o
    Ne0\                     @   sv  d dl mZ d dlZddlmZmZmZmZ ddlm	Z	 ddl
mZmZmZ ddlmZmZmZmZ d d	lmZ d d
lmZmZ d dlZdddZg dZg dZg dZg dZeeD ]	Zeee e< q]ede d< ede d< ee7 Zee7 Zee7 ZeeD ]ZeZ eev ree Z eeZ!edde  de!Z!e!e e< qeeD ]ZeZ eev ree Z eeZ!edde  de!Z!e!e e< qeeD ]ZeZ eev ree Z eeZ!edde  de!Z!e!e e< qee d d ee d d ee d d ee d  d! ee d" d# ee d$ d% ee d& d' ee d( d) ee d* d+ ee d, d- ee d. d/ ee d0 d1 ee d2 d3 ee d4 d5 ee d6 d7 ee d8 d9 ee d: d; ee d< d= ee d> d? ee d@ dA ee dB dC ee dD dE ee dF dG ee dH dI ee dJ dK ee dL dM edNZ"dqdQe#dRe#fdSdNZ$dTe$_%ee dU dV edWZ&drdXdWZ'dYe'_%edZZ(edd[ddrd\dZZ)e(j%d] e)_%ed^Z*edd_d`dadsdbd^Z+dce+_%eddZ,drdeddZ-dfe-_%edgZ.eddhddtdjdgZ/dke/_%edlZ0drdmdlZ1dne1_%drdodpZ2dS )u    )print_functionN   )generate_layer_fngenerate_activation_fngenerate_inplace_fnadd_sample_code   )core)convert_np_dtype_to_dtype_Variablein_dygraph_mode)convert_dtypecheck_variable_and_dtype
check_typecheck_dtype)
deprecated)_C_ops_legacy_C_opsZ
tanhshrinkZlog_sigmoid)tanh_shrink
logsigmoid)sigmoidsilur   r   softsigntanh)expexpm1atansqrtrsqrtabsceilfloorcostanacossinsinhasincoshround
reciprocalsquareacoshasinhatanhlgamma)Zexp_Zsqrt_Zrsqrt_Zceil_Zfloor_Zround_Zreciprocal_)softplus
softshrinkhard_shrinkcumsumthresholded_relugeluerfscaleZ_scaleZelementwise_divZ_elementwise_divz2.0.0zpaddle.nn.functional.%s)since	update_toz	paddle.%sr   a  
Examples:
    .. code-block:: python

        import paddle
        import paddle.nn.functional as F

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = F.sigmoid(x)
        print(out)
        # [0.40131234 0.450166   0.52497919 0.57444252]

r   z
Examples:
    .. code-block:: python

        import paddle
        import paddle.nn.functional as F

        x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0])
        out = F.silu(x)
        print(out)
        # [ 0.7310586 1.7615942 2.8577224, 3.9280552 ]

r   a  
Examples:
    .. code-block:: python

        import paddle
        import paddle.nn.functional as F

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = F.log_sigmoid(x)
        print(out)
        # [-0.91301525 -0.79813887 -0.64439666 -0.55435524]

r   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.exp(x)
        print(out)
        # [0.67032005 0.81873075 1.10517092 1.34985881]

r   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.expm1(x)
        print(out)
        # [-0.32967997, -0.18126924,  0.10517092,  0.34985882]

r   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.tanh(x)
        print(out)
        # [-0.37994896 -0.19737532  0.09966799  0.29131261]

r   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.atan(x)
        print(out)
        # [-0.38050638 -0.19739556  0.09966865  0.29145679]

r   a  
Examples:
    .. code-block:: python

        import paddle
        import paddle.nn.functional as F

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = F.tanhshrink(x) 
        print(out)
        # [-0.020051, -0.00262468, 0.000332005, 0.00868739]

r   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([0.1, 0.2, 0.3, 0.4])
        out = paddle.sqrt(x)
        print(out)
        # [0.31622777 0.4472136  0.54772256 0.63245553]

r   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([0.1, 0.2, 0.3, 0.4])
        out = paddle.rsqrt(x)
        print(out)
        # [3.16227766 2.23606798 1.82574186 1.58113883]

r   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.abs(x)
        print(out)
        # [0.4 0.2 0.1 0.3]

r    z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.ceil(x)
        print(out)
        # [-0. -0.  1.  1.]

