





import pickle

import numpy as np
from matplotlib import pyplot as plt

class 							__SCREAMING_SNAKE_CASE       :






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






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														# save model dict with pickle
														UpperCamelCase__					  =      {
														    """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 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	):
														# read saved model
														with open(__lowerCAmelCase	,			"""rb"""	) as f:
																					UpperCamelCase__					  =      pickle.load(__lowerCAmelCase	)  # noqa: S301

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






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






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														return round(__lowerCAmelCase	,			3	)






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

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






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase="average_pool"	):
														# pooling process
														UpperCamelCase__					  =      len(featuremaps[0]	)
														UpperCamelCase__					  =      int(size_map / size_pooling	)
														UpperCamelCase__					  =      []
														for i_map in range(len(__lowerCAmelCase	)	):
																					UpperCamelCase__					  =      featuremaps[i_map]
																					UpperCamelCase__					  =      []
																					for i_focus in range(0	,			__lowerCAmelCase	,			__lowerCAmelCase	):
																												for j_focus in range(0	,			__lowerCAmelCase	,			__lowerCAmelCase	):
																																			UpperCamelCase__					  =      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	)	)
																					UpperCamelCase__					  =      np.asmatrix(__lowerCAmelCase	).reshape(__lowerCAmelCase	,			__lowerCAmelCase	)
																					featuremap_pooled.append(__lowerCAmelCase	)
														return featuremap_pooled






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






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														# expanding matrix to one dimension list
														UpperCamelCase__					  =      np.asarray(__lowerCAmelCase	)
														UpperCamelCase__					  =      np.shape(__lowerCAmelCase	)
														UpperCamelCase__					  =      data_mat.reshape(1	,			shapes[0] * shapes[1]	)
														return data_expanded






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	):
														UpperCamelCase__					  =      []
														UpperCamelCase__					  =      0
														for i_map in range(__lowerCAmelCase	):
																					UpperCamelCase__					  =      np.ones((size_map, size_map)	)
																					for i in range(0	,			__lowerCAmelCase	,			__lowerCAmelCase	):
																												for j in range(0	,			__lowerCAmelCase	,			__lowerCAmelCase	):
																																			UpperCamelCase__					  =      pd_pool[
																																			    i_pool
																																			]
																																			UpperCamelCase__					  =      i_pool + 1
																					UpperCamelCase__					  =      np.multiply(
																					    __lowerCAmelCase	,			np.multiply(out_map[i_map]	,			(1 - out_map[i_map])	)	)
																					pd_all.append(__lowerCAmelCase	)
														return pd_all






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

																												UpperCamelCase__					  =      np.dot(__lowerCAmelCase	,			self.vji.T	) - self.thre_bpa
																												UpperCamelCase__					  =      self.sig(__lowerCAmelCase	)
																												UpperCamelCase__					  =      np.dot(__lowerCAmelCase	,			self.wkj.T	) - self.thre_bpa
																												UpperCamelCase__					  =      self.sig(__lowerCAmelCase	)

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

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

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

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

														def draw_error():
																					UpperCamelCase__					  =      [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 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														# model predict
														UpperCamelCase__					  =      []
														print("""-------------------Start Testing-------------------------"""	)
														print((""" - - Shape: Test_Data  """, np.shape(__lowerCAmelCase	))	)
														for p in range(len(__lowerCAmelCase	)	):
																					UpperCamelCase__					  =      np.asmatrix(datas_test[p]	)
																					UpperCamelCase__  ,  UpperCamelCase__					  =      self.convolute(
																					    __lowerCAmelCase	,			self.conva	,			self.w_conva	,			self.thre_conva	,			conv_step=self.step_conva	,			)
																					UpperCamelCase__					  =      self.pooling(__lowerCAmelCase	,			self.size_poolinga	)
																					UpperCamelCase__					  =      self._expand(__lowerCAmelCase	)

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







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														# return the data of image after convoluting process so we can check it out
														UpperCamelCase__					  =      np.asmatrix(__lowerCAmelCase	)
														UpperCamelCase__  ,  UpperCamelCase__					  =      self.convolute(
														    __lowerCAmelCase	,			self.conva	,			self.w_conva	,			self.thre_conva	,			conv_step=self.step_conva	,			)
														UpperCamelCase__					  =      self.pooling(__lowerCAmelCase	,			self.size_poolinga	)

														return data_conveda, data_pooleda


if __name__ == "__main__":
					pass











import os

import pytest

from transformers.dynamic_module_utils import get_imports


UpperCamelCase__           =			"\nimport os\n"

UpperCamelCase__           =			"\ndef foo():\n    import os\n    return False\n"

UpperCamelCase__           =			"\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n"

UpperCamelCase__           =			"\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n"

UpperCamelCase__           =			[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]





@pytest.mark.parametrize("""case""" ,      a__							)
def      _UpperCamelCase  (a__ :Any ,      a__ :Optional[Any]							):






							"""simple docstring"""





							UpperCamelCase__					  =      os.path.join(a__ ,      """test_file.py"""							)
							with open(a__ ,      """w"""							) as _tmp_file:
														_tmp_file.write(a__							)

							UpperCamelCase__					  =      get_imports(a__							)
							assert parsed_imports == ["os"]




