



import pickle

import numpy as np
from matplotlib import pyplot as plt



class 						UpperCAmelCase_					:







							def __init__(     self,    __a,    __a,    __a,    __a,    __a,    __a=0.2,    __a=0.2):

														'''simple docstring'''

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







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														_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(__a,    "wb") as f:
																					pickle.dump(__a,    __a)

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







							@classmethod
							def      snake_case__   (     cls,    __a):

														'''simple docstring'''

														with open(__a,    "rb") as f:
																					_lowerCAmelCase  :							Any				     =					pickle.load(__a)  # noqa: S301

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







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

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







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														return round(__a,    3)







							def      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					convs[0]
														_lowerCAmelCase  :							Union[str, Any]				     =					convs[1]
														_lowerCAmelCase  :							Optional[int]				     =					np.shape(__a)[0]
														# get the data slice of original image data, data_focus
														_lowerCAmelCase  :							Optional[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):
																												_lowerCAmelCase  :							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
														_lowerCAmelCase  :							List[Any]				     =					[]
														_lowerCAmelCase  :							List[str]				     =					int((size_data - size_conv) / conv_step + 1)
														for i_map in range(__a):
																					_lowerCAmelCase  :							List[Any]				     =					[]
																					for i_focus in range(len(__a)):
																												_lowerCAmelCase  :							List[Any]				     =					(
																												    np.sum(np.multiply(data_focus[i_focus],    w_convs[i_map]))
																												    - thre_convs[i_map]
																												)
																												featuremap.append(self.sig(__a))
																					_lowerCAmelCase  :							List[str]				     =					np.asmatrix(__a).reshape(
																					    __a,    __a)
																					data_featuremap.append(__a)

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







							def      snake_case__   (     self,    __a,    __a,    __a="average_pool"):

														'''simple docstring'''

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







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

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







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					np.asarray(__a)
														_lowerCAmelCase  :							int				     =					np.shape(__a)
														_lowerCAmelCase  :							Union[str, Any]				     =					data_mat.reshape(1,    shapes[0] * shapes[1])
														return data_expanded







							def      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a):

														'''simple docstring'''

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







							def      snake_case__   (     self,    __a,    __a,    __a,    __a,    __a,    __a=bool):

														'''simple docstring'''

														print("----------------------Start Training-------------------------")
														print((" - - Shape: Train_Data  ", np.shape(__a)))
														print((" - - Shape: Teach_Data  ", np.shape(__a)))
														_lowerCAmelCase  :							List[Any]				     =					0
														_lowerCAmelCase  :							Dict				     =					[]
														_lowerCAmelCase  :							int				     =					1_0000
														while rp < n_repeat and mse >= error_accuracy:
																					_lowerCAmelCase  :							List[Any]				     =					0
																					print(f"-------------Learning Time {rp}--------------")
																					for p in range(len(__a)):
																												# print('------------Learning Image: %d--------------'%p)
																												_lowerCAmelCase  :							Optional[Any]				     =					np.asmatrix(datas_train[p])
																												_lowerCAmelCase  :							Optional[int]				     =					np.asarray(datas_teach[p])
																												_lowerCAmelCase     ,					_lowerCAmelCase  :							str				     =					self.convolute(
																												    __a,    self.conva,    self.w_conva,    self.thre_conva,    conv_step=self.step_conva,    )
																												_lowerCAmelCase  :							int				     =					self.pooling(__a,    self.size_poolinga)
																												_lowerCAmelCase  :							Tuple				     =					np.shape(__a)
																												_lowerCAmelCase  :							Optional[Any]				     =					self._expand(__a)
																												_lowerCAmelCase  :							List[Any]				     =					data_bp_input

																												_lowerCAmelCase  :							str				     =					np.dot(__a,    self.vji.T) - self.thre_bpa
																												_lowerCAmelCase  :							List[Any]				     =					self.sig(__a)
																												_lowerCAmelCase  :							List[str]				     =					np.dot(__a,    self.wkj.T) - self.thre_bpa
																												_lowerCAmelCase  :							Any				     =					self.sig(__a)

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

																												_lowerCAmelCase  :							int				     =					pd_i_all / (self.size_poolinga * self.size_poolinga)
																												_lowerCAmelCase  :							int				     =					pd_conva_pooled.T.getA().tolist()
																												_lowerCAmelCase  :							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]):
																																			_lowerCAmelCase  :							Dict				     =					self._expand_mat(pd_conva_all[k_conv])
																																			_lowerCAmelCase  :							Union[str, Any]				     =					self.rate_weight * np.dot(__a,    __a)

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

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

														def draw_error():
																					_lowerCAmelCase  :							Any				     =					[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      snake_case__   (     self,    __a):

														'''simple docstring'''

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

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







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					np.asmatrix(__a)
														_lowerCAmelCase     ,					_lowerCAmelCase  :							int				     =					self.convolute(
														    __a,    self.conva,    self.w_conva,    self.thre_conva,    conv_step=self.step_conva,    )
														_lowerCAmelCase  :							List[str]				     =					self.pooling(__a,    self.size_poolinga)

														return data_conveda, data_pooleda


if __name__ == "__main__":
						pass





import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


_snake_case	=						{
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=None						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					XLNetConfig.from_json_file(_lowerCamelCase						)

							_lowerCAmelCase  :							Any				     =					finetuning_task.lower() if finetuning_task is not None else ""
							if finetuning_task in GLUE_TASKS_NUM_LABELS:
														print(F"Building PyTorch XLNetForSequenceClassification model from configuration: {config}"						)
														_lowerCAmelCase  :							Any				     =					finetuning_task
														_lowerCAmelCase  :							Any				     =					GLUE_TASKS_NUM_LABELS[finetuning_task]
														_lowerCAmelCase  :							Union[str, Any]				     =					XLNetForSequenceClassification(_lowerCamelCase						)
							elif "squad" in finetuning_task:
														_lowerCAmelCase  :							Union[str, Any]				     =					finetuning_task
														_lowerCAmelCase  :							Any				     =					XLNetForQuestionAnswering(_lowerCamelCase						)
							else:
														_lowerCAmelCase  :							Union[str, Any]				     =					XLNetLMHeadModel(_lowerCamelCase						)

							# Load weights from tf checkpoint
							load_tf_weights_in_xlnet(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)

							# Save pytorch-model
							_lowerCAmelCase  :							Optional[int]				     =					os.path.join(_lowerCamelCase       ,				_lowerCamelCase						)
							_lowerCAmelCase  :							Dict				     =					os.path.join(_lowerCamelCase       ,				_lowerCamelCase						)
							print(F"Save PyTorch model to {os.path.abspath(_lowerCamelCase						)}"						)
							torch.save(model.state_dict()       ,				_lowerCamelCase						)
							print(F"Save configuration file to {os.path.abspath(_lowerCamelCase						)}"						)
							with open(_lowerCamelCase       ,				"w"       ,				encoding="utf-8"						) as f:
														f.write(config.to_json_string()						)


if __name__ == "__main__":
						_snake_case	=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
						)
						parser.add_argument(
						    "--xlnet_config_file",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        "The config json file corresponding to the pre-trained XLNet model. \n"
						        "This specifies the model architecture."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the folder to store the PyTorch model or dataset/vocab.",
						)
						parser.add_argument(
						    "--finetuning_task",
						    default=None,
						    type=str,
						    help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
						)
						_snake_case	=						parser.parse_args()
						print(args)

						convert_xlnet_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
						)
