

import pickle

import numpy as np
from matplotlib import pyplot as plt
class        snake_case_		:







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







		def 			__lowercase   (		self			,     __lowercase   )				->   Tuple:
				# save model dict with pickle
				lowerCamelCase   : Optional[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(__lowercase			,     '''wb'''   ) as f:
						pickle.dump(__lowercase			,     __lowercase   )

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







		@classmethod
		def 			__lowercase   (		cls			,     __lowercase   )				->   Dict:
				# read saved model
				with open(__lowercase			,     '''rb'''   ) as f:
						lowerCamelCase   : str								=pickle.load(__lowercase   )  # noqa: S301

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







		def 			__lowercase   (		self			,     __lowercase   )				->   Union[str, Any]:
				return 1 / (1 + np.exp(-1 * x   ))







		def 			__lowercase   (		self			,     __lowercase   )				->   Any:
				return round(__lowercase			,     3   )







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

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







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







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







		def 			__lowercase   (		self			,     __lowercase   )				->   str:
				# expanding matrix to one dimension list
				lowerCamelCase   : Optional[int]								=np.asarray(__lowercase   )
				lowerCamelCase   : List[str]								=np.shape(__lowercase   )
				lowerCamelCase   : str								=data_mat.reshape(1			,     shapes[0] * shapes[1]   )
				return data_expanded







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







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

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

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

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

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

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

				def draw_error():
						lowerCamelCase   : Optional[Any]								=[error_accuracy for i in range(int(n_repeat * 1.2   )   )]
						plt.plot(__lowercase			,     '''+-'''   )
						plt.plot(__lowercase			,     '''r--'''   )
						plt.xlabel('''Learning Times'''   )
						plt.ylabel('''All_mse'''   )
						plt.grid(__lowercase			,     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			,     __lowercase   )				->   List[str]:
				# model predict
				lowerCamelCase   : Tuple								=[]
				print('''-------------------Start Testing-------------------------'''   )
				print((''' - - Shape: Test_Data  ''', np.shape(__lowercase   ))   )
				for p in range(len(__lowercase   )   ):
						lowerCamelCase   : str								=np.asmatrix(datas_test[p]   )
						lowerCamelCase   : Union[str, Any]								=self.convolute(
						    __lowercase			,     self.conva			,     self.w_conva			,     self.thre_conva			,     conv_step=self.step_conva			,     )
						lowerCamelCase   : Optional[Any]								=self.pooling(__lowercase			,     self.size_poolinga   )
						lowerCamelCase   : Union[str, Any]								=self._expand(__lowercase   )

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







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

				return data_conveda, data_pooleda


if __name__ == "__main__":
				pass








# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


snake_case_							=			'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
				import torch

				print('''Torch version:''', torch.__version__)
				print('''Cuda available:''', torch.cuda.is_available())
				print('''Cuda version:''', torch.version.cuda)
				print('''CuDNN version:''', torch.backends.cudnn.version())
				print('''Number of GPUs available:''', torch.cuda.device_count())
				print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
				print('''Torch version:''', None)

try:
				import deepspeed

				print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
				print('''DeepSpeed version:''', None)

try:
				import tensorflow as tf

				print('''TensorFlow version:''', tf.__version__)
				print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
				print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
				print('''TensorFlow version:''', None)

