



'''simple docstring'''

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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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
snake_case_			=     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-pretraining/requirements.txt""")


@dataclass
class 	a__     :
	__magic_name__		:    Optional[str]	    =     field(
	    default="cifar10",			metadata={"help": "Name of a dataset from the datasets package"}	)
	__magic_name__		:    Optional[str]	    =     field(
	    default=_lowercase,			metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}	)
	__magic_name__		:    Optional[str]	    =     field(
	    default=_lowercase,			metadata={"help": "The column name of the images in the files."}	)
	__magic_name__		:    Optional[str]	    =     field(default=_lowercase,			metadata={"help": "A folder containing the training data."}	)
	__magic_name__		:    Optional[str]	    =     field(default=_lowercase,			metadata={"help": "A folder containing the validation data."}	)
	__magic_name__		:    Optional[float]	    =     field(
	    default=0.15,			metadata={"help": "Percent to split off of train for validation."}	)
	__magic_name__		:    Optional[int]	    =     field(
	    default=_lowercase,			metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of training examples to this "
	            "value if set."
	        )
	    },			)
	__magic_name__		:    Optional[int]	    =     field(
	    default=_lowercase,			metadata={
	        "help": (
	            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
	            "value if set."
	        )
	    },			)


	def 				lowercase__  (self : Dict      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= {}
					if self.train_dir is not None:
									SCREAMING_SNAKE_CASE  :						Dict      					= self.train_dir
					if self.validation_dir is not None:
									SCREAMING_SNAKE_CASE  :						Tuple      					= self.validation_dir
					SCREAMING_SNAKE_CASE  :						Any      					= data_files if data_files else None



@dataclass
class 	a__     :
	__magic_name__		:    str	    =     field(
	    default=_lowercase,			metadata={
	        "help": (
	            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
	        )
	    },			)
	__magic_name__		:    Optional[str]	    =     field(
	    default=_lowercase,			metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}	)
	__magic_name__		:    Optional[str]	    =     field(
	    default=_lowercase,			metadata={
	        "help": (
	            "Override some existing default config settings when a model is trained from scratch. Example: "
	            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
	        )
	    },			)
	__magic_name__		:    Optional[str]	    =     field(
	    default=_lowercase,			metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}	)
	__magic_name__		:    str	    =     field(
	    default="main",			metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},			)
	__magic_name__		:    str	    =     field(default=_lowercase,			metadata={"help": "Name or path of preprocessor config."}	)
	__magic_name__		:    bool	    =     field(
	    default=_lowercase,			metadata={
	        "help": (
	            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
	            "with private models)."
	        )
	    },			)
	__magic_name__		:    float	    =     field(
	    default=0.75,			metadata={"help": "The ratio of the number of masked tokens in the input sequence."}	)
	__magic_name__		:    bool	    =     field(
	    default=_lowercase,			metadata={"help": "Whether or not to train with normalized pixel values as target."}	)



@dataclass
class 	a__     (				_lowercase	):
	__magic_name__		:    float	    =     field(
	    default=1E-3,			metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}	)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any       ):
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.stack([example['''pixel_values'''] for example in examples]       )
				return {"pixel_values": pixel_values}






def       __lowercase  ():
				# 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.

				SCREAMING_SNAKE_CASE  :						Tuple      					= HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)       )
				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.
								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						str      					= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
				else:
								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= 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_mae'''    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				# 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()

				SCREAMING_SNAKE_CASE  :						List[str]      					= training_args.get_process_log_level()
				logger.setLevel(_SCREAMING_SNAKE_CASE       )
				transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE       )
				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.
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= None
				if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
								SCREAMING_SNAKE_CASE  :						int      					= 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.'''       )

    # Initialize our dataset.
				SCREAMING_SNAKE_CASE  :						int      					= load_dataset(
				    data_args.dataset_name    ,	data_args.dataset_config_name    ,	data_files=data_args.data_files    ,	cache_dir=model_args.cache_dir    ,	use_auth_token=True if model_args.use_auth_token else None    ,	)

				# If we don't have a validation split, split off a percentage of train as validation.
				SCREAMING_SNAKE_CASE  :						Any      					= None if '''validation''' in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split    ,	_SCREAMING_SNAKE_CASE       ) and data_args.train_val_split > 0.0:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= ds['''train'''].train_test_split(data_args.train_val_split       )
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= split['''train''']
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= split['''test''']

