"""simple docstring"""



import numpy as np
from matplotlib import pyplot as plt

def    _A(		lowerCAmelCase       ):
							return 1 / (1 + np.exp(-1 * x       ))
class 	__UpperCAmelCase						:







			'''simple docstring'''





			def __init__( self      ,			snake_case_      ,			snake_case_=None      ,			snake_case_=None      ,			snake_case_=False       ):







										'''simple docstring'''


										A__					:    str														= units
										A__					:    Union[str, Any]														= None
										A__					:    Optional[Any]														= None
										A__					:    int														= activation
										if learning_rate is None:
																	A__					:    Union[str, Any]														= 0.3
										A__					:    int														= learning_rate
										A__					:    str														= is_input_layer





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    int														= np.asmatrix(np.random.normal(0      ,			0.5      ,			(self.units, back_units)       )       )
										A__					:    str														= np.asmatrix(np.random.normal(0      ,			0.5      ,			self.units       )       ).T
										if self.activation is None:
																	A__					:    List[Any]														= sigmoid





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										if self.activation == sigmoid:
																	A__					:    Optional[int]														= np.dot(self.output      ,			(1 - self.output).T       )
																	A__					:    List[Any]														= np.diag(np.diag(snake_case_       )       )
										else:
																	A__					:    int														= 1
										return gradient_activation





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    List[str]														= xdata
										if self.is_input_layer:
																	# input layer
																	A__					:    str														= xdata
																	A__					:    List[str]														= xdata
																	return xdata
										else:
																	A__					:    List[Any]														= np.dot(self.weight      ,			self.xdata       ) - self.bias
																	A__					:    Union[str, Any]														= self.activation(self.wx_plus_b       )
																	return self.output





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= self.cal_gradient()  # i * i 维
										A__					:    Dict														= np.asmatrix(np.dot(gradient.T      ,			snake_case_       )       )

										A__					:    Union[str, Any]														= np.asmatrix(self.xdata       )
										A__					:    Dict														= -1
										A__					:    str														= self.weight

										A__					:    Union[str, Any]														= np.dot(gradient.T      ,			self._gradient_weight.T       )
										A__					:    Any														= gradient * self._gradient_bias
										A__					:    Optional[int]														= np.dot(snake_case_      ,			self._gradient_x       ).T
										# upgrade: the Negative gradient direction
										A__					:    str														= self.weight - self.learn_rate * self.gradient_weight
										A__					:    Dict														= 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       ):







										'''simple docstring'''


										A__					:    str														= []
										A__					:    int														= []
										A__					:    List[str]														= plt.figure()
										A__					:    int														= self.fig_loss.add_subplot(1      ,			1      ,			1       )





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										self.layers.append(snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''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 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    List[Any]														= train_round
										A__					:    str														= accuracy

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

										A__					:    Optional[Any]														= np.shape(snake_case_       )
										for _ in range(snake_case_       ):
																	A__					:    Optional[Any]														= 0
																	for row in range(x_shape[0]       ):
																								A__					:    Tuple														= np.asmatrix(xdata[row, :]       ).T
																								A__					:    int														= np.asmatrix(ydata[row, :]       ).T

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

																								A__							,       A__					:    Optional[Any]														= self.cal_loss(_ydata      ,			_xdata       )
																								A__					:    Optional[int]														= all_loss + loss

																								# back propagation: the input_layer does not upgrade
																								for layer in self.layers[:0:-1]:
																															A__					:    Union[str, Any]														= layer.back_propagation(snake_case_       )

																	A__					:    Any														= all_loss / x_shape[0]
																	self.train_mse.append(snake_case_       )

																	self.plot_loss()

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





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


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





			def 		lowerCamelCase				( self       ):







										'''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(		):
							A__					:    Union[str, Any]														= np.random.randn(10 ,     10       )
							A__					:    int														= 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__					:    Optional[int]														= BPNN()
							for i in (10, 20, 30, 2):
														model.add_layer(DenseLayer(lowerCAmelCase       )       )
							model.build()
							model.summary()
							model.train(xdata=lowerCAmelCase ,     ydata=lowerCAmelCase ,     train_round=100 ,     accuracy=0.01       )


if __name__ == "__main__":
				example()






"""simple docstring"""



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


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

from .logging import get_logger
from .state import PartialState


_UpperCamelCase         =		get_logger(__name__)

