


import pickle

import numpy as np
from matplotlib import pyplot as plt



class A__     :



  """simple docstring"""



  def __init__( self			:  str				,				lowerCamelCase__			:  Optional[int]				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  Any				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  int=0.2				,				lowerCamelCase__			:  Dict=0.2       ):
        a__	:  Optional[int]     =   bp_numa
        a__	:  Tuple     =   bp_numa
        a__	:  Optional[Any]     =   bp_numa
        a__	:  str     =   conva_get[:2]
        a__	:  Optional[int]     =   conva_get[2]
        a__	:  Any     =   size_pa
        a__	:  List[str]     =   rate_w
        a__	:  str     =   rate_t
        a__	:  Optional[int]     =   [
            np.mat(-1 * np.random.rand(self.conva[0]				,				self.conva[0]       ) + 0.5       )
            for i in range(self.conva[1]       )
        ]
        a__	:  Optional[int]     =   np.mat(-1 * np.random.rand(self.num_bpa				,				self.num_bpa       ) + 0.5       )
        a__	:  Any     =   np.mat(-1 * np.random.rand(self.num_bpa				,				self.num_bpa       ) + 0.5       )
        a__	:  List[str]     =   -2 * np.random.rand(self.conva[1]       ) + 1
        a__	:  List[str]     =   -2 * np.random.rand(self.num_bpa       ) + 1
        a__	:  List[Any]     =   -2 * np.random.rand(self.num_bpa       ) + 1

  def    _UpperCamelCase( self			:  List[Any]				,				lowerCamelCase__			:  Optional[Any]       ):
        # save model dict with pickle
        a__	:  int     =   {
            "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			:  Tuple				,				lowerCamelCase__			:  Tuple       ):
        # read saved model
        with open(lowerCamelCase__				,				"rb"       ) as f:
              a__	:  Tuple     =   pickle.load(lowerCamelCase__       )  # noqa: S301

        a__	:  List[str]     =   model_dic.get("conv1"       )
        conv_get.append(model_dic.get("step_conv1"       )       )
        a__	:  Optional[Any]     =   model_dic.get("size_pooling1"       )
        a__	:  Optional[Any]     =   model_dic.get("num_bp1"       )
        a__	:  Tuple     =   model_dic.get("num_bp2"       )
        a__	:  int     =   model_dic.get("num_bp3"       )
        a__	:  Tuple     =   model_dic.get("rate_weight"       )
        a__	:  Optional[Any]     =   model_dic.get("rate_thre"       )
        # create model instance
        a__	:  Tuple     =   CNN(lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       )
        # modify model parameter
        a__	:  Tuple     =   model_dic.get("w_conv1"       )
        a__	:  int     =   model_dic.get("wkj"       )
        a__	:  List[str]     =   model_dic.get("vji"       )
        a__	:  Optional[int]     =   model_dic.get("thre_conv1"       )
        a__	:  Optional[int]     =   model_dic.get("thre_bp2"       )
        a__	:  Optional[int]     =   model_dic.get("thre_bp3"       )
        return conv_ins

  def    _UpperCamelCase( self			:  Any				,				lowerCamelCase__			:  List[str]       ):
        return 1 / (1 + np.exp(-1 * x       ))

  def    _UpperCamelCase( self			:  Any				,				lowerCamelCase__			:  List[str]       ):
        return round(lowerCamelCase__				,				3       )

  def    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  Tuple				,				lowerCamelCase__			:  Union[str, Any]				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  Any       ):
        # convolution process
        a__	:  Any     =   convs[0]
        a__	:  str     =   convs[1]
        a__	:  int     =   np.shape(lowerCamelCase__       )[0]
        # get the data slice of original image data, data_focus
        a__	:  Optional[int]     =   []
        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__	:  List[str]     =   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__	:  str     =   []
        a__	:  Dict     =   int((size_data - size_conv) / conv_step + 1       )
        for i_map in range(lowerCamelCase__       ):
              a__	:  Optional[Any]     =   []
              for i_focus in range(len(lowerCamelCase__       )       ):
                    a__	:  Tuple     =   (
                        np.sum(np.multiply(data_focus[i_focus]				,				w_convs[i_map]       )       )
                        - thre_convs[i_map]
                    )
                    featuremap.append(self.sig(lowerCamelCase__       )       )
              a__	:  Optional[int]     =   np.asmatrix(lowerCamelCase__       ).reshape(
                  lowerCamelCase__				,				lowerCamelCase__       )
              data_featuremap.append(lowerCamelCase__       )