				# Load pretrained model and image processor
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				SCREAMING_SNAKE_CASE  :						Tuple      					= {
				    '''cache_dir''': model_args.cache_dir,
				    '''revision''': model_args.model_revision,
				    '''use_auth_token''': True if model_args.use_auth_token else None,
				}
				if model_args.config_name:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= ViTMAEConfig.from_pretrained(model_args.config_name    ,	**_SCREAMING_SNAKE_CASE       )
				elif model_args.model_name_or_path:
								SCREAMING_SNAKE_CASE  :						Tuple      					= ViTMAEConfig.from_pretrained(model_args.model_name_or_path    ,	**_SCREAMING_SNAKE_CASE       )
				else:
								SCREAMING_SNAKE_CASE  :						List[str]      					= ViTMAEConfig()
								logger.warning('''You are instantiating a new config instance from scratch.'''       )
								if model_args.config_overrides is not None:
												logger.info(F'''Overriding config: {model_args.config_overrides}'''       )
												config.update_from_string(model_args.config_overrides       )
												logger.info(F'''New config: {config}'''       )

    # adapt config
				config.update(
				    {
				        '''mask_ratio''': model_args.mask_ratio,
				        '''norm_pix_loss''': model_args.norm_pix_loss,
				    }       )

				# create image processor
				if model_args.image_processor_name:
								SCREAMING_SNAKE_CASE  :						Dict      					= ViTImageProcessor.from_pretrained(model_args.image_processor_name    ,	**_SCREAMING_SNAKE_CASE       )
				elif model_args.model_name_or_path:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ViTImageProcessor.from_pretrained(model_args.model_name_or_path    ,	**_SCREAMING_SNAKE_CASE       )
				else:
								SCREAMING_SNAKE_CASE  :						List[str]      					= ViTImageProcessor()

				# create model
				if model_args.model_name_or_path:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= ViTMAEForPreTraining.from_pretrained(
								    model_args.model_name_or_path    ,	from_tf=bool('''.ckpt''' in model_args.model_name_or_path       )    ,	config=_SCREAMING_SNAKE_CASE    ,	cache_dir=model_args.cache_dir    ,	revision=model_args.model_revision    ,	use_auth_token=True if model_args.use_auth_token else None    ,	)
				else:
								logger.info('''Training new model from scratch'''       )
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE       )

				if training_args.do_train:
								SCREAMING_SNAKE_CASE  :						str      					= ds['''train'''].column_names
				else:
								SCREAMING_SNAKE_CASE  :						str      					= ds['''validation'''].column_names

				if data_args.image_column_name is not None:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= data_args.image_column_name
				elif "image" in column_names:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= '''image'''
				elif "img" in column_names:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''img'''
				else:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= column_names[0]

				# transformations as done in original MAE paper
				# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
				if "shortest_edge" in image_processor.size:
								SCREAMING_SNAKE_CASE  :						Any      					= image_processor.size['''shortest_edge''']
				else:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= (image_processor.size['''height'''], image_processor.size['''width'''])
				SCREAMING_SNAKE_CASE  :						str      					= Compose(
				    [
				        Lambda(lambda _SCREAMING_SNAKE_CASE       : img.convert('''RGB'''       ) if img.mode != "RGB" else img       ),
				        RandomResizedCrop(_SCREAMING_SNAKE_CASE    ,	scale=(0.2, 1.0)    ,	interpolation=InterpolationMode.BICUBIC       ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean    ,	std=image_processor.image_std       ),
				    ]       )

				def preprocess_images(_SCREAMING_SNAKE_CASE      :Dict       ):
								SCREAMING_SNAKE_CASE  :						Any      					= [transforms(_SCREAMING_SNAKE_CASE       ) for image in examples[image_column_name]]
								return examples

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

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

				# Compute absolute learning rate
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= (
				    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
				)
				if training_args.base_learning_rate is not None:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= training_args.base_learning_rate * total_train_batch_size / 2_56

				# Initialize our trainer
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= Trainer(
				    model=_SCREAMING_SNAKE_CASE    ,	args=_SCREAMING_SNAKE_CASE    ,	train_dataset=ds['''train'''] if training_args.do_train else None    ,	eval_dataset=ds['''validation'''] if training_args.do_eval else None    ,	tokenizer=_SCREAMING_SNAKE_CASE    ,	data_collator=_SCREAMING_SNAKE_CASE    ,	)

