






import pickle

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







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







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# save model dict with pickle
								__magic_name__		:int   =   {
								    '''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       A						(    cls							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# read saved model
								with open(__lowerCAmelCase							,       '''rb'''  ) as f:
												__magic_name__		:Union[str, Any]   =   pickle.load(__lowerCAmelCase  )  # noqa: S301

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







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return 1 / (1 + np.exp(-1 * x  ))







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return round(__lowerCAmelCase							,       3  )







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# convolution process
								__magic_name__		:str   =   convs[0]
								__magic_name__		:Any   =   convs[1]
								__magic_name__		:Optional[Any]   =   np.shape(__lowerCAmelCase  )[0]
								# get the data slice of original image data, data_focus
								__magic_name__		:Optional[int]   =   []
								for i_focus in range(0							,       size_data - size_conv + 1							,       __lowerCAmelCase  ):
												for j_focus in range(0							,       size_data - size_conv + 1							,       __lowerCAmelCase  ):
																__magic_name__		:Tuple   =   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
								__magic_name__		:List[Any]   =   []
								__magic_name__		:Optional[int]   =   int((size_data - size_conv) / conv_step + 1  )
								for i_map in range(__lowerCAmelCase  ):
												__magic_name__		:Optional[Any]   =   []
												for i_focus in range(len(__lowerCAmelCase  )  ):
																__magic_name__		:int   =   (
																    np.sum(np.multiply(data_focus[i_focus]							,       w_convs[i_map]  )  )
																    - thre_convs[i_map]
																)
																featuremap.append(self.sig(__lowerCAmelCase  )  )
												__magic_name__		:str   =   np.asmatrix(__lowerCAmelCase  ).reshape(
												    __lowerCAmelCase							,       __lowerCAmelCase  )
												data_featuremap.append(__lowerCAmelCase  )

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







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







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# expanding three dimension data to one dimension list
								__magic_name__		:Tuple   =   []
								for i in range(len(__lowerCAmelCase  )  ):
												__magic_name__		:Dict   =   np.shape(data[i]  )
												__magic_name__		:Dict   =   data[i].reshape(1							,       shapes[0] * shapes[1]  )
												__magic_name__		:Any   =   data_listed.getA().tolist()[0]
												data_expanded.extend(__lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   np.asarray(__lowerCAmelCase  )
								return data_expanded







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# expanding matrix to one dimension list
								__magic_name__		:Union[str, Any]   =   np.asarray(__lowerCAmelCase  )
								__magic_name__		:Any   =   np.shape(__lowerCAmelCase  )
								__magic_name__		:List[str]   =   data_mat.reshape(1							,       shapes[0] * shapes[1]  )
								return data_expanded







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   []
								__magic_name__		:Tuple   =   0
								for i_map in range(__lowerCAmelCase  ):
												__magic_name__		:List[Any]   =   np.ones((size_map, size_map)  )
												for i in range(0							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
																for j in range(0							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
																				__magic_name__		:str   =   pd_pool[
																				    i_pool
																				]
																				__magic_name__		:List[str]   =   i_pool + 1
												__magic_name__		:Union[str, Any]   =   np.multiply(
												    __lowerCAmelCase							,       np.multiply(out_map[i_map]							,       (1 - out_map[i_map])  )  )
												pd_all.append(__lowerCAmelCase  )
								return pd_all







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

																__magic_name__		:Any   =   np.dot(__lowerCAmelCase							,       self.vji.T  ) - self.thre_bpa
																__magic_name__		:int   =   self.sig(__lowerCAmelCase  )
																__magic_name__		:Union[str, Any]   =   np.dot(__lowerCAmelCase							,       self.wkj.T  ) - self.thre_bpa
																__magic_name__		:Optional[int]   =   self.sig(__lowerCAmelCase  )

																# --------------Model Leaning ------------------------
																# calculate error and gradient---------------
																__magic_name__		:Any   =   np.multiply(
																    (data_teach - bp_outa)							,       np.multiply(__lowerCAmelCase							,       (1 - bp_outa)  )  )
																__magic_name__		:int   =   np.multiply(
																    np.dot(__lowerCAmelCase							,       self.wkj  )							,       np.multiply(__lowerCAmelCase							,       (1 - bp_outa)  )  )
																__magic_name__		:Tuple   =   np.dot(__lowerCAmelCase							,       self.vji  )

