
'''simple docstring'''





import numpy as np
from matplotlib import pyplot as plt





def 							snake_case				(		UpperCAmelCase						)->      Dict:







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


class 			UpperCamelCase__	:






 def __init__(      self	:Tuple						,					_A	:Optional[Any]						,					_A	:str=None						,					_A	:Tuple=None						,					_A	:List[Any]=False      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      units
       __A													=      None
       __A													=      None
       __A													=      activation
       if learning_rate is None:
             __A													=      0.3
       __A													=      learning_rate
       __A													=      is_input_layer






 def 						lowercase_    (      self	:Dict						,					_A	:Any      )					->      Tuple:







       '''simple docstring'''



       __A													=      np.asmatrix(np.random.normal(0						,					0.5						,					(self.units, back_units)      )      )
       __A													=      np.asmatrix(np.random.normal(0						,					0.5						,					self.units      )      ).T
       if self.activation is None:
             __A													=      sigmoid






 def 						lowercase_    (      self	:List[Any]      )					->      Tuple:







       '''simple docstring'''



       if self.activation == sigmoid:
             __A													=      np.dot(self.output						,					(1 - self.output).T      )
             __A													=      np.diag(np.diag(_A      )      )
       else:
             __A													=      1
       return gradient_activation






 def 						lowercase_    (      self	:int						,					_A	:Union[str, Any]      )					->      str:







       '''simple docstring'''



       __A													=      xdata
       if self.is_input_layer:
             # input layer
             __A													=      xdata
             __A													=      xdata
             return xdata
       else:
             __A													=      np.dot(self.weight						,					self.xdata      ) - self.bias
             __A													=      self.activation(self.wx_plus_b      )
             return self.output






 def 						lowercase_    (      self	:Tuple						,					_A	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      self.cal_gradient()  # i * i 维
       __A													=      np.asmatrix(np.dot(gradient.T						,					_A      )      )

       __A													=      np.asmatrix(self.xdata      )
       __A													=      -1
       __A													=      self.weight

       __A													=      np.dot(gradient.T						,					self._gradient_weight.T      )
       __A													=      gradient * self._gradient_bias
       __A													=      np.dot(_A						,					self._gradient_x      ).T
       # upgrade: the Negative gradient direction
       __A													=      self.weight - self.learn_rate * self.gradient_weight
       __A													=      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__	:






 def __init__(      self	:int      )					->      List[str]:







       '''simple docstring'''



       __A													=      []
       __A													=      []
       __A													=      plt.figure()
       __A													=      self.fig_loss.add_subplot(1						,					1						,					1      )






 def 						lowercase_    (      self	:Union[str, Any]						,					_A	:Tuple      )					->      Union[str, Any]:







       '''simple docstring'''



       self.layers.append(_A      )






 def 						lowercase_    (      self	:List[Any]      )					->      Any:







       '''simple docstring'''



       for i, layer in enumerate(self.layers[:]      ):
             if i < 1:
                   __A													=      True
             else:
                   layer.initializer(self.layers[i - 1].units      )






 def 						lowercase_    (      self	:Tuple      )					->      List[str]:







       '''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 						lowercase_    (      self	:Tuple						,					_A	:List[str]						,					_A	:Optional[int]						,					_A	:Union[str, Any]						,					_A	:str      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      train_round
       __A													=      accuracy

       self.ax_loss.hlines(self.accuracy						,					0						,					self.train_round * 1.1      )

       __A													=      np.shape(_A      )
       for _ in range(_A      ):
             __A													=      0
             for row in range(x_shape[0]      ):
                   __A													=      np.asmatrix(xdata[row, :]      ).T
                   __A													=      np.asmatrix(ydata[row, :]      ).T

                   # forward propagation
                   for layer in self.layers:
                         __A													=      layer.forward_propagation(_xdata      )

                   __A						,   __A													=      self.cal_loss(_ydata						,					_xdata      )
                   __A													=      all_loss + loss

                   # back propagation: the input_layer does not upgrade
                   for layer in self.layers[:0:-1]:
                         __A													=      layer.back_propagation(_A      )

             __A													=      all_loss / x_shape[0]
             self.train_mse.append(_A      )

             self.plot_loss()

             if mse < self.accuracy:
                   print('----达到精度----'      )
                   return mse
       return None






 def 						lowercase_    (      self	:Dict						,					_A	:Tuple						,					_A	:str      )					->      Tuple:







       '''simple docstring'''



       __A													=      np.sum(np.power((ydata - ydata_)						,					2      )      )
       __A													=      2 * (ydata_ - ydata)
       # vector (shape is the same as _ydata.shape)
       return self.loss, self.loss_gradient






 def 						lowercase_    (      self	:Tuple      )					->      Dict:







       '''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 							snake_case				(		)->      List[str]:







      """simple docstring"""
      __A													=      np.random.randn(1_0   ,	1_0						)
      __A													=      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],
          ]						)
      __A													=      BPNN()
      for i in (1_0, 2_0, 3_0, 2):
            model.add_layer(DenseLayer(UpperCAmelCase						)						)
      model.build()
      model.summary()
      model.train(xdata=UpperCAmelCase   ,	ydata=UpperCAmelCase   ,	train_round=1_0_0   ,	accuracy=0.01						)


if __name__ == "__main__":
 example()














"""simple docstring"""



import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
						import torch

						from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
						import pyanvml.pyanvml as nvml


_snake_case							:   str	  =       logging.get_logger(__name__)




class 							__lowercase(							_UpperCamelCase	):
						lowerCAmelCase_		:				PyTorchBenchmarkArguments
						lowerCAmelCase_		:				PretrainedConfig
						lowerCAmelCase_		:				str=							"PyTorch"






						@property
						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





											return torch.__version__






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	float:




											"""simple docstring"""





											lowerCamelCase__					=self._prepare_inference_func(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											return self._measure_speed(_inference)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	[Memory, Optional[MemorySummary]]:




											"""simple docstring"""





											lowerCamelCase__					=self._prepare_inference_func(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											return self._measure_memory(_inference)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	float:




											"""simple docstring"""





											lowerCamelCase__					=self._prepare_train_func(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											return self._measure_speed(_train)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	[Memory, Optional[MemorySummary]]:




											"""simple docstring"""





											lowerCamelCase__					=self._prepare_train_func(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											return self._measure_memory(_train)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	Callable[[], None]:




											"""simple docstring"""





											lowerCamelCase__					=self.config_dict[model_name]

											if self.args.torchscript:
																lowerCamelCase__					=True

											lowerCamelCase__					=(
											    hasattr(_SCREAMING_SNAKE_CASE     ,    'architectures')
											    and isinstance(config.architectures     ,    _SCREAMING_SNAKE_CASE)
											    and len(config.architectures) > 0
											)
											if not self.args.only_pretrain_model and has_model_class_in_config:
																try:
																					lowerCamelCase__					=config.architectures[0]
																					lowerCamelCase__					=__import__('transformers'     ,    fromlist=[model_class])
																					lowerCamelCase__					=getattr(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
																					lowerCamelCase__					=model_cls(_SCREAMING_SNAKE_CASE)
																except ImportError:
																					raise ImportError(
																					    F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
																					    ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.')
											else:
																lowerCamelCase__					=MODEL_MAPPING[config.__class__](_SCREAMING_SNAKE_CASE)

											model.eval()
											model.to(self.args.device)

											# encoder-decoder has vocab size saved differently
											lowerCamelCase__					=config.vocab_size if hasattr(_SCREAMING_SNAKE_CASE     ,    'vocab_size') else config.encoder.vocab_size
											lowerCamelCase__					=torch.randint(_SCREAMING_SNAKE_CASE     ,    (batch_size, sequence_length)     ,    dtype=torch.long     ,    device=self.args.device)

											if self.args.fpaa:
																logger.info('Running training in Mixed Precision...')
																if not self.args.is_gpu:
																					raise ValueError('Mixed precision is possible only for GPU.')
																# amp seems to have memory leaks so that memory usage
																# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
																model.half()

											if self.args.torchscript:
																with torch.no_grad():
																					lowerCamelCase__					=torch.jit.trace(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											else:
																lowerCamelCase__					=model

											def encoder_decoder_forward():
																with torch.no_grad():
																					lowerCamelCase__					=inference_model(_SCREAMING_SNAKE_CASE     ,    decoder_input_ids=_SCREAMING_SNAKE_CASE)
																return outputs

											def encoder_forward():
																with torch.no_grad():
																					lowerCamelCase__					=inference_model(_SCREAMING_SNAKE_CASE)
																return outputs

											lowerCamelCase__					=encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
											return _forward






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	Callable[[], None]:




											"""simple docstring"""





											lowerCamelCase__					=self.config_dict[model_name]

											lowerCamelCase__					=(
											    hasattr(_SCREAMING_SNAKE_CASE     ,    'architectures')
											    and isinstance(config.architectures     ,    _SCREAMING_SNAKE_CASE)
											    and len(config.architectures) > 0
											)
											if not self.args.only_pretrain_model and has_model_class_in_config:
																try:
																					lowerCamelCase__					=config.architectures[0]
																					lowerCamelCase__					=__import__('transformers'     ,    fromlist=[model_class])
																					lowerCamelCase__					=getattr(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
																					lowerCamelCase__					=model_cls(_SCREAMING_SNAKE_CASE)
																except ImportError:
																					raise ImportError(
																					    F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
																					    ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.')
											else:
																lowerCamelCase__					=MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_SCREAMING_SNAKE_CASE)

											if self.args.torchscript:
																raise NotImplementedError('Training for torchscript is currently not implemented')
											else:
																lowerCamelCase__					=model

											model.train()
											model.to(self.args.device)

