






'''simple docstring'''







import pickle

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







     def __init__( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__=0.2    ,   A__=0.2    ) ->	int:
      snake_case				=			bp_numa
      snake_case				=			bp_numa
      snake_case				=			bp_numa
      snake_case				=			conva_get[:2]
      snake_case				=			conva_get[2]
      snake_case				=			size_pa
      snake_case				=			rate_w
      snake_case				=			rate_t
      snake_case				=			[
          np.mat(-1 * np.random.rand(self.conva[0]    ,   self.conva[0]    ) + 0.5    )
          for i in range(self.conva[1]    )
      ]
      snake_case				=			np.mat(-1 * np.random.rand(self.num_bpa    ,   self.num_bpa    ) + 0.5    )
      snake_case				=			np.mat(-1 * np.random.rand(self.num_bpa    ,   self.num_bpa    ) + 0.5    )
      snake_case				=			-2 * np.random.rand(self.conva[1]    ) + 1
      snake_case				=			-2 * np.random.rand(self.num_bpa    ) + 1
      snake_case				=			-2 * np.random.rand(self.num_bpa    ) + 1







     def     UpperCamelCase			( self    ,   A__    ) ->	Tuple:
      # save model dict with pickle
      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(A__    ,   '''wb'''    ) as f:
       pickle.dump(A__    ,   A__    )

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







     @classmethod
     def     UpperCamelCase			( cls    ,   A__    ) ->	Optional[Any]:
      # read saved model
      with open(A__    ,   '''rb'''    ) as f:
       snake_case				=			pickle.load(A__    )  # noqa: S301

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







     def     UpperCamelCase			( self    ,   A__    ) ->	str:
      return 1 / (1 + np.exp(-1 * x    ))







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







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	str:
      # convolution process
      snake_case				=			convs[0]
      snake_case				=			convs[1]
      snake_case				=			np.shape(A__    )[0]
      # get the data slice of original image data, data_focus
      snake_case				=			[]
      for i_focus in range(0    ,   size_data - size_conv + 1    ,   A__    ):
       for j_focus in range(0    ,   size_data - size_conv + 1    ,   A__    ):
        snake_case				=			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
      snake_case				=			[]
      snake_case				=			int((size_data - size_conv) / conv_step + 1    )
      for i_map in range(A__    ):
       snake_case				=			[]
       for i_focus in range(len(A__    )    ):
        snake_case				=			(
            np.sum(np.multiply(data_focus[i_focus]    ,   w_convs[i_map]    )    )
            - thre_convs[i_map]
        )
        featuremap.append(self.sig(A__    )    )
       snake_case				=			np.asmatrix(A__    ).reshape(
           A__    ,   A__    )
       data_featuremap.append(A__    )

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







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__="average_pool"    ) ->	int:
      # pooling process
      snake_case				=			len(featuremaps[0]    )
      snake_case				=			int(size_map / size_pooling    )
      snake_case				=			[]
      for i_map in range(len(A__    )    ):
       snake_case				=			featuremaps[i_map]
       snake_case				=			[]
       for i_focus in range(0    ,   A__    ,   A__    ):
        for j_focus in range(0    ,   A__    ,   A__    ):
         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(A__    )    )
         elif pooling_type == "max_pooling":
          # max pooling
          map_pooled.append(np.max(A__    )    )
       snake_case				=			np.asmatrix(A__    ).reshape(A__    ,   A__    )
       featuremap_pooled.append(A__    )
      return featuremap_pooled







     def     UpperCamelCase			( self    ,   A__    ) ->	Tuple:
      # expanding three dimension data to one dimension list
      snake_case				=			[]
      for i in range(len(A__    )    ):
       snake_case				=			np.shape(data[i]    )
       snake_case				=			data[i].reshape(1    ,   shapes[0] * shapes[1]    )
       snake_case				=			data_listed.getA().tolist()[0]
       data_expanded.extend(A__    )
      snake_case				=			np.asarray(A__    )
      return data_expanded







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







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Optional[Any]:
      snake_case				=			[]
      snake_case				=			0
      for i_map in range(A__    ):
       snake_case				=			np.ones((size_map, size_map)    )
       for i in range(0    ,   A__    ,   A__    ):
        for j in range(0    ,   A__    ,   A__    ):
         snake_case				=			pd_pool[
             i_pool
         ]
         snake_case				=			i_pool + 1
       snake_case				=			np.multiply(
           A__    ,   np.multiply(out_map[i_map]    ,   (1 - out_map[i_map])    )    )
       pd_all.append(A__    )
      return pd_all







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

        snake_case				=			np.dot(A__    ,   self.vji.T    ) - self.thre_bpa
        snake_case				=			self.sig(A__    )
        snake_case				=			np.dot(A__    ,   self.wkj.T    ) - self.thre_bpa
        snake_case				=			self.sig(A__    )

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

        snake_case				=			pd_i_all / (self.size_poolinga * self.size_poolinga)
        snake_case				=			pd_conva_pooled.T.getA().tolist()
        snake_case				=			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]    ):
         snake_case				=			self._expand_mat(pd_conva_all[k_conv]    )
         snake_case				=			self.rate_weight * np.dot(A__    ,   A__    )