				# Training
				if training_args.do_train:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= None
								if training_args.resume_from_checkpoint is not None:
												SCREAMING_SNAKE_CASE  :						Tuple      					= training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												SCREAMING_SNAKE_CASE  :						List[Any]      					= last_checkpoint
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE       )
								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:
								SCREAMING_SNAKE_CASE  :						Tuple      					= trainer.evaluate()
								trainer.log_metrics('''eval'''    ,	_SCREAMING_SNAKE_CASE       )
								trainer.save_metrics('''eval'''    ,	_SCREAMING_SNAKE_CASE       )

				# Write model card and (optionally) push to hub
				SCREAMING_SNAKE_CASE  :						List[Any]      					= {
				    '''tasks''': '''masked-auto-encoding''',
				    '''dataset''': data_args.dataset_name,
				    '''tags''': ['''masked-auto-encoding'''],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**_SCREAMING_SNAKE_CASE       )
				else:
								trainer.create_model_card(**_SCREAMING_SNAKE_CASE       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
	main()









'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt


class 	a__     :


	def __init__(self : Any,					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : List[str]=0.2,					__UpperCAmelCase : Dict=0.2      )     ->			List[Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[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(__UpperCAmelCase,					'''wb'''      ) as f:
									pickle.dump(__UpperCAmelCase,					__UpperCAmelCase      )

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


	@classmethod
	def 				lowercase__  (cls : str,					__UpperCAmelCase : List[str]      )     ->			int:

					"""simple docstring"""

					with open(__UpperCAmelCase,					'''rb'''      ) as f:
									SCREAMING_SNAKE_CASE  :						int      					= pickle.load(__UpperCAmelCase      )  # noqa: S301

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


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Dict      )     ->			Optional[Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : List[str]      )     ->			str:

					"""simple docstring"""

					return round(__UpperCAmelCase,					3      )


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Any,					__UpperCAmelCase : int      )     ->			List[Any]:

					"""simple docstring"""

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

					# expanding the data slice to One dimenssion
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
					for each_focus in data_focus:
									focusa_list.extend(self.Expand_Mat(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asarray(__UpperCAmelCase      )
					return focus_list, data_featuremap


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[str]="average_pool"      )     ->			List[Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Any,					__UpperCAmelCase : List[str]      )     ->			Optional[int]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= np.asarray(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= np.shape(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= data_mat.reshape(1,					shapes[0] * shapes[1]      )
					return data_expanded


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Dict,					__UpperCAmelCase : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Union[str, Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= []
					SCREAMING_SNAKE_CASE  :						List[str]      					= 0
					for i_map in range(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Dict      					= np.ones((size_map, size_map)      )
									for i in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
													for j in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
																	SCREAMING_SNAKE_CASE  :						str      					= pd_pool[
																	    i_pool
																	]
																	SCREAMING_SNAKE_CASE  :						Dict      					= i_pool + 1
									SCREAMING_SNAKE_CASE  :						List[Any]      					= np.multiply(
									    __UpperCAmelCase,					np.multiply(out_map[i_map],					(1 - out_map[i_map])      )      )
									pd_all.append(__UpperCAmelCase      )
					return pd_all


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Optional[int]=bool      )     ->			List[Any]:

					"""simple docstring"""

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

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

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

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

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

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

					def draw_error():
									SCREAMING_SNAKE_CASE  :						Tuple      					= [error_accuracy for i in range(int(n_repeat * 1.2      )      )]
									plt.plot(__UpperCAmelCase,					'''+-'''      )
									plt.plot(__UpperCAmelCase,					'''r--'''      )
									plt.xlabel('''Learning Times'''      )
									plt.ylabel('''All_mse'''      )
									plt.grid(__UpperCAmelCase,					alpha=0.5      )
									plt.show()

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


	def 				lowercase__  (self : List[str],					__UpperCAmelCase : List[str]      )     ->			int:

					"""simple docstring"""

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

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






	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Union[str, Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asmatrix(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.convolute(
					    __UpperCAmelCase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pooling(__UpperCAmelCase,					self.size_poolinga      )

					return data_conveda, data_pooleda


if __name__ == "__main__":
	pass




