





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
SCREAMING_SNAKE_CASE__							:      str         =    logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-classification/requirements.txt""")

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
SCREAMING_SNAKE_CASE__							:      str         =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def    _A	(   lowerCamelCase ):
   with open(lowerCamelCase				,			"rb" ) as f:
      a__						:    Optional[Any]   		=	Image.open(lowerCamelCase )
      return im.convert("RGB" )







@dataclass
class 	__lowerCAmelCase      :
   _UpperCamelCase			:    Optional[str]  		=					field(
       default=_UpperCamelCase		,metadata={
           """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
       }		,)
   _UpperCamelCase			:    Optional[str]  		=					field(
       default=_UpperCamelCase		,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}	)
   _UpperCamelCase			:    Optional[str]  		=					field(default=_UpperCamelCase		,metadata={"""help""": """A folder containing the training data."""}	)
   _UpperCamelCase			:    Optional[str]  		=					field(default=_UpperCamelCase		,metadata={"""help""": """A folder containing the validation data."""}	)
   _UpperCamelCase			:    Optional[float]  		=					field(
       default=0.15		,metadata={"""help""": """Percent to split off of train for validation."""}	)
   _UpperCamelCase			:    Optional[int]  		=					field(
       default=_UpperCamelCase		,metadata={
           """help""": (
               """For debugging purposes or quicker training, truncate the number of training examples to this """
               """value if set."""
           )
       }		,)
   _UpperCamelCase			:    Optional[int]  		=					field(
       default=_UpperCamelCase		,metadata={
           """help""": (
               """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
               """value if set."""
           )
       }		,)





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
         raise ValueError(
             "You must specify either a dataset name from the hub or a train and/or validation directory."   )







@dataclass
class 	__lowerCAmelCase      :
   _UpperCamelCase			:    str  		=					field(
       default="""google/vit-base-patch16-224-in21k"""		,metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		,)
   _UpperCamelCase			:    Optional[str]  		=					field(
       default=_UpperCamelCase		,metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_UpperCamelCase	)}		,)
   _UpperCamelCase			:    Optional[str]  		=					field(
       default=_UpperCamelCase		,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
   _UpperCamelCase			:    Optional[str]  		=					field(
       default=_UpperCamelCase		,metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}	)
   _UpperCamelCase			:    str  		=					field(
       default="""main"""		,metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}		,)
   _UpperCamelCase			:    str  		=					field(default=_UpperCamelCase		,metadata={"""help""": """Name or path of preprocessor config."""}	)
   _UpperCamelCase			:    bool  		=					field(
       default=_UpperCamelCase		,metadata={
           """help""": (
               """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
               """with private models)."""
           )
       }		,)
   _UpperCamelCase			:    bool  		=					field(
       default=_UpperCamelCase		,metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}		,)







def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	torch.stack([example["pixel_values"] for example in examples] )
   a__						:    List[Any]   		=	torch.tensor([example["labels"] for example in examples] )
   return {"pixel_values": pixel_values, "labels": labels}






def    _A	(   ):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   a__						:    Tuple   		=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
   if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      a__		,  a__		,  a__						:    List[str]   		=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
   else:
      a__		,  a__		,  a__						:    Optional[Any]   		=	parser.parse_args_into_dataclasses()

   # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
   # information sent is the one passed as arguments along with your Python/PyTorch versions.
   send_example_telemetry("run_image_classification"				,			lowerCamelCase				,			lowerCamelCase )

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,			datefmt="%m/%d/%Y %H:%M:%S"				,			handlers=[logging.StreamHandler(sys.stdout )]				,			)

   if training_args.should_log:
      # The default of training_args.log_level is passive, so we set log level at info here to have that default.
      transformers.utils.logging.set_verbosity_info()

   a__						:    Optional[int]   		=	training_args.get_process_log_level()
   logger.setLevel(lowerCamelCase )
   transformers.utils.logging.set_verbosity(lowerCamelCase )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()

   # Log on each process the small summary:
   logger.warning(
       F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
       + F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
   logger.info(F"""Training/evaluation parameters {training_args}""" )

