"""simple docstring"""






import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
						import jax.numpy as jnp
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def __get__(				self   :	str    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	List[str]=None			):
									# See docs.python.org/3/howto/descriptor.html#properties
									if obj is None:
													return self
									if self.fget is None:
													raise AttributeError('unreadable attribute'			)
									UpperCAmelCase__             =							'__cached_' + self.fget.__name__
									UpperCAmelCase__             =							getattr(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			)
									if cached is None:
													UpperCAmelCase__             =							self.fget(lowerCamelCase__			)
													setattr(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			)
									return cached






def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							val.lower()
				if val in {"y", "yes", "t", "true", "on", "1"}:
								return 1
				if val in {"n", "no", "f", "false", "off", "0"}:
								return 0
				raise ValueError(f'''invalid truth value {val!r}'''       )



def   a_						(					lowerCamelCase       ):
				if is_torch_fx_proxy(lowerCamelCase       ):
								return True
				if is_torch_available():
								import torch

								if isinstance(lowerCamelCase					,			torch.Tensor       ):
												return True
				if is_tf_available():
								import tensorflow as tf

								if isinstance(lowerCamelCase					,			tf.Tensor       ):
												return True

				if is_flax_available():
								import jax.numpy as jnp
								from jax.core import Tracer

								if isinstance(lowerCamelCase					,			(jnp.ndarray, Tracer)       ):
												return True

				return isinstance(lowerCamelCase					,			np.ndarray       )



def   a_						(					lowerCamelCase       ):
				return isinstance(lowerCamelCase					,			np.ndarray       )



def   a_						(					lowerCamelCase       ):
				return _is_numpy(lowerCamelCase       )



def   a_						(					lowerCamelCase       ):
				import torch

				return isinstance(lowerCamelCase					,			torch.Tensor       )



def   a_						(					lowerCamelCase       ):
				return False if not is_torch_available() else _is_torch(lowerCamelCase       )



def   a_						(					lowerCamelCase       ):
				import torch

				return isinstance(lowerCamelCase					,			torch.device       )



def   a_						(					lowerCamelCase       ):
				return False if not is_torch_available() else _is_torch_device(lowerCamelCase       )



def   a_						(					lowerCamelCase       ):
				import torch

				if isinstance(lowerCamelCase					,			lowerCamelCase       ):
								if hasattr(lowerCamelCase					,			lowerCamelCase       ):
												UpperCAmelCase__             =							getattr(lowerCamelCase					,			lowerCamelCase       )
								else:
												return False
				return isinstance(lowerCamelCase					,			torch.dtype       )



def   a_						(					lowerCamelCase       ):
				return False if not is_torch_available() else _is_torch_dtype(lowerCamelCase       )



def   a_						(					lowerCamelCase       ):
				import tensorflow as tf

				return isinstance(lowerCamelCase					,			tf.Tensor       )



def   a_						(					lowerCamelCase       ):
				return False if not is_tf_available() else _is_tensorflow(lowerCamelCase       )



def   a_						(					lowerCamelCase       ):
				import tensorflow as tf

				# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
				if hasattr(lowerCamelCase					,			'is_symbolic_tensor'       ):
								return tf.is_symbolic_tensor(lowerCamelCase       )
				return type(lowerCamelCase       ) == tf.Tensor



def   a_						(					lowerCamelCase       ):
				return False if not is_tf_available() else _is_tf_symbolic_tensor(lowerCamelCase       )



def   a_						(					lowerCamelCase       ):
				import jax.numpy as jnp  # noqa: F811

				return isinstance(lowerCamelCase					,			jnp.ndarray       )



def   a_						(					lowerCamelCase       ):
				return False if not is_flax_available() else _is_jax(lowerCamelCase       )



def   a_						(					lowerCamelCase       ):
				if isinstance(lowerCamelCase					,			(dict, UserDict)       ):
								return {k: to_py_obj(lowerCamelCase       ) for k, v in obj.items()}
				elif isinstance(lowerCamelCase					,			(list, tuple)       ):
								return [to_py_obj(lowerCamelCase       ) for o in obj]
				elif is_tf_tensor(lowerCamelCase       ):
								return obj.numpy().tolist()
				elif is_torch_tensor(lowerCamelCase       ):
								return obj.detach().cpu().tolist()
				elif is_jax_tensor(lowerCamelCase       ):
								return np.asarray(lowerCamelCase       ).tolist()
				elif isinstance(lowerCamelCase					,			(np.ndarray, np.number)       ):  # tolist also works on 0d np arrays
								return obj.tolist()
				else:
								return obj



def   a_						(					lowerCamelCase       ):
				if isinstance(lowerCamelCase					,			(dict, UserDict)       ):
								return {k: to_numpy(lowerCamelCase       ) for k, v in obj.items()}
				elif isinstance(lowerCamelCase					,			(list, tuple)       ):
								return np.array(lowerCamelCase       )
				elif is_tf_tensor(lowerCamelCase       ):
								return obj.numpy()
				elif is_torch_tensor(lowerCamelCase       ):
								return obj.detach().cpu().numpy()
				elif is_jax_tensor(lowerCamelCase       ):
								return np.asarray(lowerCamelCase       )
				else:
								return obj
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							fields(self			)

