code: 
  
  
  
  
  
  
  """simple docstring"""
  import csv
  from collections import defaultdict
  from dataclasses import dataclass, field
  from typing import List, Optional
  
  import matplotlib.pyplot as plt
  import numpy as np
  from matplotlib.ticker import ScalarFormatter
  
  from transformers import HfArgumentParser
  
  
  def        A	(							__snake_case:   Any=None   ,    __snake_case:   Union[str, Any]=None     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         return field(default_factory=lambda: default   ,    metadata=lowercase__     )
  
  
  
  @dataclass
  class 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   field(
          metadata={"""help""": """The csv file to plot."""}       ,  )
      __UpperCAmelCase											=   field(
          default=lowerCamelCase_       ,  metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}       ,  )
      __UpperCAmelCase											=   field(
          default=lowerCamelCase_       ,  metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}       ,  )
      __UpperCAmelCase											=   field(
          default=lowerCamelCase_       ,  metadata={"""help""": """Disable logarithmic scale when plotting"""}       ,  )
      __UpperCAmelCase											=   field(
          default=lowerCamelCase_       ,  metadata={
              """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
          }       ,  )
      __UpperCAmelCase											=   field(
          default=lowerCamelCase_       ,  metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}       ,  )
      __UpperCAmelCase											=   list_field(
          default=lowerCamelCase_       ,  metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""})
  
  
  def        A	(							__snake_case:   int     )						->		List[str]:
  
         """simple docstring"""
  
  
  
  
  
         try:
                int(lowercase__     )
                return True
         except ValueError:
                return False
  
  
  def        A	(							__snake_case:   str     )						->		List[Any]:
  
         """simple docstring"""
  
  
  
  
  
         try:
                float(lowercase__     )
                return True
         except ValueError:
                return False
  
  
  
  class 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Dict							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						args
             __magic_name__										=						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}   )
  
             with open(self.args.csv_file							,						newline=''   ) as csv_file:
                    __magic_name__										=						csv.DictReader(UpperCamelCase_   )
                    for row in reader:
                           __magic_name__										=						row["model"]
                           self.result_dict[model_name]["bsz"].append(int(row['batch_size']   )   )
                           self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']   )   )
                           if can_convert_to_int(row['result']   ):
                                  # value is not None
                                  __magic_name__										=						int(row['result']   )
                           elif can_convert_to_float(row['result']   ):
                                  # value is not None
                                  __magic_name__										=						float(row['result']   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						plt.subplots()
             __magic_name__										=						"Time usage" if self.args.is_time else "Memory usage"
             __magic_name__										=						title_str + " for training" if self.args.is_train else title_str + " for inference"
  
             if not self.args.no_log_scale:
                    # set logarithm scales
                    ax.set_xscale('log'   )
                    ax.set_yscale('log'   )
  
             for axis in [ax.xaxis, ax.yaxis]:
                    axis.set_major_formatter(ScalarFormatter()   )
  
             for model_name_idx, model_name in enumerate(self.result_dict.keys()   ):
                    __magic_name__										=						sorted(set(self.result_dict[model_name]['bsz']   )   )
                    __magic_name__										=						sorted(set(self.result_dict[model_name]['seq_len']   )   )
                    __magic_name__										=						self.result_dict[model_name]["result"]
  
                    (__magic_name__)										=						(
                        (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                    )
  
                    __magic_name__										=						(
                        model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
                    )
  
                    for inner_loop_value in inner_loop_array:
                           if self.args.plot_along_batch:
                                  __magic_name__										=						np.asarray(
                                      [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,						dtype=UpperCamelCase_							,						)
                           else:
                                  __magic_name__										=						np.asarray(
                                      [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							,						dtype=np.floataa							,						)
  
                           (__magic_name__)										=						(
                               ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                           )
  
                           __magic_name__										=						np.asarray(UpperCamelCase_							,						UpperCamelCase_   )[: len(UpperCamelCase_   )]
                           plt.scatter(
                               UpperCamelCase_							,						UpperCamelCase_							,						label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""   )
                           plt.plot(UpperCamelCase_							,						UpperCamelCase_							,						'--'   )
  
                    title_str += f""" {label_model_name} vs."""
  
             __magic_name__										=						title_str[:-4]
             __magic_name__										=						"Time in s" if self.args.is_time else "Memory in MB"
  
             # plot
             plt.title(UpperCamelCase_   )
             plt.xlabel(UpperCamelCase_   )
             plt.ylabel(UpperCamelCase_   )
             plt.legend()
  
             if self.args.figure_png_file is not None:
                    plt.savefig(self.args.figure_png_file   )
             else:
                    plt.show()
  
  
  def        A	(							)						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						HfArgumentParser(lowercase__     )
         __magic_name__										=						parser.parse_args_into_dataclasses()[0]
         __magic_name__										=						Plot(args=lowercase__     )
         plot.plot()
  
  
  if __name__ == "__main__":
   main()
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  from matplotlib import pyplot as plt
  
  
  def        A	(							__snake_case:   List[Any]     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         return 1 / (1 + np.exp(-1 * x     ))
  
  
  
  class 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Tuple							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  Optional[int]=None							,						UpperCamelCase_   :  List[str]=None							,						UpperCamelCase_   :  Tuple=False   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						units
             __magic_name__										=						None
             __magic_name__										=						None
             __magic_name__										=						activation
             if learning_rate is None:
                    __magic_name__										=						0.3
             __magic_name__										=						learning_rate
             __magic_name__										=						is_input_layer
  
  
  
  
  