																__magic_name__		:Dict   =   pd_i_all / (self.size_poolinga * self.size_poolinga)
																__magic_name__		:Dict   =   pd_conva_pooled.T.getA().tolist()
																__magic_name__		: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]  ):
																				__magic_name__		:List[str]   =   self._expand_mat(pd_conva_all[k_conv]  )
																				__magic_name__		:Optional[int]   =   self.rate_weight * np.dot(__lowerCAmelCase							,       __lowerCAmelCase  )

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

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

								def draw_error():
												__magic_name__		:List[str]   =   [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       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# model predict
								__magic_name__		:str   =   []
								print('''-------------------Start Testing-------------------------'''  )
								print((''' - - Shape: Test_Data  ''', np.shape(__lowerCAmelCase  ))  )
								for p in range(len(__lowerCAmelCase  )  ):
												__magic_name__		:Any   =   np.asmatrix(datas_test[p]  )
												__magic_name__  ,					__magic_name__		:Any   =   self.convolute(
												    __lowerCAmelCase							,       self.conva							,       self.w_conva							,       self.thre_conva							,       conv_step=self.step_conva							,       )
												__magic_name__		:Tuple   =   self.pooling(__lowerCAmelCase							,       self.size_poolinga  )
												__magic_name__		:List[Any]   =   self._expand(__lowerCAmelCase  )

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







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

								return data_conveda, data_pooleda


if __name__ == "__main__":
					pass













from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__           =			logging.get_logger(__name__)

UpperCamelCase__           =			{
    "tanreinama/GPTSAN-2.8B-spout_is_uniform": (
        "https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
    ),
}

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							"""gptsan-japanese"""
							snake_case  :   Any      =							[
							    """past_key_values""",
							]
							snake_case  :   str      =							{
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}






							def __init__(    self	,			__lowerCAmelCase=36000	,			__lowerCAmelCase=1280	,			__lowerCAmelCase=1024	,			__lowerCAmelCase=8192	,			__lowerCAmelCase=4096	,			__lowerCAmelCase=128	,			__lowerCAmelCase=10	,			__lowerCAmelCase=0	,			__lowerCAmelCase=16	,			__lowerCAmelCase=16	,			__lowerCAmelCase=128	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase=1E-5	,			__lowerCAmelCase=False	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase="float32"	,			__lowerCAmelCase=False	,			__lowerCAmelCase=False	,			__lowerCAmelCase=False	,			__lowerCAmelCase=0.002	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True	,			__lowerCAmelCase=35998	,			__lowerCAmelCase=35995	,			__lowerCAmelCase=35999	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      vocab_size
														UpperCamelCase__					  =      max_position_embeddings
														UpperCamelCase__					  =      d_model
														UpperCamelCase__					  =      d_ff
														UpperCamelCase__					  =      d_ext
														UpperCamelCase__					  =      d_spout
														UpperCamelCase__					  =      num_switch_layers
														UpperCamelCase__					  =      num_ext_layers
														UpperCamelCase__					  =      num_switch_layers + num_ext_layers
														UpperCamelCase__					  =      num_heads
														UpperCamelCase__					  =      num_experts
														UpperCamelCase__					  =      expert_capacity
														UpperCamelCase__					  =      dropout_rate
														UpperCamelCase__					  =      layer_norm_epsilon
														UpperCamelCase__					  =      router_bias
														UpperCamelCase__					  =      router_jitter_noise
														UpperCamelCase__					  =      router_dtype
														UpperCamelCase__					  =      router_ignore_padding_tokens
														UpperCamelCase__					  =      output_hidden_states
														UpperCamelCase__					  =      output_attentions
														UpperCamelCase__					  =      initializer_factor
														UpperCamelCase__					  =      output_router_logits
														UpperCamelCase__					  =      use_cache

														super().__init__(
														    separator_token_id=__lowerCAmelCase	,			pad_token_id=__lowerCAmelCase	,			eos_token_id=__lowerCAmelCase	,			**__lowerCAmelCase	,			)




