



import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerCamelCase__	=       logging.getLogger(__name__)

def      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	str ,   _SCREAMING_SNAKE_CASE				:	str			):
				"""simple docstring"""







				__a        =						np.argmax(UpperCamelCase__ ,   axis=1			)
				return np.sum(outputs == labels			)

def      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	Optional[Any]			):
				"""simple docstring"""







				with open(UpperCamelCase__ ,   encoding="""utf_8"""			) as f:
								__a        =						csv.reader(UpperCamelCase__			)
								__a        =						[]
								next(UpperCamelCase__			)  # skip the first line
								for line in tqdm(UpperCamelCase__			):
												output.append((""" """.join(line[1:5]			), line[5], line[6], int(line[-1]			) - 1)			)
				return output

def      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	Any ,   _SCREAMING_SNAKE_CASE				:	Any ,   _SCREAMING_SNAKE_CASE				:	int ,   _SCREAMING_SNAKE_CASE				:	Union[str, Any] ,   _SCREAMING_SNAKE_CASE				:	Optional[Any] ,   _SCREAMING_SNAKE_CASE				:	Optional[int]			):
				"""simple docstring"""







				__a        =						[]
				for dataset in encoded_datasets:
								__a        =						len(UpperCamelCase__			)
								__a        =						np.zeros((n_batch, 2, input_len) ,   dtype=np.intaa			)
								__a        =						np.zeros((n_batch, 2) ,   dtype=np.intaa			)
								__a        =						np.full((n_batch, 2, input_len) ,   fill_value=-100 ,   dtype=np.intaa			)
								__a        =						np.zeros((n_batch,) ,   dtype=np.intaa			)
								for (
								    i,
								    (story, conta, conta, mc_label),
								) in enumerate(UpperCamelCase__			):
												__a        =						[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
												__a        =						[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
												__a        =						with_conta
												__a        =						with_conta
												__a        =						len(UpperCamelCase__			) - 1
												__a        =						len(UpperCamelCase__			) - 1
												__a        =						with_conta
												__a        =						with_conta
												__a        =						mc_label
								__a        =						(input_ids, mc_token_ids, lm_labels, mc_labels)
								tensor_datasets.append(tuple(torch.tensor(UpperCamelCase__			) for t in all_inputs			)			)
				return tensor_datasets

def      lowerCAmelCase__  (							):
				"""simple docstring"""







				__a        =						argparse.ArgumentParser()
				parser.add_argument("""--model_name""" ,   type=UpperCamelCase__ ,   default="""openai-gpt""" ,   help="""pretrained model name"""			)
				parser.add_argument("""--do_train""" ,   action="""store_true""" ,   help="""Whether to run training."""			)
				parser.add_argument("""--do_eval""" ,   action="""store_true""" ,   help="""Whether to run eval on the dev set."""			)
				parser.add_argument(
				    """--output_dir""" ,   default=UpperCamelCase__ ,   type=UpperCamelCase__ ,   required=UpperCamelCase__ ,   help="""The output directory where the model predictions and checkpoints will be written.""" ,   )
				parser.add_argument("""--train_dataset""" ,   type=UpperCamelCase__ ,   default=""""""			)
				parser.add_argument("""--eval_dataset""" ,   type=UpperCamelCase__ ,   default=""""""			)
				parser.add_argument("""--seed""" ,   type=UpperCamelCase__ ,   default=42			)
				parser.add_argument("""--num_train_epochs""" ,   type=UpperCamelCase__ ,   default=3			)
				parser.add_argument("""--train_batch_size""" ,   type=UpperCamelCase__ ,   default=8			)
				parser.add_argument("""--eval_batch_size""" ,   type=UpperCamelCase__ ,   default=16			)
				parser.add_argument("""--adam_epsilon""" ,   default=1e-8 ,   type=UpperCamelCase__ ,   help="""Epsilon for Adam optimizer."""			)
				parser.add_argument("""--max_grad_norm""" ,   type=UpperCamelCase__ ,   default=1			)
				parser.add_argument(
				    """--max_steps""" ,   default=-1 ,   type=UpperCamelCase__ ,   help=(
				        """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
				    ) ,   )
				parser.add_argument(
				    """--gradient_accumulation_steps""" ,   type=UpperCamelCase__ ,   default=1 ,   help="""Number of updates steps to accumulate before                        performing a backward/update pass.""" ,   )
				parser.add_argument("""--learning_rate""" ,   type=UpperCamelCase__ ,   default=6.25e-5			)
				parser.add_argument("""--warmup_steps""" ,   default=0 ,   type=UpperCamelCase__ ,   help="""Linear warmup over warmup_steps."""			)
				parser.add_argument("""--lr_schedule""" ,   type=UpperCamelCase__ ,   default="""warmup_linear"""			)
				parser.add_argument("""--weight_decay""" ,   type=UpperCamelCase__ ,   default=0.01			)
				parser.add_argument("""--lm_coef""" ,   type=UpperCamelCase__ ,   default=0.9			)
				parser.add_argument("""--n_valid""" ,   type=UpperCamelCase__ ,   default=374			)