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

  def    _UpperCamelCase( self			:  List[Any]				,				lowerCamelCase__			:  List[str]				,				lowerCamelCase__			:  Optional[int]				,				lowerCamelCase__			:  Optional[int]="average_pool"       ):
        # pooling process
        a__	:  Union[str, Any]     =   len(featuremaps[0]       )
        a__	:  Union[str, Any]     =   int(size_map / size_pooling       )
        a__	:  List[str]     =   []
        for i_map in range(len(lowerCamelCase__       )       ):
              a__	:  str     =   featuremaps[i_map]
              a__	:  Tuple     =   []
              for i_focus in range(0				,				lowerCamelCase__				,				lowerCamelCase__       ):
                    for j_focus in range(0				,				lowerCamelCase__				,				lowerCamelCase__       ):
                          a__	:  Any     =   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__	:  List[str]     =   np.asmatrix(lowerCamelCase__       ).reshape(lowerCamelCase__				,				lowerCamelCase__       )
              featuremap_pooled.append(lowerCamelCase__       )
        return featuremap_pooled

  def    _UpperCamelCase( self			:  Any				,				lowerCamelCase__			:  str       ):
        # expanding three dimension data to one dimension list
        a__	:  Optional[int]     =   []
        for i in range(len(lowerCamelCase__       )       ):
              a__	:  Dict     =   np.shape(data[i]       )
              a__	:  Union[str, Any]     =   data[i].reshape(1				,				shapes[0] * shapes[1]       )
              a__	:  List[Any]     =   data_listed.getA().tolist()[0]
              data_expanded.extend(lowerCamelCase__       )
        a__	:  Dict     =   np.asarray(lowerCamelCase__       )
        return data_expanded

  def    _UpperCamelCase( self			:  List[str]				,				lowerCamelCase__			:  Tuple       ):
        # expanding matrix to one dimension list
        a__	:  str     =   np.asarray(lowerCamelCase__       )
        a__	:  Optional[int]     =   np.shape(lowerCamelCase__       )
        a__	:  Union[str, Any]     =   data_mat.reshape(1				,				shapes[0] * shapes[1]       )
        return data_expanded

  def    _UpperCamelCase( self			:  Any				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  Any       ):
        a__	:  int     =   []
        a__	:  int     =   0
        for i_map in range(lowerCamelCase__       ):
              a__	:  List[Any]     =   np.ones((size_map, size_map)       )
              for i in range(0				,				lowerCamelCase__				,				lowerCamelCase__       ):
                    for j in range(0				,				lowerCamelCase__				,				lowerCamelCase__       ):
                          a__	:  Union[str, Any]     =   pd_pool[
                              i_pool
                          ]
                          a__	:  str     =   i_pool + 1
              a__	:  Any     =   np.multiply(
                  lowerCamelCase__				,				np.multiply(out_map[i_map]				,				(1 - out_map[i_map])       )       )
              pd_all.append(lowerCamelCase__       )
        return pd_all

  def    _UpperCamelCase( self			:  Union[str, Any]				,				lowerCamelCase__			:  List[str]				,				lowerCamelCase__			:  Optional[Any]				,				lowerCamelCase__			:  List[Any]				,				lowerCamelCase__			:  Tuple				,				lowerCamelCase__			:  Optional[Any]				,				lowerCamelCase__			:  List[str]=bool       ):
        # model traning
        print("----------------------Start Training-------------------------"       )
        print((" - - Shape: Train_Data  ", np.shape(lowerCamelCase__       ))       )
        print((" - - Shape: Teach_Data  ", np.shape(lowerCamelCase__       ))       )
        a__	:  str     =   0
        a__	:  List[str]     =   []
        a__	:  int     =   10_000
        while rp < n_repeat and mse >= error_accuracy:
              a__	:  Optional[int]     =   0
              print(f'''-------------Learning Time {rp}--------------'''       )
              for p in range(len(lowerCamelCase__       )       ):
                    # print('------------Learning Image: %d--------------'%p)
                    a__	:  Optional[Any]     =   np.asmatrix(datas_train[p]       )
                    a__	:  str     =   np.asarray(datas_teach[p]       )
                    a__,	a__	:  Dict     =   self.convolute(
                        lowerCamelCase__				,				self.conva				,				self.w_conva				,				self.thre_conva				,				conv_step=self.step_conva				,				)
                    a__	:  int     =   self.pooling(lowerCamelCase__				,				self.size_poolinga       )
                    a__	:  Dict     =   np.shape(lowerCamelCase__       )
                    a__	:  List[str]     =   self._expand(lowerCamelCase__       )
                    a__	:  Dict     =   data_bp_input

                    a__	:  Dict     =   np.dot(lowerCamelCase__				,				self.vji.T       ) - self.thre_bpa
                    a__	:  Optional[int]     =   self.sig(lowerCamelCase__       )
                    a__	:  int     =   np.dot(lowerCamelCase__				,				self.wkj.T       ) - self.thre_bpa
                    a__	:  int     =   self.sig(lowerCamelCase__       )

