




'''simple docstring'''





import pickle

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




				'''simple docstring'''





				def __init__(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__=0.2  ,						SCREAMING_SNAKE_CASE__=0.2				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Tuple        =       {
							    '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        _UpperCamelCase							(   cls  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							with open(lowercase_  ,						'rb'				) as f:
										snake_case:       Optional[Any]        =       pickle.load(lowercase_				)  # noqa: S301

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







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							return round(lowercase_  ,						3				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







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

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







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__="average_pool"				):
							'''simple docstring'''







							snake_case:       Union[str, Any]        =       len(featuremaps[0]				)
							snake_case:       Union[str, Any]        =       int(size_map / size_pooling				)
							snake_case:       int        =       []
							for i_map in range(len(lowercase_				)				):
										snake_case:       Optional[Any]        =       featuremaps[i_map]
										snake_case:       Optional[int]        =       []
										for i_focus in range(0  ,						lowercase_  ,						lowercase_				):
													for j_focus in range(0  ,						lowercase_  ,						lowercase_				):
																snake_case:       List[Any]        =       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_				)				)
										snake_case:       List[str]        =       np.asmatrix(lowercase_				).reshape(lowercase_  ,						lowercase_				)
										featuremap_pooled.append(lowercase_				)
							return featuremap_pooled







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Dict        =       []
							for i in range(len(lowercase_				)				):
										snake_case:       int        =       np.shape(data[i]				)
										snake_case:       Tuple        =       data[i].reshape(1  ,						shapes[0] * shapes[1]				)
										snake_case:       Any        =       data_listed.getA().tolist()[0]
										data_expanded.extend(lowercase_				)
							snake_case:       Any        =       np.asarray(lowercase_				)
							return data_expanded







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       str        =       np.asarray(lowercase_				)
							snake_case:       str        =       np.shape(lowercase_				)
							snake_case:       Any        =       data_mat.reshape(1  ,						shapes[0] * shapes[1]				)
							return data_expanded







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__=bool				):
							'''simple docstring'''







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

													snake_case:       Tuple        =       np.dot(lowercase_  ,						self.vji.T				) - self.thre_bpa
													snake_case:       Any        =       self.sig(lowercase_				)
													snake_case:       List[str]        =       np.dot(lowercase_  ,						self.wkj.T				) - self.thre_bpa
													snake_case:       str        =       self.sig(lowercase_				)

													# --------------Model Leaning ------------------------
													# calculate error and gradient---------------
													snake_case:       int        =       np.multiply(
													    (data_teach - bp_outa)  ,						np.multiply(lowercase_  ,						(1 - bp_outa)				)				)
													snake_case:       Union[str, Any]        =       np.multiply(
													    np.dot(lowercase_  ,						self.wkj				)  ,						np.multiply(lowercase_  ,						(1 - bp_outa)				)				)
													snake_case:       Union[str, Any]        =       np.dot(lowercase_  ,						self.vji				)

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

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

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

							def draw_error():
										snake_case:       Optional[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        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







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

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







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Union[str, Any]        =       np.asmatrix(lowercase_				)
							snake_case    ,						snake_case:       Dict        =       self.convolute(
							    lowercase_  ,						self.conva  ,						self.w_conva  ,						self.thre_conva  ,						conv_step=self.step_conva  ,						)
							snake_case:       List[Any]        =       self.pooling(lowercase_  ,						self.size_poolinga				)

							return data_conveda, data_pooleda


if __name__ == "__main__":
			pass
'''simple docstring'''


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


__snake_case						  =		logging.get_logger(__name__)





@dataclass
class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						[
      '''no_inference''',
      '''no_cuda''',
      '''no_tpu''',
      '''no_speed''',
      '''no_memory''',
      '''no_env_print''',
      '''no_multi_process''',
  ]




  def __init__(		self      :       int   ,				**lowercase_      :       int      ):







    '''simple docstring'''


    for deprecated_arg in self.deprecated_args:
      if deprecated_arg in kwargs:
        lowercase_     =       deprecated_arg[3:]
        setattr(self   ,				lowercase_   ,				not kwargs.pop(lowercase_      )      )
        logger.warning(
            F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
            F""" {positive_arg}={kwargs[positive_arg]}"""      )

    lowercase_     =       kwargs.pop("""torchscript"""   ,				self.torchscript      )
    lowercase_     =       kwargs.pop("""torch_xla_tpu_print_metrics"""   ,				self.torch_xla_tpu_print_metrics      )
    lowercase_     =       kwargs.pop("""fp16_opt_level"""   ,				self.fpaa_opt_level      )
    super().__init__(**lowercase_      )

  A_    							=						field(default=__a ,				metadata={'''help''': '''Trace the models using torchscript'''}      )
  A_    							=						field(default=__a ,				metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}      )
  A_    							=						field(
      default='''O1''' ,				metadata={
          '''help''': (
              '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
              '''See details at https://nvidia.github.io/apex/amp.html'''
          )
      } ,				)




  @cached_property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    logger.info("""PyTorch: setting up devices"""      )
    if not self.cuda:
      lowercase_     =       torch.device("""cpu"""      )
      lowercase_     =       0
    elif is_torch_tpu_available():
      lowercase_     =       xm.xla_device()
      lowercase_     =       0
    else:
      lowercase_     =       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )
      lowercase_     =       torch.cuda.device_count()
    return device, n_gpu




  @property
  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    return is_torch_tpu_available() and self.tpu




  @property
  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    # TODO(PVP): currently only single GPU is supported
    return torch.cuda.current_device()




  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    return self._setup_devices[0]




  @property
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    requires_backends(self   ,				["""torch"""]      )
    return self._setup_devices[1]





  @property
  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    return self.n_gpu > 0