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = None ,     ):
							# Model states
							for i, state in enumerate(lowerCAmelCase       ):
														A__					:    Tuple														= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
														A__					:    Union[str, Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														save(lowerCAmelCase ,     lowerCAmelCase       )
														logger.info(F'''Model weights saved in {output_model_file}'''       )
							# Optimizer states
							for i, opt in enumerate(lowerCAmelCase       ):
														A__					:    Tuple														= opt.state_dict()
														A__					:    Union[str, Any]														= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
														A__					:    Tuple														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														save(lowerCAmelCase ,     lowerCAmelCase       )
														logger.info(F'''Optimizer state saved in {output_optimizer_file}'''       )
							# Scheduler states
							for i, scheduler in enumerate(lowerCAmelCase       ):
														A__					:    List[str]														= scheduler.state_dict()
														A__					:    Dict														= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
														A__					:    Optional[int]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														save(lowerCAmelCase ,     lowerCAmelCase       )
														logger.info(F'''Scheduler state saved in {output_scheduler_file}'''       )
							# GradScaler state
							if scaler is not None:
														A__					:    Dict														= scaler.state_dict()
														A__					:    Optional[int]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														torch.save(lowerCAmelCase ,     lowerCAmelCase       )
														logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''       )
							# Random number generator states
							A__					:    Tuple														= {}
							A__					:    Any														= F'''{RNG_STATE_NAME}_{process_index}.pkl'''
							A__					:    Any														= random.getstate()
							A__					:    Any														= np.random.get_state()
							A__					:    int														= torch.get_rng_state()
							if is_xpu_available():
														A__					:    List[str]														= torch.xpu.get_rng_state_all()
							else:
														A__					:    Union[str, Any]														= torch.cuda.get_rng_state_all()
							if is_tpu_available():
														A__					:    str														= xm.get_rng_state()
							A__					:    str														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
							torch.save(lowerCAmelCase ,     lowerCAmelCase       )
							logger.info(F'''Random states saved in {output_states_file}'''       )
							return output_dir

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase=None ,     lowerCAmelCase=None ,     **lowerCAmelCase ,     ):
							if map_location not in [None, "cpu", "on_device"]:
														raise TypeError(
														    """Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"""       )
							if map_location is None:
														A__					:    Union[str, Any]														= """cpu"""
							elif map_location == "on_device":
														A__					:    str														= PartialState().device
							# Model states
							for i, model in enumerate(lowerCAmelCase       ):
														A__					:    List[str]														= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
														A__					:    Optional[Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														models[i].load_state_dict(torch.load(lowerCAmelCase ,     map_location=lowerCAmelCase       ) ,     **lowerCAmelCase       )
							logger.info("""All model weights loaded successfully"""       )

							# Optimizer states
							for i, opt in enumerate(lowerCAmelCase       ):
														A__					:    str														= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
														A__					:    Union[str, Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														A__					:    List[str]														= torch.load(lowerCAmelCase ,     map_location=lowerCAmelCase       )
														optimizers[i].load_state_dict(lowerCAmelCase       )
							logger.info("""All optimizer states loaded successfully"""       )

							# Scheduler states
							for i, scheduler in enumerate(lowerCAmelCase       ):
														A__					:    Any														= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
														A__					:    Optional[int]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														scheduler.load_state_dict(torch.load(lowerCAmelCase       )       )
							logger.info("""All scheduler states loaded successfully"""       )

							# GradScaler state
							if scaler is not None:
														A__					:    Optional[Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														scaler.load_state_dict(torch.load(lowerCAmelCase       )       )
														logger.info("""GradScaler state loaded successfully"""       )

							# Random states
							try:
														A__					:    Union[str, Any]														= torch.load(os.path.join(lowerCAmelCase ,     F'''{RNG_STATE_NAME}_{process_index}.pkl'''       )       )
														random.setstate(states["""random_state"""]       )
														np.random.set_state(states["""numpy_random_seed"""]       )
														torch.set_rng_state(states["""torch_manual_seed"""]       )
														if is_xpu_available():
																					torch.xpu.set_rng_state_all(states["""torch_xpu_manual_seed"""]       )
														else:
																					torch.cuda.set_rng_state_all(states["""torch_cuda_manual_seed"""]       )
														if is_tpu_available():
																					xm.set_rng_state(states["""xm_seed"""]       )
														logger.info("""All random states loaded successfully"""       )
							except Exception:
														logger.info("""Could not load random states"""       )

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 0       ):
							A__					:    int														= Path(lowerCAmelCase       ) / F'''custom_checkpoint_{index}.pkl'''
							logger.info(F'''Saving the state of {get_pretty_name(lowerCAmelCase       )} to {save_location}'''       )
							torch.save(obj.state_dict() ,     lowerCAmelCase       )



def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 0       ):
							A__					:    List[str]														= F'''{path}/custom_checkpoint_{index}.pkl'''
							logger.info(F'''Loading the state of {get_pretty_name(lowerCAmelCase       )} from {load_location}'''       )
							obj.load_state_dict(torch.load(lowerCAmelCase ,     map_location="""cpu"""       )       )