				parser.add_argument("""--server_ip""" ,   type=UpperCamelCase__ ,   default="""""" ,   help="""Can be used for distant debugging."""			)
				parser.add_argument("""--server_port""" ,   type=UpperCamelCase__ ,   default="""""" ,   help="""Can be used for distant debugging."""			)
				__a        =						parser.parse_args()
				print(UpperCamelCase__			)

				if args.server_ip and args.server_port:
								# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
								import ptvsd

								print("""Waiting for debugger attach"""			)
								ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,   redirect_output=UpperCamelCase__			)
								ptvsd.wait_for_attach()

				random.seed(args.seed			)
				np.random.seed(args.seed			)
				torch.manual_seed(args.seed			)
				torch.cuda.manual_seed_all(args.seed			)

				__a        =						torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""			)
				__a        =						torch.cuda.device_count()
				logger.info("""device: {}, n_gpu {}""".format(UpperCamelCase__ ,   UpperCamelCase__			)			)

				if not args.do_train and not args.do_eval:
								raise ValueError("""At least one of `do_train` or `do_eval` must be True."""			)

				if not os.path.exists(args.output_dir			):
								os.makedirs(args.output_dir			)

				# Load tokenizer and model
				# This loading functions also add new tokens and embeddings called `special tokens`
				# These new embeddings will be fine-tuned on the RocStories dataset
				__a        =						["_start_", "_delimiter_", "_classify_"]
				__a        =						OpenAIGPTTokenizer.from_pretrained(args.model_name			)
				tokenizer.add_tokens(UpperCamelCase__			)
				__a        =						tokenizer.convert_tokens_to_ids(UpperCamelCase__			)
				__a        =						OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name			)
				model.resize_token_embeddings(len(UpperCamelCase__			)			)
				model.to(UpperCamelCase__			)

				# Load and encode the datasets
				def tokenize_and_encode(_SCREAMING_SNAKE_CASE				:	int			):
								if isinstance(UpperCamelCase__ ,   UpperCamelCase__			):
												return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(UpperCamelCase__			)			)
								elif isinstance(UpperCamelCase__ ,   UpperCamelCase__			):
												return obj
								return [tokenize_and_encode(UpperCamelCase__			) for o in obj]

				logger.info("""Encoding dataset..."""			)
				__a        =						load_rocstories_dataset(args.train_dataset			)
				__a        =						load_rocstories_dataset(args.eval_dataset			)
				__a        =						(train_dataset, eval_dataset)
				__a        =						tokenize_and_encode(UpperCamelCase__			)

				# Compute the max input length for the Transformer
				__a        =						model.config.n_positions // 2 - 2
				__a        =						max(
				    len(story[:max_length]			) + max(len(conta[:max_length]			) ,   len(conta[:max_length]			)			) + 3
				    for dataset in encoded_datasets
				    for story, conta, conta, _ in dataset			)
				__a        =						min(UpperCamelCase__ ,   model.config.n_positions			)  # Max size of input for the pre-trained model

				# Prepare inputs tensors and dataloaders
				__a        =						pre_process_datasets(UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__ ,   *UpperCamelCase__			)
				__a        =						tensor_datasets[0], tensor_datasets[1]

