
'''simple docstring'''



import numpy as np
from matplotlib import pyplot as plt





def 	_A						(							lowercase__		):
						return 1 / (1 + np.exp(-1 * x		))





class      A							:



			def __init__(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=None    ,       lowerCamelCase__=None    ,       lowerCamelCase__=False    )     ->						Any:




									'''simple docstring'''



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



			def A__	(   self    ,       lowerCamelCase__    )     ->						Optional[int]:




									'''simple docstring'''



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



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



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



			def A__	(   self    ,       lowerCamelCase__    )     ->						List[Any]:




									'''simple docstring'''



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



			def A__	(   self    ,       lowerCamelCase__    )     ->						Union[str, Any]:




									'''simple docstring'''



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

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

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



			def __init__(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



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



			def A__	(   self    ,       lowerCamelCase__    )     ->						Tuple:




									'''simple docstring'''



									self.layers.append(lowerCamelCase__    )



			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



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



			def A__	(   self    )     ->						Union[str, 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    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    )     ->						Dict:




									'''simple docstring'''



									lowercase__								=    train_round
									lowercase__								=    accuracy

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

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

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

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

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

															lowercase__								=    all_loss / x_shape[0]
															self.train_mse.append(lowerCamelCase__    )

															self.plot_loss()

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



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    np.sum(np.power((ydata - ydata_)    ,       2    )    )
									lowercase__								=    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						(							):
						lowercase__								=    np.random.randn(10						,						10		)
						lowercase__								=    np.asarray(
						    [
						        [0.8, 0.4],
						        [0.4, 0.3],
						        [0.3_4, 0.4_5],
						        [0.6_7, 0.3_2],
						        [0.8_8, 0.6_7],
						        [0.7_8, 0.7_7],
						        [0.5_5, 0.6_6],
						        [0.5_5, 0.4_3],
						        [0.5_4, 0.1],
						        [0.1, 0.5],
						    ]		)
						lowercase__								=    BPNN()
						for i in (10, 20, 30, 2):
												model.add_layer(DenseLayer(lowercase__		)		)
						model.build()
						model.summary()
						model.train(xdata=lowercase__						,						ydata=lowercase__						,						train_round=100						,						accuracy=0.0_1		)


if __name__ == "__main__":
				example()








'''simple docstring'''



import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


__A       =      logging.get_logger(__name__)

__A       =      {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}





class      A							(    __UpperCAmelCase			):
			lowerCamelCase							:							List[str]    						=					"""vit-hybrid"""



			def __init__(   self    ,       lowerCamelCase__=None    ,       lowerCamelCase__=768    ,       lowerCamelCase__=12    ,       lowerCamelCase__=12    ,       lowerCamelCase__=3_072    ,       lowerCamelCase__="gelu"    ,       lowerCamelCase__=0.0    ,       lowerCamelCase__=0.0    ,       lowerCamelCase__=0.02    ,       lowerCamelCase__=1e-12    ,       lowerCamelCase__=224    ,       lowerCamelCase__=1    ,       lowerCamelCase__=3    ,       lowerCamelCase__=[1, 1_024, 24, 24]    ,       lowerCamelCase__=True    ,       **lowerCamelCase__    ,       )     ->						Optional[Any]:




									'''simple docstring'''



									super().__init__(**lowerCamelCase__    )

									if backbone_config is None:
															logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""    )
															lowercase__								=    {
															    """global_padding""": """same""",
															    """layer_type""": """bottleneck""",
															    """depths""": [3, 4, 9],
															    """out_features""": ["""stage3"""],
															    """embedding_dynamic_padding""": True,
															}

									if isinstance(lowerCamelCase__    ,       lowerCamelCase__    ):
															if "model_type" in backbone_config:
																					lowercase__								=    CONFIG_MAPPING[backbone_config["""model_type"""]]
															else:
																					logger.info(
																					    """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""    )
																					lowercase__								=    BitConfig
															lowercase__								=    backbone_config_class(**lowerCamelCase__    )

									lowercase__								=    backbone_featmap_shape
									lowercase__								=    backbone_config
									lowercase__								=    hidden_size
									lowercase__								=    num_hidden_layers
									lowercase__								=    num_attention_heads
									lowercase__								=    intermediate_size
									lowercase__								=    hidden_act
									lowercase__								=    hidden_dropout_prob
									lowercase__								=    attention_probs_dropout_prob
									lowercase__								=    initializer_range
									lowercase__								=    layer_norm_eps
									lowercase__								=    image_size
									lowercase__								=    patch_size
									lowercase__								=    num_channels
									lowercase__								=    qkv_bias



			def A__	(   self    )     ->						Dict[str, any]:




									'''simple docstring'''



									lowercase__								=    copy.deepcopy(self.__dict__    )
									lowercase__								=    self.backbone_config.to_dict()
									lowercase__								=    self.__class__.model_type
									return output