r!   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.floor(x)
        print(out)
        # [-1. -1.  0.  0.]

r"   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.cos(x)
        print(out)
        # [0.92106099 0.98006658 0.99500417 0.95533649]

r#   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.tan(x)
        print(out)
        # [-0.42279324, -0.20271005, 0.10033467, 0.30933627]

r$   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.acos(x)
        print(out)
        # [1.98231317 1.77215425 1.47062891 1.26610367]

r%   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.sin(x)
        print(out)
        # [-0.38941834 -0.19866933  0.09983342  0.29552021]

r'   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.asin(x)
        print(out)
        # [-0.41151685 -0.20135792  0.10016742  0.30469265]

r(   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.cosh(x)
        print(out)
        # [1.08107237 1.02006676 1.00500417 1.04533851]

r&   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.sinh(x)
        print(out)
        # [-0.41075233 -0.201336    0.10016675  0.30452029]

r-   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.asinh(x)
        print(out)
        # [-0.39003533, -0.19869010,  0.09983408,  0.29567307]

r,   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([1., 3., 4., 5.])
        out = paddle.acosh(x)
        print(out)
        # [0.        , 1.76274729, 2.06343699, 2.29243159]

r.   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.atanh(x)
        print(out)
        # [-0.42364895, -0.20273256,  0.10033535,  0.30951962]

r)   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.5, -0.2, 0.6, 1.5])
        out = paddle.round(x)
        print(out)
        # [-1. -0.  1.  2.]

r*   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.reciprocal(x)
        print(out)
        # [-2.5        -5.         10.          3.33333333]

r+   z
Examples:
    .. code-block:: python

        import paddle

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.square(x)
        print(out)
        # [0.16 0.04 0.01 0.09]

r0         ?      4@beta	thresholdc                 C   sR   t | dddgd t  }t }| D ]\}}|d ur!|||< qtdi |S )Nxfloat32float64r0    )r   localscopydictitems
_softplus_)r>   r<   r=   nameZ
locals_valkwargsvalrA   rA   GD:\Projects\ConvertPro\env\Lib\site-packages\paddle/fluid/layers/ops.pyr0     s   
a  
    :alias_main: paddle.nn.functional.softplus
    :alias: paddle.nn.functional.softplus, paddle.nn.functional.activation.softplus
    :old_api: paddle.fluid.layers.softplus

:strong:`Softplus Activation Operator`

Equation:
    .. math::
        out = \\frac{1}{beta} * log(1 + e^{beta * x})
        For numerical stability, the implementation reverts to the linear function when: beta * x > threshold.

Args:
    x(Tensor): Input of Softplus op, Tensor, dtype: float32 or float64
    beta(float, optional): The value of beta for softplus. Default is 1
    threshold (float, optional): The value of threshold for softplus. Default is 20
    name(str, optional): Name for the operation (optional, default is None)

Returns:
    Variable: The output of Softplus op, Tensor, dtype: float32 or float64

Examples:
    .. code-block:: python

        import paddle
        import paddle.nn.functional as F

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = F.softplus(x) 
        print(out)
        # [0.513015, 0.598139, 0.744397, 0.854355]
r   a  
Examples:
    .. code-block:: python

        import paddle
        import paddle.nn.functional as F

        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = F.softsign(x) 
        print(out)
        # [-0.285714, -0.166667, 0.0909091, 0.230769]

r1   c                 C   sd   t | dg dd t  }t }| D ]\}}|d ur*|dkr&||d< q|||< qtdi |S )Nr>   Zfloat16r?   r@   r1   alphalambdarA   )r   rB   rC   rD   rE   _softshrink_)r>   rL   
locals_varrH   rG   rI   rA   rA   rJ   r1     s   

aE  
	:alias_main: paddle.nn.functional.softshrink
	:alias: paddle.nn.functional.softshrink,paddle.nn.functional.activation.softshrink
	:old_api: paddle.fluid.layers.softshrink

