





import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


_lowerCamelCase 					=      logging.getLogger(__name__)

_lowerCamelCase 					=      '''pytorch_model.bin'''




@dataclasses.dataclass
class 	UpperCamelCase_							:
							lowerCamelCase_							     =       dataclasses.field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}     )
							lowerCamelCase_							     =       dataclasses.field(
							    default=SCREAMING_SNAKE_CASE_					,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}					,	)




@dataclasses.dataclass
class 	UpperCamelCase_							:
							lowerCamelCase_							     =       dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}     )
							lowerCamelCase_							     =       dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}     )
							lowerCamelCase_							     =       dataclasses.field(
							    default=SCREAMING_SNAKE_CASE_					,	metadata={"help": "A csv or a json file containing the validation data."}     )
							lowerCamelCase_							     =       dataclasses.field(
							    default=SCREAMING_SNAKE_CASE_					,	metadata={"help": "The name of the task to train on."}					,	)
							lowerCamelCase_							     =       dataclasses.field(
							    default=SCREAMING_SNAKE_CASE_					,	metadata={"help": "The list of labels for the task."}     )




@dataclasses.dataclass
class 	UpperCamelCase_							:
							lowerCamelCase_							     =       dataclasses.field(
							    metadata={"help": "The output directory where the model predictions and checkpoints will be written."}     )
							lowerCamelCase_							     =       dataclasses.field(
							    default="accuracy"					,	metadata={"help": "The evaluation metric used for the task."}     )
							lowerCamelCase_							     =       dataclasses.field(
							    default="no"					,	metadata={
							        "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
							    }					,	)
							lowerCamelCase_							     =       dataclasses.field(
							    default=10					,	metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}					,	)
							lowerCamelCase_							     =       dataclasses.field(
							    default=0.0					,	metadata={
							        "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
							    }					,	)
							lowerCamelCase_							     =       dataclasses.field(
							    default=SCREAMING_SNAKE_CASE_					,	metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}					,	)
							lowerCamelCase_							     =       dataclasses.field(
							    default=SCREAMING_SNAKE_CASE_					,	metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}					,	)
							lowerCamelCase_							     =       dataclasses.field(
							    default=SCREAMING_SNAKE_CASE_					,	metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}					,	)
							lowerCamelCase_							     =       dataclasses.field(
							    default=0.0					,	metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}					,	)
							lowerCamelCase_							     =       dataclasses.field(
							    default=1_00					,	metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}					,	)
							lowerCamelCase_							     =       dataclasses.field(
							    default=SCREAMING_SNAKE_CASE_					,	metadata={"help": "Random seed for initialization."}					,	)






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				Any       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				int       , UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__								=   datasets.concatenate_datasets([infer_input, infer_output]       , axis=1     )

		if args.do_filter_by_confidence:
				SCREAMING_SNAKE_CASE__								=   dataset.filter(lambda UpperCamelCase__     : example["probability"] > args.confidence_threshold     )

		if args.do_filter_by_val_performance:
				assert eval_result >= 0.0 and eval_result <= 1.0
				SCREAMING_SNAKE_CASE__								=   int(eval_result * len(_UpperCAmelCase     )     )
				print(_UpperCAmelCase     )
				SCREAMING_SNAKE_CASE__								=   dataset.sort("""probability"""       , reverse=_UpperCAmelCase     )
				SCREAMING_SNAKE_CASE__								=   dataset.select(range(_UpperCAmelCase     )     )

		SCREAMING_SNAKE_CASE__								=   dataset.remove_columns(["""label""", """probability"""]     )
		SCREAMING_SNAKE_CASE__								=   dataset.rename_column("""prediction"""       , """label"""     )
		SCREAMING_SNAKE_CASE__								=   dataset.map(lambda UpperCamelCase__     : {"label": idalabel[example["label"]]}     )
		SCREAMING_SNAKE_CASE__								=   dataset.shuffle(seed=args.seed     )

		SCREAMING_SNAKE_CASE__								=   os.path.join(_UpperCAmelCase       , f'''train_pseudo.{args.data_file_extension}'''     )
		if args.data_file_extension == "csv":
				dataset.to_csv(_UpperCAmelCase       , index=_UpperCAmelCase     )
		else:
				dataset.to_json(_UpperCAmelCase     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				int       , UpperCamelCase__:				int       , **UpperCamelCase__:				Union[str, Any]     ):
		SCREAMING_SNAKE_CASE__								=   Accelerator()
		# Make one log on every process with the configuration for debugging.
		logging.basicConfig(
		    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""       , datefmt="""%m/%d/%Y %H:%M:%S"""       , level=logging.INFO       , )
		logger.info(accelerator.state     )