									# Safety and consistency checks
									if not len(lowerCamelCase__			):
													raise ValueError(f'''{self.__class__.__name__} has no fields.'''			)
									if not all(field.default is None for field in class_fields[1:]			):
													raise ValueError(f'''{self.__class__.__name__} should not have more than one required field.'''			)

									UpperCAmelCase__             =							getattr(self    ,class_fields[0].name			)
									UpperCAmelCase__             =							all(getattr(self    ,field.name			) is None for field in class_fields[1:]			)

									if other_fields_are_none and not is_tensor(lowerCamelCase__			):
													if isinstance(lowerCamelCase__    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							first_field.items()
																	UpperCAmelCase__             =							True
													else:
																	try:
																					UpperCAmelCase__             =							iter(lowerCamelCase__			)
																					UpperCAmelCase__             =							True
																	except TypeError:
																					UpperCAmelCase__             =							False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
													if first_field_iterator:
																	for idx, element in enumerate(lowerCamelCase__			):
																					if (
																					    not isinstance(lowerCamelCase__    ,(list, tuple)			)
																					    or not len(lowerCamelCase__			) == 2
																					    or not isinstance(element[0]    ,lowerCamelCase__			)
																					):
																									if idx == 0:
																													# If we do not have an iterator of key/values, set it as attribute
																													UpperCAmelCase__             =							first_field
																									else:
																													# If we have a mixed iterator, raise an error
																													raise ValueError(
																													    f'''Cannot set key/value for {element}. It needs to be a tuple (key, value).'''			)
																									break
																					setattr(self    ,element[0]    ,element[1]			)
																					if element[1] is not None:
																									UpperCAmelCase__             =							element[1]
													elif first_field is not None:
																	UpperCAmelCase__             =							first_field
									else:
													for field in class_fields:
																	UpperCAmelCase__             =							getattr(self    ,field.name			)
																	if v is not None:
																					UpperCAmelCase__             =							v







					def __delitem__(				self   :	Union[str, Any]    ,*lowerCamelCase__   :	List[str]    ,**lowerCamelCase__   :	Dict			):
									raise Exception(f'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,*lowerCamelCase__   :	List[str]    ,**lowerCamelCase__   :	str			):
									raise Exception(f'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''			)







					def 		__lowerCAmelCase						(				self   :	str    ,*lowerCamelCase__   :	Any    ,**lowerCamelCase__   :	Union[str, Any]			):
									raise Exception(f'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''			)







					def 		__lowerCAmelCase						(				self   :	str    ,*lowerCamelCase__   :	Optional[Any]    ,**lowerCamelCase__   :	List[Any]			):
									raise Exception(f'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''			)







					def __getitem__(				self   :	List[str]    ,lowerCamelCase__   :	List[Any]			):
									if isinstance(lowerCamelCase__    ,lowerCamelCase__			):
													UpperCAmelCase__             =							dict(self.items()			)
													return inner_dict[k]
									else:
													return self.to_tuple()[k]







					def __setattr__(				self   :	Optional[int]    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	Optional[int]			):
									if name in self.keys() and value is not None:
													# Don't call self.__setitem__ to avoid recursion errors
													super().__setitem__(lowerCamelCase__    ,lowerCamelCase__			)
									super().__setattr__(lowerCamelCase__    ,lowerCamelCase__			)







					def __setitem__(				self   :	Optional[Any]    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	Dict			):
									# Will raise a KeyException if needed
									super().__setitem__(lowerCamelCase__    ,lowerCamelCase__			)
									# Don't call self.__setattr__ to avoid recursion errors
									super().__setattr__(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									return tuple(self[k] for k in self.keys()			)






class 				snake_case       ( __UpperCAmelCase      ,    __UpperCAmelCase				):




					"""simple docstring"""

