			lowercase_ :str		,			lowercase_ :Any		,			lowercase_ :Union[str, Any]				)->				Any:
     A__							=     []
     A__							=     0
     for i_map in range(lowercase_				):
         A__							=     np.ones((size_map, size_map)				)
         for i in range(0		,			lowercase_		,			lowercase_				):
             for j in range(0		,			lowercase_		,			lowercase_				):
                 A__							=     pd_pool[
                     i_pool
                 ]
                 A__							=     i_pool + 1
         A__							=     np.multiply(
             lowercase_		,			np.multiply(out_map[i_map]		,			(1 - out_map[i_map])				)				)
         pd_all.append(lowercase_				)
     return pd_all
 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :Optional[Any]		,			lowercase_ :Optional[Any]		,			lowercase_ :Union[str, Any]		,			lowercase_ :List[Any]		,			lowercase_ :Union[str, Any]		,			lowercase_ :str=bool				)->				Tuple:
     # model traning
     print("----------------------Start Training-------------------------"				)
     print((" - - Shape: Train_Data  ", np.shape(lowercase_				))				)
     print((" - - Shape: Teach_Data  ", np.shape(lowercase_				))				)
     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(lowercase_				)				):
             # print('------------Learning Image: %d--------------'%p)
             A__							=     np.asmatrix(datas_train[p]				)
             A__							=     np.asarray(datas_teach[p]				)
             A__,      A__							=     self.convolute(
                 lowercase_		,			self.conva		,			self.w_conva		,			self.thre_conva		,			conv_step=self.step_conva		,			)
             A__							=     self.pooling(lowercase_		,			self.size_poolinga				)
             A__							=     np.shape(lowercase_				)
             A__							=     self._expand(lowercase_				)
             A__							=     data_bp_input

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

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

                 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(lowercase_				)

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

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



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

     return data_conveda, data_pooleda


if __name__ == "__main__":
  pass



