






"""simple docstring"""




import pickle

import numpy as np
from matplotlib import pyplot as plt






class        UpperCamelCase			:


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


 def _lowercase	(     self		:						str							,						UpperCAmelCase__		:						Any							)					->       Any:
   # save model dict with pickle
   _a  :       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(UpperCAmelCase__							,						"""wb"""							) as f:
     pickle.dump(UpperCAmelCase__							,						UpperCAmelCase__							)

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


 @classmethod
 def _lowercase	(     cls		:						Optional[Any]							,						UpperCAmelCase__		:						List[str]							)					->       Tuple:
   # read saved model
   with open(UpperCAmelCase__							,						"""rb"""							) as f:
     _a  :       Dict             =	pickle.load(UpperCAmelCase__							)  # noqa: S301

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


 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						int							)					->       Any:
   return 1 / (1 + np.exp(-1 * x							))


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Any							)					->       Optional[Any]:
   return round(UpperCAmelCase__							,						3							)


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

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


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


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


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


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


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						Tuple							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[Any]=bool							)					->       Union[str, Any]:
   # model traning
   print("""----------------------Start Training-------------------------"""							)
   print((""" - - Shape: Train_Data  """, np.shape(UpperCAmelCase__							))							)
   print((""" - - Shape: Teach_Data  """, np.shape(UpperCAmelCase__							))							)
   _a  :       Union[str, Any]             =	0
   _a  :       Dict             =	[]
   _a  :       Tuple             =	10000
   while rp < n_repeat and mse >= error_accuracy:
     _a  :       str             =	0
     print(f"""-------------Learning Time {rp}--------------"""							)
     for p in range(len(UpperCAmelCase__							)							):
       # print('------------Learning Image: %d--------------'%p)
       _a  :       int             =	np.asmatrix(datas_train[p]							)
       _a  :       List[str]             =	np.asarray(datas_teach[p]							)
       _a      ,  _a  :       int             =	self.convolute(
           UpperCAmelCase__							,						self.conva							,						self.w_conva							,						self.thre_conva							,						conv_step=self.step_conva							,						)
       _a  :       int             =	self.pooling(UpperCAmelCase__							,						self.size_poolinga							)
       _a  :       Optional[int]             =	np.shape(UpperCAmelCase__							)
       _a  :       Union[str, Any]             =	self._expand(UpperCAmelCase__							)
       _a  :       Union[str, Any]             =	data_bp_input

       _a  :       Tuple             =	np.dot(UpperCAmelCase__							,						self.vji.T							) - self.thre_bpa
       _a  :       List[str]             =	self.sig(UpperCAmelCase__							)
       _a  :       Dict             =	np.dot(UpperCAmelCase__							,						self.wkj.T							) - self.thre_bpa
       _a  :       int             =	self.sig(UpperCAmelCase__							)

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

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

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

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

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

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







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

   return data_conveda, data_pooleda


if __name__ == "__main__":
 pass









"""simple docstring"""




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_snake_case          =   logging.getLogger(__name__)






class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Optional[Any]							,						UpperCAmelCase__		:						Optional[int]=-1							)					->       Tuple:
   # in NER datasets, the last column is usually reserved for NER label
   _a  :       Optional[int]             =	label_idx


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       Any             =	mode.value
   _a  :       Optional[int]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       int             =	1
   _a  :       int             =	[]
   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as f:
     _a  :       str             =	[]
     _a  :       str             =	[]
     for line in f:
       if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
         if words:
           examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
           guid_index += 1
           _a  :       List[str]             =	[]
           _a  :       str             =	[]
       else:
         _a  :       List[Any]             =	line.split(""" """							)
         words.append(splits[0]							)
         if len(UpperCAmelCase__							) > 1:
           labels.append(splits[self.label_idx].replace("""\n"""							,						""""""							)							)
         else:
           # Examples could have no label for mode = "test"
           labels.append("""O"""							)
     if words:
       examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
   return examples


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						List							)					->       Union[str, Any]:
   _a  :       List[str]             =	0
   for line in test_input_reader:
     if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
       writer.write(UpperCAmelCase__							)
       if not preds_list[example_id]:
         example_id += 1
     elif preds_list[example_id]:
       _a  :       int             =	line.split()[0] + """ """ + preds_list[example_id].pop(0							) + """\n"""
       writer.write(UpperCAmelCase__							)
     else:
       logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""							,						line.split()[0]							)







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       List[Any]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Union[str, Any]             =	["""O"""] + labels
     return labels
   else:
     return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Union[str, Any]							)					->       List[str]:
   # in CONLL2003 dataset chunk column is second-to-last
   super().__init__(label_idx=-2							)







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       Optional[int]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Optional[Any]             =	["""O"""] + labels
     return labels
   else:
     return [
         "O",
         "B-ADVP",
         "B-INTJ",
         "B-LST",
         "B-PRT",
         "B-NP",
         "B-SBAR",
         "B-VP",
         "B-ADJP",
         "B-CONJP",
         "B-PP",
         "I-ADVP",
         "I-INTJ",
         "I-LST",
         "I-PRT",
         "I-NP",
         "I-SBAR",
         "I-VP",
         "I-ADJP",
         "I-CONJP",
         "I-PP",
     ]







class        UpperCamelCase			(	snake_case_      ):


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       List[Any]             =	mode.value
   _a  :       Union[str, Any]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       List[str]             =	1
   _a  :       Optional[Any]             =	[]

   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as f:
     for sentence in parse_incr(UpperCAmelCase__							):
       _a  :       List[Any]             =	[]
       _a  :       Any             =	[]
       for token in sentence:
         words.append(token["""form"""]							)
         labels.append(token["""upos"""]							)
       assert len(UpperCAmelCase__							) == len(UpperCAmelCase__							)
       if words:
         examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
         guid_index += 1
   return examples


 def _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						List							)					->       Dict:
   _a  :       Optional[Any]             =	0
   for sentence in parse_incr(UpperCAmelCase__							):
     _a  :       List[str]             =	preds_list[example_id]
     _a  :       str             =	""""""
     for token in sentence:
       out += f"""{token['form']} ({token['upos']}|{s_p.pop(0							)}) """
     out += "\n"
     writer.write(UpperCAmelCase__							)
     example_id += 1







 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       return f.read().splitlines()
   else:
     return [
         "ADJ",
         "ADP",
         "ADV",
         "AUX",
         "CCONJ",
         "DET",
         "INTJ",
         "NOUN",
         "NUM",
         "PART",
         "PRON",
         "PROPN",
         "PUNCT",
         "SCONJ",
         "SYM",
         "VERB",
         "X",
     ]