				__a        =						TensorDataset(*UpperCamelCase__			)
				__a        =						RandomSampler(UpperCamelCase__			)
				__a        =						DataLoader(UpperCamelCase__ ,   sampler=UpperCamelCase__ ,   batch_size=args.train_batch_size			)

				__a        =						TensorDataset(*UpperCamelCase__			)
				__a        =						SequentialSampler(UpperCamelCase__			)
				__a        =						DataLoader(UpperCamelCase__ ,   sampler=UpperCamelCase__ ,   batch_size=args.eval_batch_size			)

				# Prepare optimizer
				if args.do_train:
								if args.max_steps > 0:
												__a        =						args.max_steps
												__a        =						args.max_steps // (len(UpperCamelCase__			) // args.gradient_accumulation_steps) + 1
								else:
												__a        =						len(UpperCamelCase__			) // args.gradient_accumulation_steps * args.num_train_epochs

								__a        =						list(model.named_parameters()			)
								__a        =						["bias", "LayerNorm.bias", "LayerNorm.weight"]
								__a        =						[
								    {
								        "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay			)],
								        "weight_decay": args.weight_decay,
								    },
								    {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay			)], "weight_decay": 0.0},
								]
								__a        =						AdamW(UpperCamelCase__ ,   lr=args.learning_rate ,   eps=args.adam_epsilon			)
								__a        =						get_linear_schedule_with_warmup(
								    UpperCamelCase__ ,   num_warmup_steps=args.warmup_steps ,   num_training_steps=UpperCamelCase__			)

				if args.do_train:
								__a        =						0, 0, None
								model.train()
								for _ in trange(int(args.num_train_epochs			) ,   desc="""Epoch"""			):
												__a        =						0
												__a        =						0
												__a        =						tqdm(UpperCamelCase__ ,   desc="""Training"""			)
												for step, batch in enumerate(UpperCamelCase__			):
																__a        =						tuple(t.to(UpperCamelCase__			) for t in batch			)
																__a        =						batch
																__a        =						model(UpperCamelCase__ ,   mc_token_ids=UpperCamelCase__ ,   lm_labels=UpperCamelCase__ ,   mc_labels=UpperCamelCase__			)
																__a        =						args.lm_coef * losses[0] + losses[1]
																loss.backward()
																optimizer.step()
																scheduler.step()
																optimizer.zero_grad()
																tr_loss += loss.item()
																__a        =						(
																    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																)
																nb_tr_steps += 1
																__a        =						"Training loss: {:.2e} lr: {:.2e}".format(UpperCamelCase__ ,   scheduler.get_lr()[0]			)

    # Save a trained model
				if args.do_train:
								# Save a trained model, configuration and tokenizer
								__a        =						model.module if hasattr(UpperCamelCase__ ,   """module"""			) else model  # Only save the model itself

								# If we save using the predefined names, we can load using `from_pretrained`
								__a        =						os.path.join(args.output_dir ,   UpperCamelCase__			)
								__a        =						os.path.join(args.output_dir ,   UpperCamelCase__			)

								torch.save(model_to_save.state_dict() ,   UpperCamelCase__			)
								model_to_save.config.to_json_file(UpperCamelCase__			)
								tokenizer.save_vocabulary(args.output_dir			)

								# Load a trained model and vocabulary that you have fine-tuned
								__a        =						OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir			)
								__a        =						OpenAIGPTTokenizer.from_pretrained(args.output_dir			)
								model.to(UpperCamelCase__			)

				if args.do_eval:
								model.eval()
								__a        =						0, 0
								__a        =						0, 0
								for batch in tqdm(UpperCamelCase__ ,   desc="""Evaluating"""			):
												__a        =						tuple(t.to(UpperCamelCase__			) for t in batch			)
												__a        =						batch
												with torch.no_grad():
																__a        =						model(
																    UpperCamelCase__ ,   mc_token_ids=UpperCamelCase__ ,   lm_labels=UpperCamelCase__ ,   mc_labels=UpperCamelCase__			)