   # Detecting last checkpoint.
   a__						:    Optional[int]   		=	None
   if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
      a__						:    Any   		=	get_last_checkpoint(training_args.output_dir )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
         raise ValueError(
             F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
             "Use --overwrite_output_dir to overcome." )
      elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
         logger.info(
             F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
             "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )

    # Set seed before initializing model.
   set_seed(training_args.seed )

   # Initialize our dataset and prepare it for the 'image-classification' task.
   if data_args.dataset_name is not None:
      a__						:    str   		=	load_dataset(
          data_args.dataset_name				,			data_args.dataset_config_name				,			cache_dir=model_args.cache_dir				,			task="image-classification"				,			use_auth_token=True if model_args.use_auth_token else None				,			)
   else:
      a__						:    Tuple   		=	{}
      if data_args.train_dir is not None:
         a__						:    Optional[int]   		=	os.path.join(data_args.train_dir				,			"**" )
      if data_args.validation_dir is not None:
         a__						:    Tuple   		=	os.path.join(data_args.validation_dir				,			"**" )
      a__						:    Optional[int]   		=	load_dataset(
          "imagefolder"				,			data_files=lowerCamelCase				,			cache_dir=model_args.cache_dir				,			task="image-classification"				,			)

   # If we don't have a validation split, split off a percentage of train as validation.
   a__						:    int   		=	None if "validation" in dataset.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split				,			lowerCamelCase ) and data_args.train_val_split > 0.0:
      a__						:    Tuple   		=	dataset["train"].train_test_split(data_args.train_val_split )
      a__						:    Dict   		=	split["train"]
      a__						:    List[str]   		=	split["test"]

   # Prepare label mappings.
   # We'll include these in the model's config to get human readable labels in the Inference API.
   a__						:    int   		=	dataset["train"].features["labels"].names
   a__		,  a__						:    Optional[Any]   		=	{}, {}
   for i, label in enumerate(lowerCamelCase ):
      a__						:    Union[str, Any]   		=	str(lowerCamelCase )
      a__						:    List[str]   		=	label

   # Load the accuracy metric from the datasets package
   a__						:    Any   		=	evaluate.load("accuracy" )

   # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
   # predictions and label_ids field) and has to return a dictionary string to float.
   def compute_metrics(lowerCamelCase ):
      return metric.compute(predictions=np.argmax(p.predictions				,			axis=1 )				,			references=p.label_ids )

   a__						:    str   		=	AutoConfig.from_pretrained(
       model_args.config_name or model_args.model_name_or_path				,			num_labels=len(lowerCamelCase )				,			labelaid=lowerCamelCase				,			idalabel=lowerCamelCase				,			finetuning_task="image-classification"				,			cache_dir=model_args.cache_dir				,			revision=model_args.model_revision				,			use_auth_token=True if model_args.use_auth_token else None				,			)
   a__						:    Optional[int]   		=	AutoModelForImageClassification.from_pretrained(
       model_args.model_name_or_path				,			from_tf=bool(".ckpt" in model_args.model_name_or_path )				,			config=lowerCamelCase				,			cache_dir=model_args.cache_dir				,			revision=model_args.model_revision				,			use_auth_token=True if model_args.use_auth_token else None				,			ignore_mismatched_sizes=model_args.ignore_mismatched_sizes				,			)
   a__						:    Any   		=	AutoImageProcessor.from_pretrained(
       model_args.image_processor_name or model_args.model_name_or_path				,			cache_dir=model_args.cache_dir				,			revision=model_args.model_revision				,			use_auth_token=True if model_args.use_auth_token else None				,			)