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

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

      def draw_error():
       snake_case				=			[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     UpperCamelCase			( self    ,   A__    ) ->	List[str]:
      # model predict
      snake_case				=			[]
      print('''-------------------Start Testing-------------------------'''    )
      print((''' - - Shape: Test_Data  ''', np.shape(A__    ))    )
      for p in range(len(A__    )    ):
       snake_case				=			np.asmatrix(datas_test[p]    )
       snake_case				,		snake_case				=			self.convolute(
           A__    ,   self.conva    ,   self.w_conva    ,   self.thre_conva    ,   conv_step=self.step_conva    ,   )
       snake_case				=			self.pooling(A__    ,   self.size_poolinga    )
       snake_case				=			self._expand(A__    )

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







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

      return data_conveda, data_pooleda


if __name__ == "__main__":
   pass








'''simple docstring'''







import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
   import torch
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					'''char'''
     _UpperCAmelCase										=					'''bpe'''
     _UpperCAmelCase										=					'''wp'''




_lowercase       						=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					['''image_processor''', '''char_tokenizer''']
     _UpperCAmelCase										=					'''ViTImageProcessor'''
     _UpperCAmelCase										=					'''MgpstrTokenizer'''







     def __init__( self    ,   A__=None    ,   A__=None    ,   **A__    ) ->	List[Any]:
      snake_case				=			None
      if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''    ,   A__    ,   )
       snake_case				=			kwargs.pop('''feature_extractor'''    )

      snake_case				=			image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.'''    )
      if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.'''    )

      snake_case				=			tokenizer
      snake_case				=			AutoTokenizer.from_pretrained('''gpt2'''    )
      snake_case				=			AutoTokenizer.from_pretrained('''bert-base-uncased'''    )

      super().__init__(A__    ,   A__    )







     def __call__( self    ,   A__=None    ,   A__=None    ,   A__=None    ,   **A__    ) ->	List[str]:
      if images is None and text is None:
       raise ValueError('''You need to specify either an `images` or `text` input to process.'''    )

      if images is not None:
       snake_case				=			self.image_processor(A__    ,   return_tensors=A__    ,   **A__    )
      if text is not None:
       snake_case				=			self.char_tokenizer(A__    ,   return_tensors=A__    ,   **A__    )

      if text is None:
       return inputs
      elif images is None:
       return encodings
      else:
       snake_case				=			encodings['''input_ids''']
       return inputs







     def     UpperCamelCase			( self    ,   A__    ) ->	Dict:
      snake_case				,		snake_case				,		snake_case				=			sequences
      snake_case				=			char_preds.size(0    )

      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''char'''    )
      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''bpe'''    )
      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''wp'''    )

      snake_case				=			[]
      snake_case				=			[]
      for i in range(A__    ):
       snake_case				=			[char_scores[i], bpe_scores[i], wp_scores[i]]
       snake_case				=			[char_strs[i], bpe_strs[i], wp_strs[i]]
       snake_case				=			scores.index(max(A__    )    )
       final_strs.append(strs[max_score_index]    )
       final_scores.append(scores[max_score_index]    )

      snake_case				=			{}
      snake_case				=			final_strs
      snake_case				=			final_scores
      snake_case				=			char_strs
      snake_case				=			bpe_strs
      snake_case				=			wp_strs
      return out







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	Optional[Any]:
      if format == DecodeType.CHARACTER:
       snake_case				=			self.char_decode
       snake_case				=			1
       snake_case				=			'''[s]'''
      elif format == DecodeType.BPE:
       snake_case				=			self.bpe_decode
       snake_case				=			2
       snake_case				=			'''#'''
      elif format == DecodeType.WORDPIECE:
       snake_case				=			self.wp_decode
       snake_case				=			1_02
       snake_case				=			'''[SEP]'''
      else:
       raise ValueError(F"""Format {format} is not supported."""    )

      snake_case				,		snake_case				=			[], []
      snake_case				=			pred_logits.size(0    )
      snake_case				=			pred_logits.size(1    )
      snake_case				,		snake_case				=			pred_logits.topk(1    ,   dim=-1    ,   largest=A__    ,   sorted=A__    )
      snake_case				=			preds_index.view(-1    ,   A__    )[:, 1:]
      snake_case				=			decoder(A__    )
      snake_case				,		snake_case				=			torch.nn.functional.softmax(A__    ,   dim=2    ).max(dim=2    )
      snake_case				=			preds_max_prob[:, 1:]

      for index in range(A__    ):
       snake_case				=			preds_str[index].find(A__    )
       snake_case				=			preds_str[index][:pred_eos]
       snake_case				=			preds_index[index].cpu().tolist()
       snake_case				=			pred_index.index(A__    ) if eos_token in pred_index else -1
       snake_case				=			preds_max_prob[index][: pred_eos_index + 1]
       snake_case				=			pred_max_prob.cumprod(dim=0    )[-1] if pred_max_prob.nelement() != 0 else 0.0
       dec_strs.append(A__    )
       conf_scores.append(A__    )

      return dec_strs, conf_scores







     def     UpperCamelCase			( self    ,   A__    ) ->	int:
      snake_case				=			[seq.replace(''' '''    ,   ''''''    ) for seq in self.char_tokenizer.batch_decode(A__    )]
      return decode_strs







     def     UpperCamelCase			( self    ,   A__    ) ->	List[str]:
      return self.bpe_tokenizer.batch_decode(A__    )







     def     UpperCamelCase			( self    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			[seq.replace(''' '''    ,   ''''''    ) for seq in self.wp_tokenizer.batch_decode(A__    )]
      return decode_strs