												__a        =						mc_logits.detach().cpu().numpy()
												__a        =						mc_labels.to("""cpu"""			).numpy()
												__a        =						accuracy(UpperCamelCase__ ,   UpperCamelCase__			)

												eval_loss += mc_loss.mean().item()
												eval_accuracy += tmp_eval_accuracy

												nb_eval_examples += input_ids.size(0			)
												nb_eval_steps += 1

								__a        =						eval_loss / nb_eval_steps
								__a        =						eval_accuracy / nb_eval_examples
								__a        =						tr_loss / nb_tr_steps if args.do_train else None
								__a        =						{"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

								__a        =						os.path.join(args.output_dir ,   """eval_results.txt"""			)
								with open(UpperCamelCase__ ,   """w"""			) as writer:
												logger.info("""***** Eval results *****"""			)
												for key in sorted(result.keys()			):
																logger.info("""  %s = %s""" ,   UpperCamelCase__ ,   str(result[key]			)			)
																writer.write("""%s = %s\n""" % (key, str(result[key]			))			)


if __name__ == "__main__":
						main()




import pickle

import numpy as np
from matplotlib import pyplot as plt







class      lowerCAmelCase__       :




     '''simple docstring'''


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


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            # save model dict with pickle
            _A      :       Dict				    = {
                "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(__lowerCamelCase ,						"wb") as f:
                   pickle.dump(__lowerCamelCase ,						__lowerCamelCase)

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


     @classmethod
     def  _lowerCamelCase (						cls ,						__lowerCamelCase)			->				Any:
            # read saved model
            with open(__lowerCamelCase ,						"rb") as f:
                   _A      :       Any				    = pickle.load(__lowerCamelCase)  # noqa: S301

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


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            return 1 / (1 + np.exp(-1 * x))


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Union[str, Any]:
            return round(__lowerCamelCase ,						3)


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

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


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


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


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


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase)			->				Optional[int]:
            _A      :       Dict				    = []
            _A      :       Any				    = 0
            for i_map in range(__lowerCamelCase):
                   _A      :       Union[str, Any]				    = np.ones((size_map, size_map))
                   for i in range(0 ,						__lowerCamelCase ,						__lowerCamelCase):
                          for j in range(0 ,						__lowerCamelCase ,						__lowerCamelCase):
                                 _A      :       List[Any]				    = pd_pool[
                                     i_pool
                                 ]
                                 _A      :       Tuple				    = i_pool + 1
                   _A      :       Optional[Any]				    = np.multiply(
                       __lowerCamelCase ,						np.multiply(out_map[i_map] ,						(1 - out_map[i_map])))
                   pd_all.append(__lowerCamelCase)
            return pd_all


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

                          _A      :       int				    = np.dot(__lowerCamelCase ,						self.vji.T) - self.thre_bpa
                          _A      :       List[Any]				    = self.sig(__lowerCamelCase)
                          _A      :       Union[str, Any]				    = np.dot(__lowerCamelCase ,						self.wkj.T) - self.thre_bpa
                          _A      :       List[str]				    = self.sig(__lowerCamelCase)

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

                          _A      :       Any				    = pd_i_all / (self.size_poolinga * self.size_poolinga)
                          _A      :       Dict				    = pd_conva_pooled.T.getA().tolist()
                          _A      :       Optional[Any]				    = self._calculate_gradient_from_pool(
                              __lowerCamelCase ,						__lowerCamelCase ,						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      :       Optional[int]				    = self.rate_weight * np.dot(__lowerCamelCase ,						__lowerCamelCase)

                                 _A      :       List[Any]				    = self.w_conva[k_conv] + delta_w.reshape(
                                     (self.conva[0], self.conva[0]))

                                 _A      :       Any				    = (
                                     self.thre_conva[k_conv]
                                     - np.sum(pd_conva_all[k_conv]) * self.rate_thre
                                 )
                          # all connected layer
                          _A      :       Tuple				    = self.wkj + pd_k_all.T * bp_outa * self.rate_weight
                          _A      :       int				    = self.vji + pd_j_all.T * bp_outa * self.rate_weight
                          _A      :       Tuple				    = self.thre_bpa - pd_k_all * self.rate_thre
                          _A      :       List[str]				    = 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      :       Any				    = rp + 1
                   _A      :       Dict				    = error_count / patterns
                   all_mse.append(__lowerCamelCase)

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

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


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

            return data_conveda, data_pooleda


if __name__ == "__main__":
    pass
