




import pickle

import numpy as np
from matplotlib import pyplot as plt






class  __magic_name__						:
				'''simple docstring'''







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


				def     _A							(							self:   Any		,					_lowerCamelCase:   List[str]		):
											# save model dict with pickle
											SCREAMING_SNAKE_CASE_   	=    {
											    "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(snake_case_		,					'''wb'''		) as f:
																		pickle.dump(snake_case_		,					snake_case_		)

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


				@classmethod
				def     _A							(							cls:   int		,					_lowerCamelCase:   Any		):
											# read saved model
											with open(snake_case_		,					'''rb'''		) as f:
																		SCREAMING_SNAKE_CASE_   	=    pickle.load(snake_case_		)  # noqa: S301

											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''conv1'''		)
											conv_get.append(model_dic.get('''step_conv1'''		)		)
											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''size_pooling1'''		)
											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''num_bp1'''		)
											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''num_bp2'''		)
											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''num_bp3'''		)
											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''rate_weight'''		)
											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''rate_thre'''		)
											# create model instance
											SCREAMING_SNAKE_CASE_   	=    CNN(snake_case_		,					snake_case_		,					snake_case_		,					snake_case_		,					snake_case_		,					snake_case_		,					snake_case_		)
											# modify model parameter
											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''w_conv1'''		)
											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''wkj'''		)
											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''vji'''		)
											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''thre_conv1'''		)
											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''thre_bp2'''		)
											SCREAMING_SNAKE_CASE_   	=    model_dic.get('''thre_bp3'''		)
											return conv_ins


				def     _A							(							self:   int		,					_lowerCamelCase:   Any		):
											return 1 / (1 + np.exp(-1 * x		))


				def     _A							(							self:   int		,					_lowerCamelCase:   Tuple		):
											return round(snake_case_		,					3		)


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

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


				def     _A							(							self:   str		,					_lowerCamelCase:   int		,					_lowerCamelCase:   int		,					_lowerCamelCase:   int="average_pool"		):
											# pooling process
											SCREAMING_SNAKE_CASE_   	=    len(featuremaps[0]		)
											SCREAMING_SNAKE_CASE_   	=    int(size_map / size_pooling		)
											SCREAMING_SNAKE_CASE_   	=    []
											for i_map in range(len(snake_case_		)		):
																		SCREAMING_SNAKE_CASE_   	=    featuremaps[i_map]
																		SCREAMING_SNAKE_CASE_   	=    []
																		for i_focus in range(0		,					snake_case_		,					snake_case_		):
																									for j_focus in range(0		,					snake_case_		,					snake_case_		):
																																SCREAMING_SNAKE_CASE_   	=    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(snake_case_		)		)
																																elif pooling_type == "max_pooling":
																																							# max pooling
																																							map_pooled.append(np.max(snake_case_		)		)
																		SCREAMING_SNAKE_CASE_   	=    np.asmatrix(snake_case_		).reshape(snake_case_		,					snake_case_		)
																		featuremap_pooled.append(snake_case_		)
											return featuremap_pooled


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


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


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


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   Optional[Any]		,					_lowerCamelCase:   str=bool		):
											# model traning
											print('''----------------------Start Training-------------------------'''		)
											print((''' - - Shape: Train_Data  ''', np.shape(snake_case_		))		)
											print((''' - - Shape: Teach_Data  ''', np.shape(snake_case_		))		)
											SCREAMING_SNAKE_CASE_   	=    0
											SCREAMING_SNAKE_CASE_   	=    []
											SCREAMING_SNAKE_CASE_   	=    1_00_00
											while rp < n_repeat and mse >= error_accuracy:
																		SCREAMING_SNAKE_CASE_   	=    0
																		print(f"-------------Learning Time {rp}--------------"		)
																		for p in range(len(snake_case_		)		):
																									# print('------------Learning Image: %d--------------'%p)
																									SCREAMING_SNAKE_CASE_   	=    np.asmatrix(datas_train[p]		)
																									SCREAMING_SNAKE_CASE_   	=    np.asarray(datas_teach[p]		)
																									SCREAMING_SNAKE_CASE_   	=    self.convolute(
																									    snake_case_		,					self.conva		,					self.w_conva		,					self.thre_conva		,					conv_step=self.step_conva		,					)
																									SCREAMING_SNAKE_CASE_   	=    self.pooling(snake_case_		,					self.size_poolinga		)
																									SCREAMING_SNAKE_CASE_   	=    np.shape(snake_case_		)
																									SCREAMING_SNAKE_CASE_   	=    self._expand(snake_case_		)
																									SCREAMING_SNAKE_CASE_   	=    data_bp_input

																									SCREAMING_SNAKE_CASE_   	=    np.dot(snake_case_		,					self.vji.T		) - self.thre_bpa
																									SCREAMING_SNAKE_CASE_   	=    self.sig(snake_case_		)
																									SCREAMING_SNAKE_CASE_   	=    np.dot(snake_case_		,					self.wkj.T		) - self.thre_bpa
																									SCREAMING_SNAKE_CASE_   	=    self.sig(snake_case_		)

