

from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__  :      List[str]			=    logging.get_logger(__name__)

UpperCamelCase__  :      Dict			=    {
    "huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	int						 =  'autoformer'
  __lowerCAmelCase     :	Any						 =  {
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
      'num_hidden_layers': 'encoder_layers',
  }

  def __init__(					self						,		_A = None						,		_A = None						,		_A = "student_t"						,		_A = "nll"						,		_A = 1						,		_A = [1, 2, 3, 4, 5, 6, 7]						,		_A = True						,		_A = 0						,		_A = 0						,		_A = 0						,		_A = 0						,		_A = None						,		_A = None						,		_A = 64						,		_A = 2						,		_A = 2						,		_A = 2						,		_A = 2						,		_A = 32						,		_A = 32						,		_A = "gelu"						,		_A = 0.1						,		_A = 0.1						,		_A = 0.1						,		_A = 0.1						,		_A = 0.1						,		_A = 100						,		_A = 0.0_2						,		_A = True						,		_A=True						,		_A = 10						,		_A = 25						,		_A = 3						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						prediction_length
      SCREAMING_SNAKE_CASE_									=						context_length if context_length is not None else prediction_length
      SCREAMING_SNAKE_CASE_									=						distribution_output
      SCREAMING_SNAKE_CASE_									=						loss
      SCREAMING_SNAKE_CASE_									=						input_size
      SCREAMING_SNAKE_CASE_									=						num_time_features
      SCREAMING_SNAKE_CASE_									=						lags_sequence
      SCREAMING_SNAKE_CASE_									=						scaling
      SCREAMING_SNAKE_CASE_									=						num_dynamic_real_features
      SCREAMING_SNAKE_CASE_									=						num_static_real_features
      SCREAMING_SNAKE_CASE_									=						num_static_categorical_features
      if cardinality is not None and num_static_categorical_features > 0:
          if len(_A) != num_static_categorical_features:
              raise ValueError(
                  'The cardinality should be a list of the same length as `num_static_categorical_features`')
          SCREAMING_SNAKE_CASE_									=						cardinality
      else:
          SCREAMING_SNAKE_CASE_									=						[0]
      if embedding_dimension is not None and num_static_categorical_features > 0:
          if len(_A) != num_static_categorical_features:
              raise ValueError(
                  'The embedding dimension should be a list of the same length as `num_static_categorical_features`')
          SCREAMING_SNAKE_CASE_									=						embedding_dimension
      else:
          SCREAMING_SNAKE_CASE_									=						[min(50						,		(cat + 1) // 2) for cat in self.cardinality]
      SCREAMING_SNAKE_CASE_									=						num_parallel_samples

      # Transformer architecture configuration
      SCREAMING_SNAKE_CASE_									=						input_size * len(self.lags_sequence) + self._number_of_features
      SCREAMING_SNAKE_CASE_									=						d_model
      SCREAMING_SNAKE_CASE_									=						encoder_attention_heads
      SCREAMING_SNAKE_CASE_									=						decoder_attention_heads
      SCREAMING_SNAKE_CASE_									=						encoder_ffn_dim
      SCREAMING_SNAKE_CASE_									=						decoder_ffn_dim
      SCREAMING_SNAKE_CASE_									=						encoder_layers
      SCREAMING_SNAKE_CASE_									=						decoder_layers

      SCREAMING_SNAKE_CASE_									=						dropout
      SCREAMING_SNAKE_CASE_									=						attention_dropout
      SCREAMING_SNAKE_CASE_									=						activation_dropout
      SCREAMING_SNAKE_CASE_									=						encoder_layerdrop
      SCREAMING_SNAKE_CASE_									=						decoder_layerdrop

      SCREAMING_SNAKE_CASE_									=						activation_function
      SCREAMING_SNAKE_CASE_									=						init_std

      SCREAMING_SNAKE_CASE_									=						use_cache

      # Autoformer
      SCREAMING_SNAKE_CASE_									=						label_length
      SCREAMING_SNAKE_CASE_									=						moving_average
      SCREAMING_SNAKE_CASE_									=						autocorrelation_factor

      super().__init__(is_encoder_decoder=_A						,		**_A)


  @property
  def 						lowerCAmelCase__     (					self):
      return (
          sum(self.embedding_dimension)
          + self.num_dynamic_real_features
          + self.num_time_features
          + self.num_static_real_features
          + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
      )





import pickle

import numpy as np
from matplotlib import pyplot as plt



class       __snake_case  :

  def __init__(					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A=0.2						,		_A=0.2):
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						conva_get[:2]
      SCREAMING_SNAKE_CASE_									=						conva_get[2]
      SCREAMING_SNAKE_CASE_									=						size_pa
      SCREAMING_SNAKE_CASE_									=						rate_w
      SCREAMING_SNAKE_CASE_									=						rate_t
      SCREAMING_SNAKE_CASE_									=						[
          np.mat(-1 * np.random.rand(self.conva[0]						,		self.conva[0]) + 0.5)
          for i in range(self.conva[1])
      ]
      SCREAMING_SNAKE_CASE_									=						np.mat(-1 * np.random.rand(self.num_bpa						,		self.num_bpa) + 0.5)
      SCREAMING_SNAKE_CASE_									=						np.mat(-1 * np.random.rand(self.num_bpa						,		self.num_bpa) + 0.5)
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.conva[1]) + 1
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.num_bpa) + 1
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.num_bpa) + 1

  def 						lowerCAmelCase__     (					self						,		_A):
      # save model dict with pickle
      SCREAMING_SNAKE_CASE_									=						{
          '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(_A						,		'wb') as f:
          pickle.dump(_A						,		_A)

      print(f"""Model saved： {save_path}""")

