'''simple docstring'''


import pickle

import numpy as np
from matplotlib import pyplot as plt





class       _a    :


  """simple docstring"""

  def __init__(		self      :       Union[str, Any]   ,				lowercase_      :       Tuple   ,				lowercase_      :       Optional[Any]   ,				lowercase_      :       int   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       int   ,				lowercase_      :       Any=0.2   ,				lowercase_      :       Dict=0.2      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				lowercase_      :       Any      ):







    '''simple docstring'''


    lowercase_     =       {
        """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 		lowerCamelCase__  (		cls      :       str   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


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

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




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       Tuple      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       Tuple   ,				lowercase_      :       int      ):







    '''simple docstring'''


    return round(lowercase_   ,				3      )




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       Tuple   ,				lowercase_      :       Optional[Any]   ,				lowercase_      :       Dict   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


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

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




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				lowercase_      :       List[Any]   ,				lowercase_      :       Optional[int]   ,				lowercase_      :       Any="average_pool"      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       List[str]   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       str   ,				lowercase_      :       int   ,				lowercase_      :       Any   ,				lowercase_      :       Optional[Any]   ,				lowercase_      :       Optional[Any]      ):







    '''simple docstring'''


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




  def 		lowerCamelCase__  (		self      :       Dict   ,				lowercase_      :       int   ,				lowercase_      :       Union[str, Any]   ,				lowercase_      :       List[str]   ,				lowercase_      :       Optional[Any]   ,				lowercase_      :       str   ,				lowercase_      :       Union[str, Any]=bool      ):







    '''simple docstring'''


    print("""----------------------Start Training-------------------------"""      )
    print((""" - - Shape: Train_Data  """, np.shape(lowercase_      ))      )
    print((""" - - Shape: Teach_Data  """, np.shape(lowercase_      ))      )
    lowercase_     =       0
    lowercase_     =       []
    lowercase_     =       10_000
    while rp < n_repeat and mse >= error_accuracy:
      lowercase_     =       0
      print(F"""-------------Learning Time {rp}--------------"""      )
      for p in range(len(lowercase_      )      ):
        # print('------------Learning Image: %d--------------'%p)
        lowercase_     =       np.asmatrix(datas_train[p]      )
        lowercase_     =       np.asarray(datas_teach[p]      )
        lowercase_     ,	lowercase_     =       self.convolute(
            lowercase_   ,				self.conva   ,				self.w_conva   ,				self.thre_conva   ,				conv_step=self.step_conva   ,				)
        lowercase_     =       self.pooling(lowercase_   ,				self.size_poolinga      )
        lowercase_     =       np.shape(lowercase_      )
        lowercase_     =       self._expand(lowercase_      )
        lowercase_     =       data_bp_input

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

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

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

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

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

    def draw_error():
      lowercase_     =       [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 		lowerCamelCase__  (		self      :       int   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


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

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





  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       Union[str, Any]      ):







    '''simple docstring'''


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

    return data_conveda, data_pooleda


if __name__ == "__main__":
       pass


'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''camembert'''




  def __init__(		self      :       Union[str, Any]   ,				lowercase_      :       Union[str, Any]=30_522   ,				lowercase_      :       Optional[Any]=768   ,				lowercase_      :       Tuple=12   ,				lowercase_      :       Dict=12   ,				lowercase_      :       Tuple=3_072   ,				lowercase_      :       Optional[Any]="gelu"   ,				lowercase_      :       Optional[int]=0.1   ,				lowercase_      :       Optional[int]=0.1   ,				lowercase_      :       List[Any]=512   ,				lowercase_      :       Optional[int]=2   ,				lowercase_      :       str=0.0_2   ,				lowercase_      :       int=1e-12   ,				lowercase_      :       str=1   ,				lowercase_      :       List[str]=0   ,				lowercase_      :       int=2   ,				lowercase_      :       Union[str, Any]="absolute"   ,				lowercase_      :       Optional[Any]=True   ,				lowercase_      :       List[Any]=None   ,				**lowercase_      :       int   ,				):







    '''simple docstring'''


    super().__init__(pad_token_id=lowercase_   ,				bos_token_id=lowercase_   ,				eos_token_id=lowercase_   ,				**lowercase_      )

    lowercase_     =       vocab_size
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       hidden_act
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       max_position_embeddings
    lowercase_     =       type_vocab_size
    lowercase_     =       initializer_range
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       use_cache
    lowercase_     =       classifier_dropout






class       _a    (		__a      ):


  """simple docstring"""

  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    if self.task == "multiple-choice":
      lowercase_     =       {0: """batch""", 1: """choice""", 2: """sequence"""}
    else:
      lowercase_     =       {0: """batch""", 1: """sequence"""}
    return OrderedDict(
        [
            ("""input_ids""", dynamic_axis),
            ("""attention_mask""", dynamic_axis),
        ]      )

