






import pickle

import numpy as np
from matplotlib import pyplot as plt


class     _a							:






  """simple docstring"""





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




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			int:
     # save model dict with pickle
     UpperCamelCase_						=  {
         '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(_UpperCAmelCase		,		'wb'			) as f:
        pickle.dump(_UpperCAmelCase		,		_UpperCAmelCase			)

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




  @classmethod
  def  _UpperCAmelCase  (  cls		,		_UpperCAmelCase			)      ->			Optional[Any]:
     # read saved model
     with open(_UpperCAmelCase		,		'rb'			) as f:
        UpperCamelCase_						=  pickle.load(_UpperCAmelCase			)  # noqa: S301

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




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Tuple:
     return 1 / (1 + np.exp(-1 * x			))




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			str:
     return round(_UpperCAmelCase		,		3			)




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

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




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




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Union[str, Any]:
     # expanding three dimension data to one dimension list
     UpperCamelCase_						=  []
     for i in range(len(_UpperCAmelCase			)			):
        UpperCamelCase_						=  np.shape(data[i]			)
        UpperCamelCase_						=  data[i].reshape(1		,		shapes[0] * shapes[1]			)
        UpperCamelCase_						=  data_listed.getA().tolist()[0]
        data_expanded.extend(_UpperCAmelCase			)
     UpperCamelCase_						=  np.asarray(_UpperCAmelCase			)
     return data_expanded




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     # expanding matrix to one dimension list
     UpperCamelCase_						=  np.asarray(_UpperCAmelCase			)
     UpperCamelCase_						=  np.shape(_UpperCAmelCase			)
     UpperCamelCase_						=  data_mat.reshape(1		,		shapes[0] * shapes[1]			)
     return data_expanded




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




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

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

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

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

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

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

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

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




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

     return data_conveda, data_pooleda


if __name__ == "__main__":
 pass











import operator as op


__UpperCamelCase   :			Optional[Any]          = "scaler.pt"
__UpperCamelCase   :			Optional[Any]          = "pytorch_model"
__UpperCamelCase   :			str          = "random_states"
__UpperCamelCase   :			Optional[int]          = "optimizer"
__UpperCamelCase   :			Optional[int]          = "scheduler"
__UpperCamelCase   :			str          = "pytorch_model.bin"
__UpperCamelCase   :			List[str]          = "pytorch_model.bin.index.json"
__UpperCamelCase   :			List[str]          = "model.safetensors"
__UpperCamelCase   :			Optional[int]          = "model.safetensors.index.json"
__UpperCamelCase   :			List[str]          = "1.10.2"
__UpperCamelCase   :			Dict          = "py38"
__UpperCamelCase   :			List[str]          = "4.17.0"
__UpperCamelCase   :			Any          = ["ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4dn.24xlarge"]
__UpperCamelCase   :			Any          = ["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD", "HYBRID_SHARD_ZERO2"]
__UpperCamelCase   :			int          = ["TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", "NO_WRAP"]
__UpperCamelCase   :			Dict          = ["BACKWARD_PRE", "BACKWARD_POST", "NO_PREFETCH"]
__UpperCamelCase   :			str          = ["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"]
__UpperCamelCase   :			List[Any]          = "2.0.1"
__UpperCamelCase   :			int          = ["pdsh", "standard", "openmpi", "mvapich"]
__UpperCamelCase   :			List[str]          = ["default", "reduce-overhead", "max-autotune"]

__UpperCamelCase   :			List[Any]          = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
__UpperCamelCase   :			List[Any]          = [
    "nnodes",
    "nproc_per_node",
    "rdzv_backend",
    "rdzv_endpoint",
    "rdzv_id",
    "rdzv_conf",
    "standalone",
    "max_restarts",
    "monitor_interval",
    "start_method",
    "role",
    "module",
    "m",
    "no_python",
    "run_path",
    "log_dir",
    "r",
    "redirects",
    "t",
    "tee",
    "node_rank",
    "master_addr",
    "master_port",
]

__UpperCamelCase   :			List[str]          = ["DEEPSPEED", "MULTI_GPU", "FSDP", "MEGATRON_LM"]
__UpperCamelCase   :			int          = ["DEEPSPEED", "MULTI_XPU", "FSDP"]

