



import pickle

import numpy as np
from matplotlib import pyplot as plt

class   snake_case__							:







						"""simple docstring"""




						def __init__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase=0.2   ,  __lowercase=0.2    )   -> Any:
									"""simple docstring"""




									a__  :		List[Any]					=    bp_numa
									a__  :		str					=    bp_numa
									a__  :		Any					=    bp_numa
									a__  :		Tuple					=    conva_get[:2]
									a__  :		Optional[int]					=    conva_get[2]
									a__  :		Any					=    size_pa
									a__  :		Dict					=    rate_w
									a__  :		Tuple					=    rate_t
									a__  :		int					=    [
									    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[Any]					=    np.mat(-1 * np.random.rand(self.num_bpa   ,  self.num_bpa    ) + 0.5    )
									a__  :		Union[str, Any]					=    -2 * np.random.rand(self.conva[1]    ) + 1
									a__  :		Optional[int]					=    -2 * np.random.rand(self.num_bpa    ) + 1
									a__  :		int					=    -2 * np.random.rand(self.num_bpa    ) + 1

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> int:
									"""simple docstring"""




									a__  :		str					=    {
									    "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(__lowercase   ,  """wb"""    ) as f:
												pickle.dump(__lowercase   ,  __lowercase    )

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

						@classmethod
						def 				SCREAMING_SNAKE_CASE__(  cls   ,  __lowercase    )   -> List[Any]:
									"""simple docstring"""




									with open(__lowercase   ,  """rb"""    ) as f:
												a__  :		List[str]					=    pickle.load(__lowercase    )  # noqa: S301

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

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> Optional[int]:
									"""simple docstring"""




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

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> str:
									"""simple docstring"""




									return round(__lowercase   ,  3    )

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase    )   -> Optional[int]:
									"""simple docstring"""




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

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

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase="average_pool"    )   -> Tuple:
									"""simple docstring"""




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

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> Optional[Any]:
									"""simple docstring"""




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

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> str:
									"""simple docstring"""




									a__  :		Dict					=    np.asarray(__lowercase    )
									a__  :		List[Any]					=    np.shape(__lowercase    )
									a__  :		Optional[Any]					=    data_mat.reshape(1   ,  shapes[0] * shapes[1]    )
									return data_expanded

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase    )   -> Any:
									"""simple docstring"""




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

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase=bool    )   -> Optional[int]:
									"""simple docstring"""




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

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

															# --------------Model Leaning ------------------------
															# calculate error and gradient---------------
															a__  :		str					=    np.multiply(
															    (data_teach - bp_outa)   ,  np.multiply(__lowercase   ,  (1 - bp_outa)    )    )
															a__  :		List[Any]					=    np.multiply(
															    np.dot(__lowercase   ,  self.wkj    )   ,  np.multiply(__lowercase   ,  (1 - bp_outa)    )    )
															a__  :		List[str]					=    np.dot(__lowercase   ,  self.vji    )

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

																		a__  :		Dict					=    self.w_conva[k_conv] + delta_w.reshape(
																		    (self.conva[0], self.conva[0])    )

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

									def draw_error():
												a__  :		List[Any]					=    [error_accuracy for i in range(int(n_repeat * 1.2    )    )]
												plt.plot(__lowercase   ,  """+-"""    )
												plt.plot(__lowercase   ,  """r--"""    )
												plt.xlabel("""Learning Times"""    )
												plt.ylabel("""All_mse"""    )
												plt.grid(__lowercase   ,  alpha=0.5    )
												plt.show()

									print("""------------------Training Complished---------------------"""    )
									print((""" - - Training epoch: """, rp, F'''     - - Mse: {mse:.6f}''')    )
									if draw_e:
												draw_error()
									return mse

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> Any:
									"""simple docstring"""




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

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

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase    )   -> Union[str, Any]:
									"""simple docstring"""




									a__  :		Dict					=    np.asmatrix(__lowercase    )
									a__  :		Optional[int]					=    self.convolute(
									    __lowercase   ,  self.conva   ,  self.w_conva   ,  self.thre_conva   ,  conv_step=self.step_conva   ,  )
									a__  :		Any					=    self.pooling(__lowercase   ,  self.size_poolinga    )

									return data_conveda, data_pooleda


if __name__ == "__main__":
		pass











import argparse
import os
import re


SCREAMING_SNAKE_CASE__							:      Any         =    """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")






def    _A	(   lowerCamelCase				,			lowerCamelCase = False ):
   with open(lowerCamelCase				,			"r"				,			encoding="utf-8" ) as f:
      a__						:    Optional[int]   		=	f.read()

   a__						:    Optional[Any]   		=	content.split("\n" )
   a__						:    Optional[int]   		=	[]
   a__						:    Optional[Any]   		=	0
   while line_idx < len(lowerCamelCase ):
      if _re_intro_mapping.search(lines[line_idx] ) is not None:
         a__						:    str   		=	len(re.search(r"^(\s*)\S"				,			lines[line_idx] ).groups()[0] ) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "(" ):
            new_lines.append(lines[line_idx] )
            line_idx += 1

         a__						:    Union[str, Any]   		=	[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               a__						:    int   		=	line_idx
               while not lines[line_idx].startswith(" " * indent + ")" ):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1] ) )
            else:
               blocks.append(lines[line_idx] )
            line_idx += 1

         # Sort blocks by their identifiers
         a__						:    Optional[Any]   		=	sorted(lowerCamelCase				,			key=lambda lowerCamelCase : _re_identifier.search(lowerCamelCase ).groups()[0] )
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx] )
         line_idx += 1

   if overwrite:
      with open(lowerCamelCase				,			"w"				,			encoding="utf-8" ) as f:
         f.write("\n".join(lowerCamelCase ) )
   elif "\n".join(lowerCamelCase ) != content:
      return True






def    _A	(   lowerCamelCase = False ):
   a__						:    List[str]   		=	[os.path.join(lowerCamelCase				,			lowerCamelCase ) for f in os.listdir(lowerCamelCase ) if f.endswith(".py" )]
   a__						:    Any   		=	[sort_auto_mapping(lowerCamelCase				,			overwrite=lowerCamelCase ) for fname in fnames]

   if not overwrite and any(lowerCamelCase ):
      a__						:    Dict   		=	[f for f, d in zip(lowerCamelCase				,			lowerCamelCase ) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(lowerCamelCase )}. Run `make style` to fix"""
          " this." )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Dict         =    argparse.ArgumentParser()
  parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    parser.parse_args()

  sort_all_auto_mappings(not args.check_only)