  
  
      def 							a__ (		self   :  str							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						np.asmatrix(np.random.normal(0							,						0.5							,						(self.units, back_units)   )   )
             __magic_name__										=						np.asmatrix(np.random.normal(0							,						0.5							,						self.units   )   ).T
             if self.activation is None:
                    __magic_name__										=						sigmoid
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self.activation == sigmoid:
                    __magic_name__										=						np.dot(self.output							,						(1 - self.output).T   )
                    __magic_name__										=						np.diag(np.diag(UpperCamelCase_   )   )
             else:
                    __magic_name__										=						1
             return gradient_activation
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[Any]							,						UpperCamelCase_   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						xdata
             if self.is_input_layer:
                    # input layer
                    __magic_name__										=						xdata
                    __magic_name__										=						xdata
                    return xdata
             else:
                    __magic_name__										=						np.dot(self.weight							,						self.xdata   ) - self.bias
                    __magic_name__										=						self.activation(self.wx_plus_b   )
                    return self.output
  
  
  
  
  
  
  
      def 							a__ (		self   :  str							,						UpperCamelCase_   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.cal_gradient()  # i * i 维
             __magic_name__										=						np.asmatrix(np.dot(gradient.T							,						UpperCamelCase_   )   )
  
             __magic_name__										=						np.asmatrix(self.xdata   )
             __magic_name__										=						-1
             __magic_name__										=						self.weight
  
             __magic_name__										=						np.dot(gradient.T							,						self._gradient_weight.T   )
             __magic_name__										=						gradient * self._gradient_bias
             __magic_name__										=						np.dot(UpperCamelCase_							,						self._gradient_x   ).T
             # upgrade: the Negative gradient direction
             __magic_name__										=						self.weight - self.learn_rate * self.gradient_weight
             __magic_name__										=						self.bias - self.learn_rate * self.gradient_bias.T
             # updates the weights and bias according to learning rate (0.3 if undefined)
             return self.gradient
  
  
  
  class 					UpperCamelCase__   :
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  List[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						[]
             __magic_name__										=						[]
             __magic_name__										=						plt.figure()
             __magic_name__										=						self.fig_loss.add_subplot(1							,						1							,						1   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[str]							,						UpperCamelCase_   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             self.layers.append(UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             for i, layer in enumerate(self.layers[:]   ):
                    if i < 1:
                           __magic_name__										=						True
                    else:
                           layer.initializer(self.layers[i - 1].units   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Any   ):
  
  
  
  
  
             '''simple docstring'''
  
             for i, layer in enumerate(self.layers[:]   ):
                    print(f"""------- layer {i} -------"""   )
                    print('weight.shape '							,						np.shape(layer.weight   )   )
                    print('bias.shape '							,						np.shape(layer.bias   )   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  str							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Any							,						UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						train_round
             __magic_name__										=						accuracy
  
             self.ax_loss.hlines(self.accuracy							,						0							,						self.train_round * 1.1   )
  
             __magic_name__										=						np.shape(UpperCamelCase_   )
             for _ in range(UpperCamelCase_   ):
                    __magic_name__										=						0
                    for row in range(x_shape[0]   ):
                           __magic_name__										=						np.asmatrix(xdata[row, :]   ).T
                           __magic_name__										=						np.asmatrix(ydata[row, :]   ).T
  
                           # forward propagation
                           for layer in self.layers:
                                  __magic_name__										=						layer.forward_propagation(_xdata   )
  
                           __magic_name__							,     __magic_name__										=						self.cal_loss(_ydata							,						_xdata   )
                           __magic_name__										=						all_loss + loss
  
                           # back propagation: the input_layer does not upgrade
                           for layer in self.layers[:0:-1]:
                                  __magic_name__										=						layer.back_propagation(UpperCamelCase_   )
  
                    __magic_name__										=						all_loss / x_shape[0]
                    self.train_mse.append(UpperCamelCase_   )
  
                    self.plot_loss()
  
                    if mse < self.accuracy:
                           print('----达到精度----'   )
                           return mse
             return None
  
  
  
  
  
  
  
      def 							a__ (		self   :  Union[str, Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						np.sum(np.power((ydata - ydata_)							,						2   )   )
             __magic_name__										=						2 * (ydata_ - ydata)
             # vector (shape is the same as _ydata.shape)
             return self.loss, self.loss_gradient
  
  
  
  
  
  
  
      def 							a__ (		self   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             if self.ax_loss.lines:
                    self.ax_loss.lines.remove(self.ax_loss.lines[0]   )
             self.ax_loss.plot(self.train_mse							,						'r-'   )
             plt.ion()
             plt.xlabel('step'   )
             plt.ylabel('loss'   )
             plt.show()
             plt.pause(0.1   )
  
  
  def        A	(							)						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						np.random.randn(1_0   ,    1_0     )
         __magic_name__										=						np.asarray(
             [
                 [0.8, 0.4],
                 [0.4, 0.3],
                 [0.34, 0.45],
                 [0.67, 0.32],
                 [0.88, 0.67],
                 [0.78, 0.77],
                 [0.55, 0.66],
                 [0.55, 0.43],
                 [0.54, 0.1],
                 [0.1, 0.5],
             ]     )
         __magic_name__										=						BPNN()
         for i in (1_0, 2_0, 3_0, 2):
                model.add_layer(DenseLayer(__snake_case     )     )
         model.build()
         model.summary()
         model.train(xdata=__snake_case   ,    ydata=__snake_case   ,    train_round=1_0_0   ,    accuracy=0.01     )
  
  
  if __name__ == "__main__":
   example()