:strong:`Softshrink Activation Operator`

..  math::
    out = \\begin{cases}
            x - \\alpha, \\text{if } x > \\alpha \\\\
            x + \\alpha, \\text{if } x < -\\alpha \\\\
            0,  \\text{otherwise}
          \\end{cases}


Args:
    x: Input of Softshrink operator, an N-D Tensor, with data type float32, float64 or float16.
    alpha (float): non-negative offset
    
Returns:
    Output of Softshrink operator with the same type of input.

Examples:
    .. code-block:: python
    
        import paddle.fluid as fluid
        data = fluid.data(name="input", shape=[None, 784])
        result = fluid.layers.softshrink(x=data, alpha=0.3)
r2   zpaddle.nn.functional.hardshrinkc                 C   R   t | dg dd t  }t }| D ]\}}|d ur!|||< qtdi |S )Nr>   rK   r2   rA   )r   rB   rC   rD   rE   _hard_shrink_r>   r=   rO   rH   rG   rI   rA   rA   rJ   r2   H  s   
z
Examples:

    >>> import paddle.fluid as fluid
    >>> data = fluid.layers.data(name="input", shape=[784])
    >>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
r3   zpaddle.cumsumz#New APIs for Paddle 2.0 are coming.)r8   r9   reasonc                 C   sN   t | dtd t  }t }| D ]\}}|d ur|||< qtdi |S )Nr>   r3   rA   )r   r   rB   rC   rD   rE   	_cum_sum_)r>   ZaxisZ	exclusivereverserO   rH   rG   rI   rA   rA   rJ   r3   `  s   
a  
	:alias_main: paddle.cumsum
	:alias: paddle.cumsum,paddle.tensor.cumsum,paddle.tensor.math.cumsum
	:old_api: paddle.fluid.layers.cumsum

The cumulative sum of the elements along a given axis. By default, the first element of the result is the same of the first element of the input. If exlusive is true, the first element of the result is 0.

Args:
    x (Variable): Input of cumsum operator, the Tensor/LoDTensor needed to be cumsumed. 
    axis (int, optional): The dimension to accumulate along. -1 means the last dimension. Default is -1.
    exclusive (bool, optional): Whether to perform exclusive cumsum. Default is False.
    reverse (bool, optional): If true, the cumsum is performed in the reversed direction. Default is False.

Returns:
    Variable(Tensor/LoDTensor): The result of cumsum operator, output of cumsum operator. 

Examples:
    .. code-block:: python
        
        import paddle.fluid as fluid
        data = fluid.layers.data(name="input", shape=[32, 784])
        result = fluid.layers.cumsum(data, axis=0)
r4   c                 C   rP   )Nr>   rK   r4   rA   )r   rB   rC   rD   rE   _thresholded_relu_rR   rA   rA   rJ   r4     s   
al	  
	:alias_main: paddle.nn.functional.thresholded_relu
	:alias: paddle.nn.functional.thresholded_relu,paddle.nn.functional.activation.thresholded_relu
	:old_api: paddle.fluid.layers.thresholded_relu

:strong:`Thresholded ReLU Activation Operator`

Equation:
    ..  math::
        out = \\begin{cases}
            x, &if x > threshold \\\\
            0, &otherwise
            \\end{cases}

Args:
    x(Variable): The input of Thresholded ReLU op, Tensor or LoDTensor, dtype: float32 or float64.
        
    threshold(float, optional): The threshold value. Note that if the arg `threshold` is not set, the threshold in the equation is 1.0.

Returns:

    Variable: The output of Thresholded ReLU op, Tensor or LoDTensor, dtype: float32 or float64, the same as the input, shape: the same as the input.