											# encoder-decoder has vocab size saved differently
											lowerCamelCase__					=config.vocab_size if hasattr(_SCREAMING_SNAKE_CASE     ,    'vocab_size') else config.encoder.vocab_size
											lowerCamelCase__					=torch.randint(_SCREAMING_SNAKE_CASE     ,    (batch_size, sequence_length)     ,    dtype=torch.long     ,    device=self.args.device)

											if self.args.fpaa:
																logger.info('Running training in Mixed Precision...')
																if not self.args.is_gpu:
																					raise ValueError('Mixed precision is possible only for GPU.')

																# amp seems to have memory leaks so that memory usage
																# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
																model.half()

											def compute_loss_and_backprob_encoder():
																lowerCamelCase__					=train_model(_SCREAMING_SNAKE_CASE     ,    labels=_SCREAMING_SNAKE_CASE)[0]
																loss.backward()
																return loss

											def compute_loss_and_backprob_encoder_decoder():
																lowerCamelCase__					=train_model(_SCREAMING_SNAKE_CASE     ,    decoder_input_ids=_SCREAMING_SNAKE_CASE     ,    labels=_SCREAMING_SNAKE_CASE)[0]
																loss.backward()
																return loss

											lowerCamelCase__					=(
											    compute_loss_and_backprob_encoder_decoder
											    if config.is_encoder_decoder
											    else compute_loss_and_backprob_encoder
											)
											return _train






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	float:




											"""simple docstring"""





											try:
																if self.args.is_tpu or self.args.torchscript:
																					# run additional 10 times to stabilize compilation for tpu and torchscript
																					logger.info('Do inference on TPU or torchscript. Running model 5 times to stabilize compilation')
																					timeit.repeat(
																					    _SCREAMING_SNAKE_CASE     ,    repeat=1     ,    number=5     ,    )

																# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
																lowerCamelCase__					=timeit.repeat(
																    _SCREAMING_SNAKE_CASE     ,    repeat=self.args.repeat     ,    number=10     ,    )

																if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																					import torch_xla.debug.metrics as met

																					self.print_fn(met.metrics_report())

																return min(_SCREAMING_SNAKE_CASE) / 10.0
											except RuntimeError as e:
																self.print_fn(F"Doesn't fit on GPU. {e}")
																return "N/A"






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	[Memory, MemorySummary]:




											"""simple docstring"""





											try:
																if self.args.trace_memory_line_by_line:
																					lowerCamelCase__					=start_memory_tracing('transformers')

																if self.args.is_tpu:
																					# tpu
																					raise NotImplementedError(
																					    'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'
																					    ' `--no-memory` or `args.memory=False`')
																elif self.args.is_gpu:
																					if not is_pyanvml_available():
																										logger.warning(
																										    'py3nvml not installed, we won\'t log GPU memory usage. '
																										    'Install py3nvml (pip install py3nvml) to log information about GPU.')
																										lowerCamelCase__					='N/A'
																					else:
																										logger.info(
																										    'Measuring total GPU usage on GPU device. Make sure to not have additional processes running'
																										    ' on the same GPU.')
																										# init nvml
																										nvml.nvmlInit()
																										func()
																										lowerCamelCase__					=nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
																										lowerCamelCase__					=nvml.nvmlDeviceGetMemoryInfo(_SCREAMING_SNAKE_CASE)
																										lowerCamelCase__					=meminfo.used
																										lowerCamelCase__					=Memory(_SCREAMING_SNAKE_CASE)
																										# shutdown nvml
																										nvml.nvmlShutdown()
																else:
																					# cpu
																					lowerCamelCase__					=measure_peak_memory_cpu(_SCREAMING_SNAKE_CASE)
																					lowerCamelCase__					=Memory(_SCREAMING_SNAKE_CASE) if isinstance(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE) else memory_bytes

																if self.args.trace_memory_line_by_line:
																					lowerCamelCase__					=stop_memory_tracing(_SCREAMING_SNAKE_CASE)
																else:
																					lowerCamelCase__					=None

																return memory, summary
											except RuntimeError as e:
																self.print_fn(F"Doesn't fit on GPU. {e}")
																return "N/A", None