					@classmethod
					def 		__lowerCAmelCase						(				cls   :	List[str]    ,lowerCamelCase__   :	List[Any]			):
									raise ValueError(
									    f'''{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()			)}'''			)






class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "longest"
					snake_case__				    = "max_length"
					snake_case__				    = "do_not_pad"






class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "pt"
					snake_case__				    = "tf"
					snake_case__				    = "np"
					snake_case__				    = "jax"






class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	Tuple    ,lowerCamelCase__   :	List[ContextManager]			):
									UpperCAmelCase__             =							context_managers
									UpperCAmelCase__             =							ExitStack()







					def __enter__(				self   :	Union[str, Any]			):
									for context_manager in self.context_managers:
													self.stack.enter_context(lowerCamelCase__			)







					def __exit__(				self   :	Union[str, Any]    ,*lowerCamelCase__   :	Optional[Any]    ,**lowerCamelCase__   :	Dict			):
									self.stack.__exit__(*lowerCamelCase__    ,**lowerCamelCase__			)






def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							infer_framework(lowerCamelCase       )
				if framework == "tf":
								UpperCAmelCase__             =							inspect.signature(model_class.call       )  # TensorFlow models
				elif framework == "pt":
								UpperCAmelCase__             =							inspect.signature(model_class.forward       )  # PyTorch models
				else:
								UpperCAmelCase__             =							inspect.signature(model_class.__call__       )  # Flax models

				for p in signature.parameters:
								if p == "return_loss" and signature.parameters[p].default is True:
												return True

				return False



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							model_class.__name__
				UpperCAmelCase__             =							infer_framework(lowerCamelCase       )
				if framework == "tf":
								UpperCAmelCase__             =							inspect.signature(model_class.call       )  # TensorFlow models
				elif framework == "pt":
								UpperCAmelCase__             =							inspect.signature(model_class.forward       )  # PyTorch models
				else:
								UpperCAmelCase__             =							inspect.signature(model_class.__call__       )  # Flax models

				if "QuestionAnswering" in model_name:
								return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
				else:
								return [p for p in signature.parameters if "label" in p]



def   a_						(					lowerCamelCase					,			lowerCamelCase = ""					,			lowerCamelCase = "."       ):

				def _flatten_dict(lowerCamelCase					,			lowerCamelCase=""					,			lowerCamelCase="."       ):
								for k, v in d.items():
												UpperCAmelCase__             =							str(lowerCamelCase       ) + delimiter + str(lowerCamelCase       ) if parent_key else k
												if v and isinstance(lowerCamelCase					,			lowerCamelCase       ):
																yield from flatten_dict(lowerCamelCase					,			lowerCamelCase					,			delimiter=lowerCamelCase       ).items()
												else:
																yield key, v

				return dict(_flatten_dict(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )       )



@contextmanager
def   a_						(					lowerCamelCase					,			lowerCamelCase = False       ):
				if use_temp_dir:
								with tempfile.TemporaryDirectory() as tmp_dir:
												yield tmp_dir
				else:
								yield working_dir



def   a_						(					lowerCamelCase					,			lowerCamelCase=None       ):
				if is_numpy_array(lowerCamelCase       ):
								return np.transpose(lowerCamelCase					,			axes=lowerCamelCase       )
				elif is_torch_tensor(lowerCamelCase       ):
								return array.T if axes is None else array.permute(*lowerCamelCase       )
				elif is_tf_tensor(lowerCamelCase       ):
								import tensorflow as tf

								return tf.transpose(lowerCamelCase					,			perm=lowerCamelCase       )
				elif is_jax_tensor(lowerCamelCase       ):
								return jnp.transpose(lowerCamelCase					,			axes=lowerCamelCase       )
				else:
								raise ValueError(f'''Type not supported for transpose: {type(lowerCamelCase       )}.'''       )



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if is_numpy_array(lowerCamelCase       ):
								return np.reshape(lowerCamelCase					,			lowerCamelCase       )
				elif is_torch_tensor(lowerCamelCase       ):
								return array.reshape(*lowerCamelCase       )
				elif is_tf_tensor(lowerCamelCase       ):
								import tensorflow as tf

								return tf.reshape(lowerCamelCase					,			lowerCamelCase       )
				elif is_jax_tensor(lowerCamelCase       ):
								return jnp.reshape(lowerCamelCase					,			lowerCamelCase       )
				else:
								raise ValueError(f'''Type not supported for reshape: {type(lowerCamelCase       )}.'''       )



def   a_						(					lowerCamelCase					,			lowerCamelCase=None       ):
				if is_numpy_array(lowerCamelCase       ):
								return np.squeeze(lowerCamelCase					,			axis=lowerCamelCase       )
				elif is_torch_tensor(lowerCamelCase       ):
								return array.squeeze() if axis is None else array.squeeze(dim=lowerCamelCase       )
				elif is_tf_tensor(lowerCamelCase       ):
								import tensorflow as tf

								return tf.squeeze(lowerCamelCase					,			axis=lowerCamelCase       )
				elif is_jax_tensor(lowerCamelCase       ):
								return jnp.squeeze(lowerCamelCase					,			axis=lowerCamelCase       )
				else:
								raise ValueError(f'''Type not supported for squeeze: {type(lowerCamelCase       )}.'''       )



