






import pickle

import numpy as np
from matplotlib import pyplot as plt



class 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






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



					def 	_lowercase					( self					:  List[str]							,		__A					:  Any	):
						# save model dict with pickle
						snake_case__			:				Union[str, Any]       =  {
						    "num_bp1": self.num_bpa,
						    "num_bp2": self.num_bpa,
						    "num_bp3": self.num_bpa,
						    "conv1": self.conva,
						    "step_conv1": self.step_conva,
						    "size_pooling1": self.size_poolinga,
						    "rate_weight": self.rate_weight,
						    "rate_thre": self.rate_thre,
						    "w_conv1": self.w_conva,
						    "wkj": self.wkj,
						    "vji": self.vji,
						    "thre_conv1": self.thre_conva,
						    "thre_bp2": self.thre_bpa,
						    "thre_bp3": self.thre_bpa,
						}
						with open(__A							,		"wb"	) as f:
							pickle.dump(__A							,		__A	)

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



					@classmethod
					def 	_lowercase					( cls					:  Optional[int]							,		__A					:  Optional[int]	):
						# read saved model
						with open(__A							,		"rb"	) as f:
							snake_case__			:				List[Any]       =  pickle.load(__A	)  # noqa: S301

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



					def 	_lowercase					( self					:  Tuple							,		__A					:  str	):
						return 1 / (1 + np.exp(-1 * x	))



					def 	_lowercase					( self					:  Tuple							,		__A					:  List[str]	):
						return round(__A							,		3	)



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

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



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



					def 	_lowercase					( self					:  int							,		__A					:  List[str]	):
						# expanding three dimension data to one dimension list
						snake_case__			:				str       =  []
						for i in range(len(__A	)	):
							snake_case__			:				Dict       =  np.shape(data[i]	)
							snake_case__			:				str       =  data[i].reshape(1							,		shapes[0] * shapes[1]	)
							snake_case__			:				Optional[int]       =  data_listed.getA().tolist()[0]
							data_expanded.extend(__A	)
						snake_case__			:				Dict       =  np.asarray(__A	)
						return data_expanded



					def 	_lowercase					( self					:  List[Any]							,		__A					:  Optional[Any]	):
						# expanding matrix to one dimension list
						snake_case__			:				Tuple       =  np.asarray(__A	)
						snake_case__			:				Any       =  np.shape(__A	)
						snake_case__			:				int       =  data_mat.reshape(1							,		shapes[0] * shapes[1]	)
						return data_expanded



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



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

								snake_case__			:				int       =  np.dot(__A							,		self.vji.T	) - self.thre_bpa
								snake_case__			:				Tuple       =  self.sig(__A	)
								snake_case__			:				str       =  np.dot(__A							,		self.wkj.T	) - self.thre_bpa
								snake_case__			:				List[Any]       =  self.sig(__A	)

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

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

									snake_case__			:				str       =  self.w_conva[k_conv] + delta_w.reshape(
									    (self.conva[0], self.conva[0])	)

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

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

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



					def 	_lowercase					( self					:  Tuple							,		__A					:  List[Any]	):
						# model predict
						snake_case__			:				Dict       =  []
						print("-------------------Start Testing-------------------------"	)
						print((" - - Shape: Test_Data  ", np.shape(__A	))	)
						for p in range(len(__A	)	):
							snake_case__			:				Optional[Any]       =  np.asmatrix(datas_test[p]	)
							snake_case__,						snake_case__			:				int       =  self.convolute(
							    __A							,		self.conva							,		self.w_conva							,		self.thre_conva							,		conv_step=self.step_conva							,		)
							snake_case__			:				Tuple       =  self.pooling(__A							,		self.size_poolinga	)
							snake_case__			:				Optional[Any]       =  self._expand(__A	)

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






					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Union[str, Any]	):
						# return the data of image after convoluting process so we can check it out
						snake_case__			:				str       =  np.asmatrix(__A	)
						snake_case__,						snake_case__			:				List[str]       =  self.convolute(
						    __A							,		self.conva							,		self.w_conva							,		self.thre_conva							,		conv_step=self.step_conva							,		)
						snake_case__			:				Any       =  self.pooling(__A							,		self.size_poolinga	)

						return data_conveda, data_pooleda


if __name__ == "__main__":
	pass











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase :      List[str]										=				logging.get_logger(__name__)

__lowerCamelCase :      List[Any]										=				{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"xmod"



					def __init__( self					:  List[str]							,		__A					:  List[str]=3_0_5_2_2							,		__A					:  Tuple=7_6_8							,		__A					:  str=1_2							,		__A					:  List[Any]=1_2							,		__A					:  List[str]=3_0_7_2							,		__A					:  List[str]="gelu"							,		__A					:  List[Any]=0.1							,		__A					:  Tuple=0.1							,		__A					:  str=5_1_2							,		__A					:  Union[str, Any]=2							,		__A					:  List[Any]=0.0_2							,		__A					:  List[str]=1e-1_2							,		__A					:  Tuple=1							,		__A					:  List[Any]=0							,		__A					:  Optional[Any]=2							,		__A					:  Optional[int]="absolute"							,		__A					:  Optional[int]=True							,		__A					:  Dict=None							,		__A					:  Optional[int]=False							,		__A					:  Dict=2							,		__A					:  List[str]=False							,		__A					:  Dict=True							,		__A					:  Union[str, Any]=True							,		__A					:  Tuple=("en_XX",)							,		__A					:  Optional[Any]=None							,		**__A					:  Tuple							,		):
						super().__init__(pad_token_id=__A							,		bos_token_id=__A							,		eos_token_id=__A							,		**__A	)

						snake_case__			:				Tuple       =  vocab_size
						snake_case__			:				Union[str, Any]       =  hidden_size
						snake_case__			:				int       =  num_hidden_layers
						snake_case__			:				str       =  num_attention_heads
						snake_case__			:				Tuple       =  hidden_act
						snake_case__			:				Optional[Any]       =  intermediate_size
						snake_case__			:				List[str]       =  hidden_dropout_prob
						snake_case__			:				Optional[int]       =  attention_probs_dropout_prob
						snake_case__			:				Union[str, Any]       =  max_position_embeddings
						snake_case__			:				str       =  type_vocab_size
						snake_case__			:				List[str]       =  initializer_range
						snake_case__			:				Dict       =  layer_norm_eps
						snake_case__			:				str       =  position_embedding_type
						snake_case__			:				List[str]       =  use_cache
						snake_case__			:				Tuple       =  classifier_dropout
						snake_case__			:				Any       =  pre_norm
						snake_case__			:				List[str]       =  adapter_reduction_factor
						snake_case__			:				List[Any]       =  adapter_layer_norm
						snake_case__			:				str       =  adapter_reuse_layer_norm
						snake_case__			:				Union[str, Any]       =  ln_before_adapter
						snake_case__			:				Tuple       =  list(__A	)
						snake_case__			:				int       =  default_language



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					@property
					def 	_lowercase					( self					:  int	):
						if self.task == "multiple-choice":
							snake_case__			:				Any       =  {0: "batch", 1: "choice", 2: "sequence"}
						else:
							snake_case__			:				Any       =  {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]	)