																									# --------------Model Leaning ------------------------
																									# calculate error and gradient---------------
																									SCREAMING_SNAKE_CASE_   	=    np.multiply(
																									    (data_teach - bp_outa)		,					np.multiply(snake_case_		,					(1 - bp_outa)		)		)
																									SCREAMING_SNAKE_CASE_   	=    np.multiply(
																									    np.dot(snake_case_		,					self.wkj		)		,					np.multiply(snake_case_		,					(1 - bp_outa)		)		)
																									SCREAMING_SNAKE_CASE_   	=    np.dot(snake_case_		,					self.vji		)

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

																																SCREAMING_SNAKE_CASE_   	=    self.w_conva[k_conv] + delta_w.reshape(
																																    (self.conva[0], self.conva[0])		)

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

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

																		SCREAMING_SNAKE_CASE_   	=    data_bp_input
																		SCREAMING_SNAKE_CASE_   	=    bp_outa * self.vji.T - self.thre_bpa
																		SCREAMING_SNAKE_CASE_   	=    self.sig(snake_case_		)
																		SCREAMING_SNAKE_CASE_   	=    bp_outa * self.wkj.T - self.thre_bpa
																		SCREAMING_SNAKE_CASE_   	=    self.sig(snake_case_		)
																		produce_out.extend(bp_outa.getA().tolist()		)
											SCREAMING_SNAKE_CASE_   	=    [list(map(self.do_round		,					snake_case_		)		) for each in produce_out]
											return np.asarray(snake_case_		)





				def     _A							(							self:   int		,					_lowerCamelCase:   List[str]		):
											# return the data of image after convoluting process so we can check it out
											SCREAMING_SNAKE_CASE_   	=    np.asmatrix(snake_case_		)
											SCREAMING_SNAKE_CASE_   	=    self.convolute(
											    snake_case_		,					self.conva		,					self.w_conva		,					self.thre_conva		,					conv_step=self.step_conva		,					)
											SCREAMING_SNAKE_CASE_   	=    self.pooling(snake_case_		,					self.size_poolinga		)

											return data_conveda, data_pooleda


if __name__ == "__main__":
			pass











import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE 						="""▁"""

__SCREAMING_SNAKE_CASE 						={"""vocab_file""": """spiece.model"""}

__SCREAMING_SNAKE_CASE 						={
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

__SCREAMING_SNAKE_CASE 						={
    """google/reformer-crime-and-punishment""": 524_288,
}






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Any	      =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					["input_ids", "attention_mask"]


				def __init__(							self:   List[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   List[Any]="</s>"		,					_lowerCamelCase:   str="<unk>"		,					_lowerCamelCase:   Union[str, Any]=[]		,					_lowerCamelCase:   Optional[Dict[str, Any]] = None		,					**_lowerCamelCase:   List[str]		,					):
											SCREAMING_SNAKE_CASE_   	=    {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    eos_token=_lowerCamelCase		,					unk_token=_lowerCamelCase		,					additional_special_tokens=_lowerCamelCase		,					sp_model_kwargs=self.sp_model_kwargs		,					**_lowerCamelCase		,					)

											SCREAMING_SNAKE_CASE_   	=    vocab_file
											SCREAMING_SNAKE_CASE_   	=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(_lowerCamelCase		)


				@property
				def     _A							(							self:   Optional[int]		):
											return self.sp_model.get_piece_size()


				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    {self.convert_ids_to_tokens(_lowerCamelCase		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab


				def __getstate__(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.__dict__.copy()
											SCREAMING_SNAKE_CASE_   	=    None
											return state


				def __setstate__(							self:   Dict		,					_lowerCamelCase:   Union[str, Any]		):
											SCREAMING_SNAKE_CASE_   	=    d

											# for backward compatibility
											if not hasattr(self		,					'''sp_model_kwargs'''		):
																		SCREAMING_SNAKE_CASE_   	=    {}

											SCREAMING_SNAKE_CASE_   	=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   str		):
											return self.sp_model.encode(_lowerCamelCase		,					out_type=_lowerCamelCase		)


				def     _A							(							self:   int		,					_lowerCamelCase:   Optional[int]		):
											return self.sp_model.piece_to_id(_lowerCamelCase		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Tuple		):
											if index < self.sp_model.get_piece_size():
																		SCREAMING_SNAKE_CASE_   	=    self.sp_model.IdToPiece(_lowerCamelCase		)
											return token


				def     _A							(							self:   List[str]		,					_lowerCamelCase:   int		):
											SCREAMING_SNAKE_CASE_   	=    []
											SCREAMING_SNAKE_CASE_   	=    ''''''
											for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																									out_string += self.sp_model.decode(_lowerCamelCase		) + token
																									SCREAMING_SNAKE_CASE_   	=    []
																		else:
																									current_sub_tokens.append(_lowerCamelCase		)
											out_string += self.sp_model.decode(_lowerCamelCase		)
											return out_string.strip()





				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[str] = None		):
											if not os.path.isdir(_lowerCamelCase		):
																		logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																		return
											SCREAMING_SNAKE_CASE_   	=    os.path.join(
											    _lowerCamelCase		,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCamelCase		) and os.path.isfile(self.vocab_file		):
																		copyfile(self.vocab_file		,					_lowerCamelCase		)
											elif not os.path.isfile(self.vocab_file		):
																		with open(_lowerCamelCase		,					'''wb'''		) as fi:
																									SCREAMING_SNAKE_CASE_   	=    self.sp_model.serialized_model_proto()
																									fi.write(_lowerCamelCase		)

											return (out_vocab_file,)