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if is_numpy_array(lowerCamelCase       ):
								return np.expand_dims(lowerCamelCase					,			lowerCamelCase       )
				elif is_torch_tensor(lowerCamelCase       ):
								return array.unsqueeze(dim=lowerCamelCase       )
				elif is_tf_tensor(lowerCamelCase       ):
								import tensorflow as tf

								return tf.expand_dims(lowerCamelCase					,			axis=lowerCamelCase       )
				elif is_jax_tensor(lowerCamelCase       ):
								return jnp.expand_dims(lowerCamelCase					,			axis=lowerCamelCase       )
				else:
								raise ValueError(f'''Type not supported for expand_dims: {type(lowerCamelCase       )}.'''       )



def   a_						(					lowerCamelCase       ):
				if is_numpy_array(lowerCamelCase       ):
								return np.size(lowerCamelCase       )
				elif is_torch_tensor(lowerCamelCase       ):
								return array.numel()
				elif is_tf_tensor(lowerCamelCase       ):
								import tensorflow as tf

								return tf.size(lowerCamelCase       )
				elif is_jax_tensor(lowerCamelCase       ):
								return array.size
				else:
								raise ValueError(f'''Type not supported for expand_dims: {type(lowerCamelCase       )}.'''       )



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				for key, value in auto_map.items():
								if isinstance(lowerCamelCase					,			(tuple, list)       ):
												UpperCAmelCase__             =							[f'''{repo_id}--{v}''' if (v is not None and '--' not in v) else v for v in value]
								elif value is not None and "--" not in value:
												UpperCAmelCase__             =							f'''{repo_id}--{value}'''

				return auto_map







def   a_						(					lowerCamelCase       ):
				for base_class in inspect.getmro(lowerCamelCase       ):
								UpperCAmelCase__             =							base_class.__module__
								UpperCAmelCase__             =							base_class.__name__
								if module.startswith('tensorflow'       ) or module.startswith('keras'       ) or name == "TFPreTrainedModel":
												return "tf"
								elif module.startswith('torch'       ) or name == "PreTrainedModel":
												return "pt"
								elif module.startswith('flax'       ) or module.startswith('jax'       ) or name == "FlaxPreTrainedModel":
												return "flax"
				else:
								raise TypeError(f'''Could not infer framework from class {model_class}.'''       )







import pickle

import numpy as np
from matplotlib import pyplot as plt







class      lowerCAmelCase__       :




     '''simple docstring'''


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


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				Dict:
            # save model dict with pickle
            _A      :       Dict				    = {
                "num_bp1": self.num_bpa,
                "num_bp2": self.num_bpa,
                "num_bp3": self.num_bpa,
                "conv1": self.conva,
                "step_conv1": self.step_conva,
                "size_pooling1": self.size_poolinga,
                "rate_weight": self.rate_weight,
                "rate_thre": self.rate_thre,
                "w_conv1": self.w_conva,
                "wkj": self.wkj,
                "vji": self.vji,
                "thre_conv1": self.thre_conva,
                "thre_bp2": self.thre_bpa,
                "thre_bp3": self.thre_bpa,
            }
            with open(__lowerCamelCase ,						"wb") as f:
                   pickle.dump(__lowerCamelCase ,						__lowerCamelCase)

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


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

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


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


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


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

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


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


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


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


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


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

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

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

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

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

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

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

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


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				int:
            # model predict
            _A      :       Union[str, Any]				    = []
            print("-------------------Start Testing-------------------------")
            print((" - - Shape: Test_Data  ", np.shape(__lowerCamelCase)))
            for p in range(len(__lowerCamelCase)):
                   _A      :       int				    = np.asmatrix(datas_test[p])
                   _A    ,			_A      :       List[Any]				    = self.convolute(
                       __lowerCamelCase ,						self.conva ,						self.w_conva ,						self.thre_conva ,						conv_step=self.step_conva ,						)
                   _A      :       str				    = self.pooling(__lowerCamelCase ,						self.size_poolinga)
                   _A      :       Optional[int]				    = self._expand(__lowerCamelCase)

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


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

            return data_conveda, data_pooleda


if __name__ == "__main__":
    pass