   # Define torchvision transforms to be applied to each image.
   if "shortest_edge" in image_processor.size:
      a__						:    Optional[int]   		=	image_processor.size["shortest_edge"]
   else:
      a__						:    str   		=	(image_processor.size["height"], image_processor.size["width"])
   a__						:    Union[str, Any]   		=	Normalize(mean=image_processor.image_mean				,			std=image_processor.image_std )
   a__						:    Tuple   		=	Compose(
       [
           RandomResizedCrop(lowerCamelCase ),
           RandomHorizontalFlip(),
           ToTensor(),
           normalize,
       ] )
   a__						:    str   		=	Compose(
       [
           Resize(lowerCamelCase ),
           CenterCrop(lowerCamelCase ),
           ToTensor(),
           normalize,
       ] )

   def train_transforms(lowerCamelCase ):
      a__						:    Optional[int]   		=	[
          _train_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]
      ]
      return example_batch

   def val_transforms(lowerCamelCase ):
      a__						:    Dict   		=	[_val_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]]
      return example_batch

   if training_args.do_train:
      if "train" not in dataset:
         raise ValueError("--do_train requires a train dataset" )
      if data_args.max_train_samples is not None:
         a__						:    int   		=	(
             dataset["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
         )
      # Set the training transforms
      dataset["train"].set_transform(lowerCamelCase )

   if training_args.do_eval:
      if "validation" not in dataset:
         raise ValueError("--do_eval requires a validation dataset" )
      if data_args.max_eval_samples is not None:
         a__						:    List[str]   		=	(
             dataset["validation"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
         )
      # Set the validation transforms
      dataset["validation"].set_transform(lowerCamelCase )

   # Initalize our trainer
   a__						:    List[Any]   		=	Trainer(
       model=lowerCamelCase				,			args=lowerCamelCase				,			train_dataset=dataset["train"] if training_args.do_train else None				,			eval_dataset=dataset["validation"] if training_args.do_eval else None				,			compute_metrics=lowerCamelCase				,			tokenizer=lowerCamelCase				,			data_collator=lowerCamelCase				,			)

   # Training
   if training_args.do_train:
      a__						:    str   		=	None
      if training_args.resume_from_checkpoint is not None:
         a__						:    List[Any]   		=	training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         a__						:    str   		=	last_checkpoint
      a__						:    Optional[Any]   		=	trainer.train(resume_from_checkpoint=lowerCamelCase )
      trainer.save_model()
      trainer.log_metrics("train"				,			train_result.metrics )
      trainer.save_metrics("train"				,			train_result.metrics )
      trainer.save_state()

   # Evaluation
   if training_args.do_eval:
      a__						:    List[Any]   		=	trainer.evaluate()
      trainer.log_metrics("eval"				,			lowerCamelCase )
      trainer.save_metrics("eval"				,			lowerCamelCase )

   # Write model card and (optionally) push to hub
   a__						:    Union[str, Any]   		=	{
       "finetuned_from": model_args.model_name_or_path,
       "tasks": "image-classification",
       "dataset": data_args.dataset_name,
       "tags": ["image-classification", "vision"],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**lowerCamelCase )
   else:
      trainer.create_model_card(**lowerCamelCase )


if __name__ == "__main__":
  main()








import pickle

import numpy as np
from matplotlib import pyplot as plt







class 	__lowerCAmelCase      :





   def __init__(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case=0.2       ,      snake_case=0.2   )					->	Any:
      """simple docstring"""





      a__						:    List[Any]   		=	bp_numa
      a__						:    str   		=	bp_numa
      a__						:    Any   		=	bp_numa
      a__						:    Tuple   		=	conva_get[:2]
      a__						:    Optional[int]   		=	conva_get[2]
      a__						:    Any   		=	size_pa
      a__						:    Dict   		=	rate_w
      a__						:    Tuple   		=	rate_t
      a__						:    int   		=	[
          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__						:    List[Any]   		=	np.mat(-1 * np.random.rand(self.num_bpa       ,      self.num_bpa   ) + 0.5   )
      a__						:    Union[str, Any]   		=	-2 * np.random.rand(self.conva[1]   ) + 1
      a__						:    Optional[int]   		=	-2 * np.random.rand(self.num_bpa   ) + 1
      a__						:    int   		=	-2 * np.random.rand(self.num_bpa   ) + 1





   def 						_snake_case			(			self       ,      snake_case   )					->	int:
      """simple docstring"""





      a__						:    str   		=	{
          "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(snake_case       ,      "wb"   ) as f:
         pickle.dump(snake_case       ,      snake_case   )

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





   @classmethod
   def 						_snake_case			(			cls       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





      with open(snake_case       ,      "rb"   ) as f:
         a__						:    List[str]   		=	pickle.load(snake_case   )  # noqa: S301

