






"""simple docstring"""



import pickle

import numpy as np
from matplotlib import pyplot as plt

class        a     :







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase=0.2							,		_lowerCamelCase=0.2       ):
      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 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      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(_A							,		'wb'       ) as f:
        pickle.dump(_A							,		_A       )

      print(F'Model saved： {save_path}'       )







    @classmethod
    def 			UpperCamelCase_      (			cls							,		_lowerCamelCase       ):



      with open(_A							,		'rb'       ) as f:
        lowercase							    =       pickle.load(_A       )  # 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(_A							,		_A							,		_A							,		_A							,		_A							,		_A							,		_A       )
      # 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 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      return 1 / (1 + np.exp(-1 * x       ))







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      return round(_A							,		3       )







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

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







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







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







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       np.asarray(_A       )
      lowercase							    =       np.shape(_A       )
      lowercase							    =       data_mat.reshape(1							,		shapes[0] * shapes[1]       )
      return data_expanded







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







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase=bool       ):
      print('----------------------Start Training-------------------------'       )
      print((' - - Shape: Train_Data  ', np.shape(_A       ))       )
      print((' - - Shape: Teach_Data  ', np.shape(_A       ))       )
      lowercase							    =       0
      lowercase							    =       []
      lowercase							    =       1_0_0_0_0
      while rp < n_repeat and mse >= error_accuracy:
        lowercase							    =       0
        print(F'-------------Learning Time {rp}--------------'       )
        for p in range(len(_A       )       ):
          # print('------------Learning Image: %d--------------'%p)
          lowercase							    =       np.asmatrix(datas_train[p]       )
          lowercase							    =       np.asarray(datas_teach[p]       )
          lowercase    ,     lowercase							    =       self.convolute(
              _A							,		self.conva							,		self.w_conva							,		self.thre_conva							,		conv_step=self.step_conva							,		)
          lowercase							    =       self.pooling(_A							,		self.size_poolinga       )
          lowercase							    =       np.shape(_A       )
          lowercase							    =       self._expand(_A       )
          lowercase							    =       data_bp_input

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

          # --------------Model Leaning ------------------------
          # calculate error and gradient---------------
          lowercase							    =       np.multiply(
              (data_teach - bp_outa)							,		np.multiply(_A							,		(1 - bp_outa)       )       )
          lowercase							    =       np.multiply(
              np.dot(_A							,		self.wkj       )							,		np.multiply(_A							,		(1 - bp_outa)       )       )
          lowercase							    =       np.dot(_A							,		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(
              _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]       ):
            lowercase							    =       self._expand_mat(pd_conva_all[k_conv]       )
            lowercase							    =       self.rate_weight * np.dot(_A							,		_A       )

            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(_A       )

      def draw_error():
        lowercase							    =       [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 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       []
      print('-------------------Start Testing-------------------------'       )
      print((' - - Shape: Test_Data  ', np.shape(_A       ))       )
      for p in range(len(_A       )       ):
        lowercase							    =       np.asmatrix(datas_test[p]       )
        lowercase    ,     lowercase							    =       self.convolute(
            _A							,		self.conva							,		self.w_conva							,		self.thre_conva							,		conv_step=self.step_conva							,		)
        lowercase							    =       self.pooling(_A							,		self.size_poolinga       )
        lowercase							    =       self._expand(_A       )

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







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       np.asmatrix(_A       )
      lowercase    ,     lowercase							    =       self.convolute(
          _A							,		self.conva							,		self.w_conva							,		self.thre_conva							,		conv_step=self.step_conva							,		)
      lowercase							    =       self.pooling(_A							,		self.size_poolinga       )

      return data_conveda, data_pooleda


if __name__ == "__main__":
    pass








import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					__lowerCAmelCase				   =       filter(lambda SCREAMING_SNAKE_CASE_     : p.requires_grad      ,      model.parameters()     )
					__lowerCAmelCase				   =       sum([np.prod(p.size()     ) for p in model_parameters]     )
					return params


UpperCamelCase__    			=			logging.getLogger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				Any     ):
					if metric == "rouge2":
										__lowerCAmelCase				   =       "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										__lowerCAmelCase				   =       "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "em":
										__lowerCAmelCase				   =       "{val_avg_em:.4f}-{step_count}"
					else:
										raise NotImplementedError(
										    F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
										    " function."     )

					__lowerCAmelCase				   =       ModelCheckpoint(
					    dirpath=SCREAMING_SNAKE_CASE_      ,      filename=SCREAMING_SNAKE_CASE_      ,      monitor=F"""val_{metric}"""      ,      mode="max"      ,      save_top_k=3      ,      every_n_epochs=1      ,      )
					return checkpoint_callback







def 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					return EarlyStopping(
					    monitor=F"""val_{metric}"""      ,      mode="min" if "loss" in metric else "max"      ,      patience=SCREAMING_SNAKE_CASE_      ,      verbose=SCREAMING_SNAKE_CASE_      ,      )


class 			a__    (					pl.Callback      ):






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {f"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
											pl_module.logger.log_metrics(_A			)






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A=True			):




											"""simple docstring"""



											logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""			)
											__lowerCAmelCase				   =       trainer.callback_metrics
											trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}			)
											# Log results
											__lowerCAmelCase				   =       Path(pl_module.hparams.output_dir			)
											if type_path == "test":
																__lowerCAmelCase				   =       od / "test_results.txt"
																__lowerCAmelCase				   =       od / "test_generations.txt"
											else:
																# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
																# If people want this it will be easy enough to add back.
																__lowerCAmelCase				   =       od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
																__lowerCAmelCase				   =       od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
																results_file.parent.mkdir(exist_ok=_A			)
																generations_file.parent.mkdir(exist_ok=_A			)
											with open(_A		,			"a+"			) as writer:
																for key in sorted(_A			):
																					if key in ["log", "progress_bar", "preds"]:
																										continue
																					__lowerCAmelCase				   =       metrics[key]
																					if isinstance(_A		,			torch.Tensor			):
																										__lowerCAmelCase				   =       val.item()
																					__lowerCAmelCase				   =       f"""{key}: {val:.6f}\n"""
																					writer.write(_A			)

											if not save_generations:
																return

											if "preds" in metrics:
																__lowerCAmelCase				   =       "\n".join(metrics["preds"]			)
																generations_file.open("w+"			).write(_A			)






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											try:
																__lowerCAmelCase				   =       pl_module.model.model.num_parameters()
											except AttributeError:
																__lowerCAmelCase				   =       pl_module.model.num_parameters()

											__lowerCAmelCase				   =       count_trainable_parameters(_A			)
											# mp stands for million parameters
											trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1E6, "grad_mp": n_trainable_pars / 1E6}			)






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											save_json(pl_module.metrics		,			pl_module.metrics_save_path			)
											return self._write_logs(_A		,			_A		,			"test"			)






						@rank_zero_only
						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A			):




											"""simple docstring"""



											save_json(pl_module.metrics		,			pl_module.metrics_save_path			)
											# Uncommenting this will save val generations
											# return self._write_logs(trainer, pl_module, "valid")