		# Setup logging, we only want one process per machine to log things on the
		# screen. accelerator.is_local_main_process is only True for one process per
		# machine.
		logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR     )

		if accelerator.is_local_main_process:
				datasets.utils.logging.set_verbosity_warning()
				transformers.utils.logging.set_verbosity_info()
		else:
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()

		SCREAMING_SNAKE_CASE__								=   STModelArguments(model_name_or_path=_UpperCAmelCase     )
		SCREAMING_SNAKE_CASE__								=   STDataArguments(train_file=_UpperCAmelCase       , infer_file=_UpperCAmelCase     )
		SCREAMING_SNAKE_CASE__								=   STTrainingArguments(output_dir=_UpperCAmelCase     )
		SCREAMING_SNAKE_CASE__								=   argparse.Namespace()

		for arg_class in (model_args, data_args, training_args):
				for key, value in vars(_UpperCAmelCase     ).items():
						setattr(_UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase     )

		for key, value in kwargs.items():
				if hasattr(_UpperCAmelCase       , _UpperCAmelCase     ):
						setattr(_UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase     )

    # Sanity checks
		SCREAMING_SNAKE_CASE__								=   {}
		SCREAMING_SNAKE_CASE__								=   None

		# You need to provide the training data and the data to predict on
		assert args.train_file is not None
		assert args.infer_file is not None
		SCREAMING_SNAKE_CASE__								=   args.train_file
		SCREAMING_SNAKE_CASE__								=   args.infer_file

		if args.evaluation_strategy != IntervalStrategy.NO.value:
				assert args.eval_file is not None
				SCREAMING_SNAKE_CASE__								=   args.eval_file

		for key in data_files:
				SCREAMING_SNAKE_CASE__								=   data_files[key].split("""."""     )[-1]
				assert extension in ["csv", "json"], f'''`{key}_file` should be a csv or a json file.'''
				if args.data_file_extension is None:
						SCREAMING_SNAKE_CASE__								=   extension
				else:
						assert extension == args.data_file_extension, f'''`{key}_file` should be a {args.data_file_extension} file`.'''

		assert (
		    args.eval_metric in datasets.list_metrics()
		), f'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''

		# If passed along, set the training seed now.
		if args.seed is not None:
				set_seed(args.seed     )

		logger.info("""Creating the initial data directory for self-training..."""     )
		SCREAMING_SNAKE_CASE__								=   f'''{args.output_dir}/self-train_iter-{{}}'''.format
		SCREAMING_SNAKE_CASE__								=   data_dir_format(0     )

		if accelerator.is_main_process:
				if args.output_dir is not None:
						os.makedirs(args.output_dir       , exist_ok=_UpperCAmelCase     )
						os.makedirs(_UpperCAmelCase       , exist_ok=_UpperCAmelCase     )
		accelerator.wait_for_everyone()

		SCREAMING_SNAKE_CASE__								=   None
		SCREAMING_SNAKE_CASE__								=   None
		SCREAMING_SNAKE_CASE__								=   0
		SCREAMING_SNAKE_CASE__								=   False
		# Show the progress bar
		SCREAMING_SNAKE_CASE__								=   tqdm(range(args.max_selftrain_iterations     )       , disable=not accelerator.is_local_main_process     )

		# Self-train
		for iteration in range(0       , int(args.max_selftrain_iterations     )     ):
				SCREAMING_SNAKE_CASE__								=   data_dir_format(_UpperCAmelCase     )
				assert os.path.exists(_UpperCAmelCase     )

				# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
				# iteration > 0
				SCREAMING_SNAKE_CASE__								=   os.path.join(_UpperCAmelCase       , """stage-1"""     )
				SCREAMING_SNAKE_CASE__								=   {
				    'accelerator': accelerator,
				    'model_name_or_path': args.model_name_or_path,
				    'cache_dir': args.cache_dir,
				    'do_train': True,
				    'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
				    'do_eval': True if args.eval_file is not None else False,
				    'eval_file': data_files['eval'],
				    'do_predict': True,
				    'infer_file': data_files['infer'],
				    'task_name': args.task_name,
				    'label_list': args.label_list,
				    'output_dir': current_output_dir,
				    'eval_metric': args.eval_metric,
				    'evaluation_strategy': args.evaluation_strategy,
				    'early_stopping_patience': args.early_stopping_patience,
				    'early_stopping_threshold': args.early_stopping_threshold,
				    'seed': args.seed,
				}
				# Add additional training arguments
				for key, value in kwargs.items():
						if key not in arguments_dict and not hasattr(_UpperCAmelCase       , _UpperCAmelCase     ):
								arguments_dict.update({key: value}     )

