'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__a: Optional[int]          =   {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a: Dict          =   ['''PLBartTokenizer''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a: int          =   [
      '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''PLBartForCausalLM''',
      '''PLBartForConditionalGeneration''',
      '''PLBartForSequenceClassification''',
      '''PLBartModel''',
      '''PLBartPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_plbart import PLBartTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_plbart import (
        PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
        PLBartForCausalLM,
        PLBartForConditionalGeneration,
        PLBartForSequenceClassification,
        PLBartModel,
        PLBartPreTrainedModel,
    )


else:
  import sys

  __a: List[Any]          =   _LazyModule(__name__, globals()["""__file__"""], _import_structure)






'''simple docstring'''




import pickle

import numpy as np
from matplotlib import pyplot as plt







class 	_lowerCAmelCase							:


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


	def     _a	(self       ,      lowercase							):
				# save model dict with pickle
				A_	:						Union[str, Any]	=     {
				    """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     _a	(cls       ,      lowercase							):
				# read saved model
				with open(lowercase       ,      """rb"""							) as f:
							A_	:						Optional[int]	=     pickle.load(lowercase							)  # noqa: S301

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


	def     _a	(self       ,      lowercase							):
				return 1 / (1 + np.exp(-1 * x							))


	def     _a	(self       ,      lowercase							):
				return round(lowercase       ,      3							)


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

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


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


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


	def     _a	(self       ,      lowercase							):
				# expanding matrix to one dimension list
				A_	:						str	=     np.asarray(lowercase							)
				A_	:						Any	=     np.shape(lowercase							)
				A_	:						int	=     data_mat.reshape(1       ,      shapes[0] * shapes[1]							)
				return data_expanded


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


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase=bool							):
				# model traning
				print("""----------------------Start Training-------------------------"""							)
				print((""" - - Shape: Train_Data  """, np.shape(lowercase							))							)
				print((""" - - Shape: Teach_Data  """, np.shape(lowercase							))							)
				A_	:						Optional[Any]	=     0
				A_	:						Dict	=     []
				A_	:						List[Any]	=     10000
				while rp < n_repeat and mse >= error_accuracy:
							A_	:						List[Any]	=     0
							print(F'-------------Learning Time {rp}--------------'							)
							for p in range(len(lowercase							)							):
										# 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(
										    lowercase       ,      self.conva       ,      self.w_conva       ,      self.thre_conva       ,      conv_step=self.step_conva       ,      )
										A_	:						Any	=     self.pooling(lowercase       ,      self.size_poolinga							)
										A_	:						int	=     np.shape(lowercase							)
										A_	:						Union[str, Any]	=     self._expand(lowercase							)
										A_	:						Dict	=     data_bp_input

										A_	:						int	=     np.dot(lowercase       ,      self.vji.T							) - self.thre_bpa
										A_	:						Any	=     self.sig(lowercase							)
										A_	:						Optional[int]	=     np.dot(lowercase       ,      self.wkj.T							) - self.thre_bpa
										A_	:						List[str]	=     self.sig(lowercase							)

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

										A_	:						int	=     pd_i_all / (self.size_poolinga * self.size_poolinga)
										A_	:						str	=     pd_conva_pooled.T.getA().tolist()
										A_	:						List[Any]	=     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_	:						int	=     self._expand_mat(pd_conva_all[k_conv]							)
													A_	:						Any	=     self.rate_weight * np.dot(lowercase       ,      lowercase							)

													A_	:						Union[str, Any]	=     self.w_conva[k_conv] + delta_w.reshape(
													    (self.conva[0], self.conva[0])							)

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

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

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


	def     _a	(self       ,      lowercase							):
				# return the data of image after convoluting process so we can check it out
				A_	:						Optional[Any]	=     np.asmatrix(lowercase							)
				A_,    A_	:						Optional[Any]	=     self.convolute(
				    lowercase       ,      self.conva       ,      self.w_conva       ,      self.thre_conva       ,      conv_step=self.step_conva       ,      )
				A_	:						Union[str, Any]	=     self.pooling(lowercase       ,      self.size_poolinga							)

				return data_conveda, data_pooleda


if __name__ == "__main__":
						pass