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





   def 						_snake_case			(			self       ,      snake_case   )					->	Optional[int]:
      """simple docstring"""





      return 1 / (1 + np.exp(-1 * x   ))





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      return round(snake_case       ,      3   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Optional[int]:
      """simple docstring"""





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

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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case="average_pool"   )					->	Tuple:
      """simple docstring"""





      a__						:    Dict   		=	len(featuremaps[0]   )
      a__						:    Optional[int]   		=	int(size_map / size_pooling   )
      a__						:    Any   		=	[]
      for i_map in range(len(snake_case   )   ):
         a__						:    List[str]   		=	featuremaps[i_map]
         a__						:    Optional[Any]   		=	[]
         for i_focus in range(0       ,      snake_case       ,      snake_case   ):
            for j_focus in range(0       ,      snake_case       ,      snake_case   ):
               a__						:    Tuple   		=	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(snake_case   )   )
               elif pooling_type == "max_pooling":
                  # max pooling
                  map_pooled.append(np.max(snake_case   )   )
         a__						:    Dict   		=	np.asmatrix(snake_case   ).reshape(snake_case       ,      snake_case   )
         featuremap_pooled.append(snake_case   )
      return featuremap_pooled





   def 						_snake_case			(			self       ,      snake_case   )					->	Optional[Any]:
      """simple docstring"""





      a__						:    Optional[Any]   		=	[]
      for i in range(len(snake_case   )   ):
         a__						:    int   		=	np.shape(data[i]   )
         a__						:    Union[str, Any]   		=	data[i].reshape(1       ,      shapes[0] * shapes[1]   )
         a__						:    str   		=	data_listed.getA().tolist()[0]
         data_expanded.extend(snake_case   )
      a__						:    Any   		=	np.asarray(snake_case   )
      return data_expanded





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      a__						:    Dict   		=	np.asarray(snake_case   )
      a__						:    List[Any]   		=	np.shape(snake_case   )
      a__						:    Optional[Any]   		=	data_mat.reshape(1       ,      shapes[0] * shapes[1]   )
      return data_expanded





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Any:
      """simple docstring"""





      a__						:    Dict   		=	[]
      a__						:    List[Any]   		=	0
      for i_map in range(snake_case   ):
         a__						:    Tuple   		=	np.ones((size_map, size_map)   )
         for i in range(0       ,      snake_case       ,      snake_case   ):
            for j in range(0       ,      snake_case       ,      snake_case   ):
               a__						:    int   		=	pd_pool[
                   i_pool
               ]
               a__						:    Any   		=	i_pool + 1
         a__						:    Optional[Any]   		=	np.multiply(
             snake_case       ,      np.multiply(out_map[i_map]       ,      (1 - out_map[i_map])   )   )
         pd_all.append(snake_case   )
      return pd_all





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case=bool   )					->	Optional[int]:
      """simple docstring"""





      print("----------------------Start Training-------------------------"   )
      print((" - - Shape: Train_Data  ", np.shape(snake_case   ))   )
      print((" - - Shape: Teach_Data  ", np.shape(snake_case   ))   )
      a__						:    List[Any]   		=	0
      a__						:    Any   		=	[]
      a__						:    Dict   		=	10_000
      while rp < n_repeat and mse >= error_accuracy:
         a__						:    Optional[Any]   		=	0
         print(F"""-------------Learning Time {rp}--------------"""   )
         for p in range(len(snake_case   )   ):
            # print('------------Learning Image: %d--------------'%p)
            a__						:    Optional[int]   		=	np.asmatrix(datas_train[p]   )
            a__						:    Optional[Any]   		=	np.asarray(datas_teach[p]   )
            a__		,  a__						:    Tuple   		=	self.convolute(
                snake_case       ,      self.conva       ,      self.w_conva       ,      self.thre_conva       ,      conv_step=self.step_conva       ,      )
            a__						:    List[Any]   		=	self.pooling(snake_case       ,      self.size_poolinga   )
            a__						:    str   		=	np.shape(snake_case   )
            a__						:    List[Any]   		=	self._expand(snake_case   )
            a__						:    Union[str, Any]   		=	data_bp_input

            a__						:    Optional[Any]   		=	np.dot(snake_case       ,      self.vji.T   ) - self.thre_bpa
            a__						:    Optional[int]   		=	self.sig(snake_case   )
            a__						:    Union[str, Any]   		=	np.dot(snake_case       ,      self.wkj.T   ) - self.thre_bpa
            a__						:    Dict   		=	self.sig(snake_case   )