				SCREAMING_SNAKE_CASE__								=   os.path.join(_UpperCAmelCase       , """best-checkpoint"""       , _UpperCAmelCase     )
				if os.path.exists(_UpperCAmelCase     ):
						logger.info(
						    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."""       , _UpperCAmelCase       , _UpperCAmelCase       , )
				else:
						logger.info("""***** Running self-training: iteration: %d, stage: 1 *****"""       , _UpperCAmelCase     )
						finetune(**_UpperCAmelCase     )
						accelerator.wait_for_everyone()
						assert os.path.exists(_UpperCAmelCase     )
						logger.info("""Self-training job completed: iteration: %d, stage: 1."""       , _UpperCAmelCase     )

				if iteration > 0 and args.finetune_on_labeled_data:
						# Stage 2 (optional): fine-tuning on the original labeled data
						SCREAMING_SNAKE_CASE__								=   os.path.join(_UpperCAmelCase       , """best-checkpoint"""     )
						SCREAMING_SNAKE_CASE__								=   os.path.join(_UpperCAmelCase       , """stage-2"""     )
						# Update arguments_dict
						SCREAMING_SNAKE_CASE__								=   model_path
						SCREAMING_SNAKE_CASE__								=   data_files['train']
						SCREAMING_SNAKE_CASE__								=   current_output_dir

						SCREAMING_SNAKE_CASE__								=   os.path.join(_UpperCAmelCase       , """best-checkpoint"""       , _UpperCAmelCase     )
						if os.path.exists(_UpperCAmelCase     ):
								logger.info(
								    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."""       , _UpperCAmelCase       , _UpperCAmelCase       , )
						else:
								logger.info("""***** Running self-training: iteration: %d, stage: 2 *****"""       , _UpperCAmelCase     )
								finetune(**_UpperCAmelCase     )
								accelerator.wait_for_everyone()
								assert os.path.exists(_UpperCAmelCase     )
								logger.info("""Self-training job completed: iteration: %d, stage: 2."""       , _UpperCAmelCase     )

				SCREAMING_SNAKE_CASE__								=   iteration
				SCREAMING_SNAKE_CASE__								=   data_dir_format(iteration + 1     )

				SCREAMING_SNAKE_CASE__								=   AutoConfig.from_pretrained(os.path.join(_UpperCAmelCase       , """best-checkpoint"""     )     )
				SCREAMING_SNAKE_CASE__								=   config.idalabel
				SCREAMING_SNAKE_CASE__								=   os.path.join(_UpperCAmelCase       , """eval_results_best-checkpoint.json"""     )
				SCREAMING_SNAKE_CASE__								=   os.path.join(_UpperCAmelCase       , """test_results_best-checkpoint.json"""     )
				assert os.path.exists(_UpperCAmelCase     )

				with open(_UpperCAmelCase       , """r"""     ) as f:
						SCREAMING_SNAKE_CASE__								=   float(json.load(_UpperCAmelCase     )[args.eval_metric]     )
				SCREAMING_SNAKE_CASE__								=   os.path.join(_UpperCAmelCase       , """infer_output_best-checkpoint.csv"""     )
				assert os.path.exists(_UpperCAmelCase     )
				# Loading the dataset from local csv or json files.
				SCREAMING_SNAKE_CASE__								=   load_dataset(args.data_file_extension       , data_files={"""data""": data_files["""infer"""]}     )['data']
				SCREAMING_SNAKE_CASE__								=   load_dataset("""csv"""       , data_files={"""data""": infer_output_file}     )['data']

				if accelerator.is_main_process:
						os.makedirs(_UpperCAmelCase       , exist_ok=_UpperCAmelCase     )
						shutil.copy(_UpperCAmelCase       , os.path.join(_UpperCAmelCase       , f'''eval_results_iter-{iteration}.json'''     )     )
						if os.path.exists(_UpperCAmelCase     ):
								shutil.copy(_UpperCAmelCase       , os.path.join(_UpperCAmelCase       , f'''test_results_iter-{iteration}.json'''     )     )
						create_pseudo_labeled_data(_UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase     )
				accelerator.wait_for_everyone()