                    # --------------Model Leaning ------------------------
                    # calculate error and gradient---------------
                    a__	:  Tuple     =   np.multiply(
                        (data_teach - bp_outa)				,				np.multiply(lowerCamelCase__				,				(1 - bp_outa)       )       )
                    a__	:  Optional[Any]     =   np.multiply(
                        np.dot(lowerCamelCase__				,				self.wkj       )				,				np.multiply(lowerCamelCase__				,				(1 - bp_outa)       )       )
                    a__	:  Optional[Any]     =   np.dot(lowerCamelCase__				,				self.vji       )

                    a__	:  str     =   pd_i_all / (self.size_poolinga * self.size_poolinga)
                    a__	:  Optional[Any]     =   pd_conva_pooled.T.getA().tolist()
                    a__	:  Dict     =   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__	:  List[str]     =   self._expand_mat(pd_conva_all[k_conv]       )
                          a__	:  Any     =   self.rate_weight * np.dot(lowerCamelCase__				,				lowerCamelCase__       )

                          a__	:  List[Any]     =   self.w_conva[k_conv] + delta_w.reshape(
                              (self.conva[0], self.conva[0])       )

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

        def draw_error():
              a__	:  Optional[int]     =   [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			:  Tuple				,				lowerCamelCase__			:  int       ):
        # model predict
        a__	:  Optional[int]     =   []
        print("-------------------Start Testing-------------------------"       )
        print((" - - Shape: Test_Data  ", np.shape(lowerCamelCase__       ))       )
        for p in range(len(lowerCamelCase__       )       ):
              a__	:  str     =   np.asmatrix(datas_test[p]       )
              a__,	a__	:  Tuple     =   self.convolute(
                  lowerCamelCase__				,				self.conva				,				self.w_conva				,				self.thre_conva				,				conv_step=self.step_conva				,				)
              a__	:  Optional[int]     =   self.pooling(lowerCamelCase__				,				self.size_poolinga       )
              a__	:  Any     =   self._expand(lowerCamelCase__       )

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

  def    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  Any       ):
        # return the data of image after convoluting process so we can check it out
        a__	:  List[Any]     =   np.asmatrix(lowerCamelCase__       )
        a__,	a__	:  List[Any]     =   self.convolute(
            lowerCamelCase__				,				self.conva				,				self.w_conva				,				self.thre_conva				,				conv_step=self.step_conva				,				)
        a__	:  Any     =   self.pooling(lowerCamelCase__				,				self.size_poolinga       )

        return data_conveda, data_pooleda


if __name__ == "__main__":
    pass



"""simple docstring"""


from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
						from .features import FeatureType




@dataclass
class  __lowerCamelCase  :
				a__:      List[str]
				a__:      Optional[str]          =    None
				# Automatically constructed
				a__:      ClassVar[str]          =    "dict"
				a__:      ClassVar[Any]          =    None
				a__:      str          =    field(default='Translation'						,	init=lowerCAmelCase						,	repr=lowerCAmelCase			)




				def __call__(				self   ):
							return pa.struct({lang: pa.string() for lang in sorted(self.languages   )}   )





				def        UpperCAmelCase__						(				self   ):
							from .features import Value

							return {k: Value('''string'''   ) for k in sorted(self.languages   )}




@dataclass
class  __lowerCamelCase  :
				a__:      Optional[List]          =    None
				a__:      Optional[int]          =    None
				a__:      Optional[str]          =    None
				# Automatically constructed
				a__:      ClassVar[str]          =    "dict"
				a__:      ClassVar[Any]          =    None
				a__:      str          =    field(default='TranslationVariableLanguages'						,	init=lowerCAmelCase						,	repr=lowerCAmelCase			)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  sorted(set(self.languages   )   ) if self.languages else None
							lowerCamelCase_					=  len(self.languages   ) if self.languages else None




				def __call__(				self   ):
							return pa.struct({'''language''': pa.list_(pa.string()   ), '''translation''': pa.list_(pa.string()   )}   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  set(self.languages   )
							if self.languages and set(UpperCAmelCase   ) - lang_set:
										raise ValueError(
										    f"Some languages in example ({', '.join(sorted(set(UpperCAmelCase   ) - lang_set   )   )}) are not in valid set ({', '.join(UpperCAmelCase   )})."   )

							# Convert dictionary into tuples, splitting out cases where there are
							# multiple translations for a single language.
							lowerCamelCase_					=  []
							for lang, text in translation_dict.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													translation_tuples.append((lang, text)   )
										else:
													translation_tuples.extend([(lang, el) for el in text]   )

        # Ensure translations are in ascending order by language code.
							lowerCamelCase_		, lowerCamelCase_					=  zip(*sorted(UpperCAmelCase   )   )

							return {"language": languages, "translation": translations}





				def        UpperCAmelCase__						(				self   ):
							from .features import Sequence, Value

							return {
							    "language": Sequence(Value('''string'''   )   ),
							    "translation": Sequence(Value('''string'''   )   ),
							}