            # --------------Model Leaning ------------------------
            # calculate error and gradient---------------
            a__						:    str   		=	np.multiply(
                (data_teach - bp_outa)       ,      np.multiply(snake_case       ,      (1 - bp_outa)   )   )
            a__						:    List[Any]   		=	np.multiply(
                np.dot(snake_case       ,      self.wkj   )       ,      np.multiply(snake_case       ,      (1 - bp_outa)   )   )
            a__						:    List[str]   		=	np.dot(snake_case       ,      self.vji   )

            a__						:    List[str]   		=	pd_i_all / (self.size_poolinga * self.size_poolinga)
            a__						:    Tuple   		=	pd_conva_pooled.T.getA().tolist()
            a__						:    List[str]   		=	self._calculate_gradient_from_pool(
                snake_case       ,      snake_case       ,      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__						:    Dict   		=	self._expand_mat(pd_conva_all[k_conv]   )
               a__						:    Optional[int]   		=	self.rate_weight * np.dot(snake_case       ,      snake_case   )

               a__						:    Dict   		=	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__						:    int   		=	self.wkj + pd_k_all.T * bp_outa * self.rate_weight
            a__						:    Optional[Any]   		=	self.vji + pd_j_all.T * bp_outa * self.rate_weight
            a__						:    List[str]   		=	self.thre_bpa - pd_k_all * self.rate_thre
            a__						:    int   		=	self.thre_bpa - pd_j_all * self.rate_thre
            # calculate the sum error of all single image
            a__						:    int   		=	np.sum(abs(data_teach - bp_outa   )   )
            error_count += errors
            # print('   ----Teach      ',data_teach)
            # print('   ----BP_output  ',bp_out3)
         a__						:    Union[str, Any]   		=	rp + 1
         a__						:    List[Any]   		=	error_count / patterns
         all_mse.append(snake_case   )

      def draw_error():
         a__						:    List[Any]   		=	[error_accuracy for i in range(int(n_repeat * 1.2   )   )]
         plt.plot(snake_case       ,      "+-"   )
         plt.plot(snake_case       ,      "r--"   )
         plt.xlabel("Learning Times"   )
         plt.ylabel("All_mse"   )
         plt.grid(snake_case       ,      alpha=0.5   )
         plt.show()

      print("------------------Training Complished---------------------"   )
      print((" - - Training epoch: ", rp, F"""     - - Mse: {mse:.6f}""")   )
      if draw_e:
         draw_error()
      return mse





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      a__						:    int   		=	[]
      print("-------------------Start Testing-------------------------"   )
      print((" - - Shape: Test_Data  ", np.shape(snake_case   ))   )
      for p in range(len(snake_case   )   ):
         a__						:    Union[str, Any]   		=	np.asmatrix(datas_test[p]   )
         a__		,  a__						:    Union[str, Any]   		=	self.convolute(
             snake_case       ,      self.conva       ,      self.w_conva       ,      self.thre_conva       ,      conv_step=self.step_conva       ,      )
         a__						:    List[Any]   		=	self.pooling(snake_case       ,      self.size_poolinga   )
         a__						:    List[str]   		=	self._expand(snake_case   )

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





   def 						_snake_case			(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    Dict   		=	np.asmatrix(snake_case   )
      a__		,  a__						:    Optional[int]   		=	self.convolute(
          snake_case       ,      self.conva       ,      self.w_conva       ,      self.thre_conva       ,      conv_step=self.step_conva       ,      )
      a__						:    Any   		=	self.pooling(snake_case       ,      self.size_poolinga   )

      return data_conveda, data_pooleda


if __name__ == "__main__":
  pass