				SCREAMING_SNAKE_CASE__								=   os.path.join(_UpperCAmelCase       , f'''train_pseudo.{args.data_file_extension}'''     )

				if args.evaluation_strategy != IntervalStrategy.NO.value:
						SCREAMING_SNAKE_CASE__								=   eval_result

						if best_iteration is None:
								SCREAMING_SNAKE_CASE__								=   new_iteration
								SCREAMING_SNAKE_CASE__								=   new_eval_result
						else:
								if new_eval_result - best_eval_result > args.early_stopping_threshold:
										SCREAMING_SNAKE_CASE__								=   new_iteration
										SCREAMING_SNAKE_CASE__								=   new_eval_result
										SCREAMING_SNAKE_CASE__								=   0
								else:
										if new_eval_result == best_eval_result:
												SCREAMING_SNAKE_CASE__								=   new_iteration
												SCREAMING_SNAKE_CASE__								=   new_eval_result
										early_stopping_patience_counter += 1

								if early_stopping_patience_counter >= args.early_stopping_patience:
										SCREAMING_SNAKE_CASE__								=   True

				progress_bar.update(1     )

				if should_training_stop:
						break

		if best_iteration is not None:
				# Save the best iteration
				logger.info("""Best iteration: %d"""       , _UpperCAmelCase     )
				logger.info("""Best evaluation result: %s = %f"""       , args.eval_metric       , _UpperCAmelCase     )
				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						shutil.copy(
						    os.path.join(_UpperCAmelCase       , f'''eval_results_iter-{iteration}.json'''     )       , os.path.join(_UpperCAmelCase       , """eval_results_best-iteration.json"""     )       , )
		else:
				# Assume that the last iteration is the best
				logger.info("""Best iteration: %d"""       , args.max_selftrain_iterations - 1     )
				logger.info("""Best evaluation result: %s = %f"""       , args.eval_metric       , _UpperCAmelCase     )
				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						shutil.copy(
						    os.path.join(_UpperCAmelCase       , f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''     )       , os.path.join(_UpperCAmelCase       , """eval_results_best-iteration.json"""     )       , )




'''simple docstring'''





import pickle

import numpy as np
from matplotlib import pyplot as plt



class  snake_case__						:
 def __init__(   self						:							List[Any]      ,     __a						:							str      ,     __a						:							Dict      ,     __a						:							List[Any]      ,     __a						:							str      ,     __a						:							str      ,     __a						:							List[str]=0.2      ,     __a						:							Any=0.2  )      ->   Any:







       '''simple docstring'''
       __snake_case :   Any													=    bp_numa
       __snake_case :   str													=    bp_numa
       __snake_case :   Optional[Any]													=    bp_numa
       __snake_case :   Any													=    conva_get[:2]
       __snake_case :   Dict													=    conva_get[2]
       __snake_case :   Optional[int]													=    size_pa
       __snake_case :   str													=    rate_w
       __snake_case :   Optional[Any]													=    rate_t
       __snake_case :   Optional[Any]													=    [
           np.mat(-1 * np.random.rand(self.conva[0]      ,     self.conva[0]  ) + 0.5  )
           for i in range(self.conva[1]  )
       ]
       __snake_case :   List[Any]													=    np.mat(-1 * np.random.rand(self.num_bpa      ,     self.num_bpa  ) + 0.5  )
       __snake_case :   Any													=    np.mat(-1 * np.random.rand(self.num_bpa      ,     self.num_bpa  ) + 0.5  )
       __snake_case :   Optional[int]													=    -2 * np.random.rand(self.conva[1]  ) + 1
       __snake_case :   Optional[Any]													=    -2 * np.random.rand(self.num_bpa  ) + 1
       __snake_case :   str													=    -2 * np.random.rand(self.num_bpa  ) + 1
 def A_   (   self						:							int      ,     __a						:							Dict  )      ->   Optional[Any]:







       '''simple docstring'''
       # save model dict with pickle
       __snake_case :   int													=    {
           '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 A_   (   cls						:							List[Any]      ,     __a						:							Union[str, Any]  )      ->   int:







       '''simple docstring'''
       # read saved model
       with open(__a      ,     'rb'  ) as f:
             __snake_case :   Dict													=    pickle.load(__a  )  # noqa: S301

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







       '''simple docstring'''
       return 1 / (1 + np.exp(-1 * x  ))
 def A_   (   self						:							Any      ,     __a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''
       return round(__a      ,     3  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							Tuple      ,     __a						:							List[str]      ,     __a						:							Dict      ,     __a						:							Optional[int]      ,     __a						:							List[str]  )      ->   str:







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

       # expanding the data slice to One dimenssion
       __snake_case :   Union[str, Any]													=    []
       for each_focus in data_focus:
             focusa_list.extend(self.Expand_Mat(__a  )  )
       __snake_case :   List[Any]													=    np.asarray(__a  )
       return focus_list, data_featuremap
 def A_   (   self						:							Any      ,     __a						:							int      ,     __a						:							Tuple      ,     __a						:							List[Any]="average_pool"  )      ->   Dict:







       '''simple docstring'''
       # pooling process
       __snake_case :   List[str]													=    len(featuremaps[0]  )
       __snake_case :   Tuple													=    int(size_map / size_pooling  )
       __snake_case :   int													=    []
       for i_map in range(len(__a  )  ):
             __snake_case :   str													=    featuremaps[i_map]
             __snake_case :   Optional[Any]													=    []
             for i_focus in range(0      ,     __a      ,     __a  ):
                   for j_focus in range(0      ,     __a      ,     __a  ):
                         __snake_case :   Dict													=    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  )  )
             __snake_case :   List[str]													=    np.asmatrix(__a  ).reshape(__a      ,     __a  )
             featuremap_pooled.append(__a  )
       return featuremap_pooled
 def A_   (   self						:							List[str]      ,     __a						:							Union[str, Any]  )      ->   int:







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







       '''simple docstring'''
       # expanding matrix to one dimension list
       __snake_case :   int													=    np.asarray(__a  )
       __snake_case :   str													=    np.shape(__a  )
       __snake_case :   Any													=    data_mat.reshape(1      ,     shapes[0] * shapes[1]  )
       return data_expanded
 def A_   (   self						:							List[Any]      ,     __a						:							str      ,     __a						:							Optional[int]      ,     __a						:							List[str]      ,     __a						:							int      ,     __a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    []
       __snake_case :   Tuple													=    0
       for i_map in range(__a  ):
             __snake_case :   Union[str, Any]													=    np.ones((size_map, size_map)  )
             for i in range(0      ,     __a      ,     __a  ):
                   for j in range(0      ,     __a      ,     __a  ):
                         __snake_case :   Any													=    pd_pool[
                             i_pool
                         ]
                         __snake_case :   List[Any]													=    i_pool + 1
             __snake_case :   List[Any]													=    np.multiply(
                 __a      ,     np.multiply(out_map[i_map]      ,     (1 - out_map[i_map])  )  )
             pd_all.append(__a  )
       return pd_all
 def A_   (   self						:							Tuple      ,     __a						:							List[str]      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]      ,     __a						:							Dict      ,     __a						:							Tuple      ,     __a						:							Optional[int]=bool  )      ->   List[Any]:







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

                   __snake_case :   List[Any]													=    np.dot(__a      ,     self.vji.T  ) - self.thre_bpa
                   __snake_case :   Any													=    self.sig(__a  )
                   __snake_case :   Tuple													=    np.dot(__a      ,     self.wkj.T  ) - self.thre_bpa
                   __snake_case :   Optional[Any]													=    self.sig(__a  )

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

                   __snake_case :   Optional[int]													=    pd_i_all / (self.size_poolinga * self.size_poolinga)
                   __snake_case :   Tuple													=    pd_conva_pooled.T.getA().tolist()
                   __snake_case :   Optional[Any]													=    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]  ):
                         __snake_case :   int													=    self._expand_mat(pd_conva_all[k_conv]  )
                         __snake_case :   Optional[int]													=    self.rate_weight * np.dot(__a      ,     __a  )

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

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

       def draw_error():
             __snake_case :   Union[str, Any]													=    [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 A_   (   self						:							Tuple      ,     __a						:							Union[str, Any]  )      ->   List[Any]:







       '''simple docstring'''
       # model predict
       __snake_case :   str													=    []
       print('-------------------Start Testing-------------------------'  )
       print((' - - Shape: Test_Data  ', np.shape(__a  ))  )
       for p in range(len(__a  )  ):
             __snake_case :   int													=    np.asmatrix(datas_test[p]  )
             __snake_case							,     __snake_case :   str													=    self.convolute(
                 __a      ,     self.conva      ,     self.w_conva      ,     self.thre_conva      ,     conv_step=self.step_conva      ,     )
             __snake_case :   List[str]													=    self.pooling(__a      ,     self.size_poolinga  )
             __snake_case :   List[Any]													=    self._expand(__a  )

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





 def A_   (   self						:							Optional[Any]      ,     __a						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       # return the data of image after convoluting process so we can check it out
       __snake_case :   int													=    np.asmatrix(__a  )
       __snake_case							,     __snake_case :   int													=    self.convolute(
           __a      ,     self.conva      ,     self.w_conva      ,     self.thre_conva      ,     conv_step=self.step_conva      ,     )
       __snake_case :   Dict													=    self.pooling(__a      ,     self.size_poolinga  )

       return data_conveda, data_pooleda


if __name__ == "__main__":
    pass