  @classmethod
  def 						lowerCAmelCase__     (					cls						,		_A):
      # read saved model
      with open(_A						,		'rb') as f:
          SCREAMING_SNAKE_CASE_									=						pickle.load(_A)  # noqa: S301

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

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

  def 						lowerCAmelCase__     (					self						,		_A):
      return round(_A						,		3)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A):
      # convolution process
      SCREAMING_SNAKE_CASE_									=						convs[0]
      SCREAMING_SNAKE_CASE_									=						convs[1]
      SCREAMING_SNAKE_CASE_									=						np.shape(_A)[0]
      # get the data slice of original image data, data_focus
      SCREAMING_SNAKE_CASE_									=						[]
      for i_focus in range(0						,		size_data - size_conv + 1						,		_A):
          for j_focus in range(0						,		size_data - size_conv + 1						,		_A):
              SCREAMING_SNAKE_CASE_									=						data[
                  i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
              ]
              data_focus.append(_A)
        # calculate the feature map of every single kernel, and saved as list of matrix
      SCREAMING_SNAKE_CASE_									=						[]
      SCREAMING_SNAKE_CASE_									=						int((size_data - size_conv) / conv_step + 1)
      for i_map in range(_A):
          SCREAMING_SNAKE_CASE_									=						[]
          for i_focus in range(len(_A)):
              SCREAMING_SNAKE_CASE_									=						(
                  np.sum(np.multiply(data_focus[i_focus]						,		w_convs[i_map]))
                  - thre_convs[i_map]
              )
              featuremap.append(self.sig(_A))
          SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A).reshape(
              _A						,		_A)
          data_featuremap.append(_A)

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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A="average_pool"):
      # pooling process
      SCREAMING_SNAKE_CASE_									=						len(featuremaps[0])
      SCREAMING_SNAKE_CASE_									=						int(size_map / size_pooling)
      SCREAMING_SNAKE_CASE_									=						[]
      for i_map in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						featuremaps[i_map]
          SCREAMING_SNAKE_CASE_									=						[]
          for i_focus in range(0						,		_A						,		_A):
              for j_focus in range(0						,		_A						,		_A):
                  SCREAMING_SNAKE_CASE_									=						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(_A))
                  elif pooling_type == "max_pooling":
                      # max pooling
                      map_pooled.append(np.max(_A))
          SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A).reshape(_A						,		_A)
          featuremap_pooled.append(_A)
      return featuremap_pooled

  def 						lowerCAmelCase__     (					self						,		_A):
      # expanding three dimension data to one dimension list
      SCREAMING_SNAKE_CASE_									=						[]
      for i in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						np.shape(data[i])
          SCREAMING_SNAKE_CASE_									=						data[i].reshape(1						,		shapes[0] * shapes[1])
          SCREAMING_SNAKE_CASE_									=						data_listed.getA().tolist()[0]
          data_expanded.extend(_A)
      SCREAMING_SNAKE_CASE_									=						np.asarray(_A)
      return data_expanded

  def 						lowerCAmelCase__     (					self						,		_A):
      # expanding matrix to one dimension list
      SCREAMING_SNAKE_CASE_									=						np.asarray(_A)
      SCREAMING_SNAKE_CASE_									=						np.shape(_A)
      SCREAMING_SNAKE_CASE_									=						data_mat.reshape(1						,		shapes[0] * shapes[1])
      return data_expanded

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

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

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

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

              SCREAMING_SNAKE_CASE_									=						pd_i_all / (self.size_poolinga * self.size_poolinga)
              SCREAMING_SNAKE_CASE_									=						pd_conva_pooled.T.getA().tolist()
              SCREAMING_SNAKE_CASE_									=						self._calculate_gradient_from_pool(
                  _A						,		_A						,		shape_featuremapa[0]						,		shape_featuremapa[1]						,		self.size_poolinga						,		)
              # weight and threshold learning process---------
              # convolution layer
              for k_conv in range(self.conva[1]):
                  SCREAMING_SNAKE_CASE_									=						self._expand_mat(pd_conva_all[k_conv])
                  SCREAMING_SNAKE_CASE_									=						self.rate_weight * np.dot(_A						,		_A)

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

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

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

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


  def 						lowerCAmelCase__     (					self						,		_A):
      # return the data of image after convoluting process so we can check it out
      SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.convolute(
          _A						,		self.conva						,		self.w_conva						,		self.thre_conva						,		conv_step=self.step_conva						,		)
      SCREAMING_SNAKE_CASE_									=						self.pooling(_A						,		self.size_poolinga)

      return data_conveda, data_pooleda


if __name__ == "__main__":
       pass