Examples:
    
    .. code-block:: python
    
        # declarative mode
        import numpy as np
        from paddle import fluid
        
        x = fluid.data(name="x", shape=(-1, 3), dtype="float32")
        y = fluid.layers.thresholded_relu(x, threshold=0.1)
        
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        start = fluid.default_startup_program()
        main = fluid.default_main_program()
        
        data = np.random.randn(2, 3).astype("float32")
        exe.run(start)
        
        y_np, = exe.run(main, feed={"x": data}, fetch_list=[y])
        
        data
        # array([[ 0.21134382, -1.1805999 ,  0.32876605],
        #        [-1.2210793 , -0.7365624 ,  1.0013918 ]], dtype=float32)
        y_np
        # array([[ 0.21134382, -0.        ,  0.32876605],
        #        [-0.        , -0.        ,  1.0013918 ]], dtype=float32)

    .. code-block:: python
    
        # imperative mode
        import numpy as np
        from paddle import fluid
        import paddle.fluid.dygraph as dg
        
        data = np.random.randn(2, 3).astype("float32")
        place = fluid.CPUPlace()
        with dg.guard(place) as g:
            x = dg.to_variable(data)
            y = fluid.layers.thresholded_relu(x, threshold=0.1)
            y_np = y.numpy()
        data
        # array([[ 0.21134382, -1.1805999 ,  0.32876605],
        #        [-1.2210793 , -0.7365624 ,  1.0013918 ]], dtype=float32)
        y_np
        # array([[ 0.21134382, -0.        ,  0.32876605],
        #        [-0.        , -0.        ,  1.0013918 ]], dtype=float32)
r5   zpaddle.nn.functional.geluFc                 C   s@   t   }t }| D ]\}}|d ur|||< qtdi |S NrA   )rB   rC   rD   rE   _gelu_)r>   ZapproximaterO   rH   rG   rI   rA   rA   rJ   r5     s   
a]  
:strong:`GeLU Activation Operator`
For more details, see [Gaussian Error Linear Units](https://arxiv.org/abs/1606.08415).

Equation:
    if approximate is True
    ..  math::
        out = 0.5 * x * (1 + tanh(\\sqrt{\\frac{2}{\\pi}} * (x + 0.044715x^{3})))

    else
    ..  math::
        out = 0.5 * x * (1 + erf(\\frac{x}{\\sqrt{2}}))

Args:

    x(Variable): The input of GeLU op, Tensor or LoDTensor, dtype: float32 or float64.

Returns:

    Variable: The output of GeLU op, Tensor or LoDTensor, dtype: float32 or float64, the same as the input, shape: the same as the input.

Examples:
    
    .. code-block:: python
    
        # declarative mode
        import numpy as np
        from paddle import fluid
        
        x = fluid.data(name="x", shape=(-1, 3), dtype="float32")
        y = fluid.layers.gelu(x)
        
        place = fluid.CPUPlace()
        exe = fluid.Executor(place)
        start = fluid.default_startup_program()
        main = fluid.default_main_program()
        
        data = np.random.randn(2, 3).astype("float32")
        exe.run(start)
        
        y_np, = exe.run(main, feed={"x": data}, fetch_list=[y])
        
        data
        # array([[ 0.87165993, -1.0541513 , -0.37214822],
        #         [ 0.15647964,  0.32496083,  0.33045998]], dtype=float32)
        y_np
        # array([[ 0.70456535, -0.15380788, -0.13207214],
        #        [ 0.08796856,  0.20387867,  0.2080159 ]], dtype=float32)

    .. code-block:: python
    
        # imperative mode
        import numpy as np
        from paddle import fluid
        import paddle.fluid.dygraph as dg
        
        data = np.random.randn(2, 3).astype("float32")
        place = fluid.CPUPlace()
        with dg.guard(place) as g:
            x = dg.to_variable(data)
            y = fluid.layers.gelu(x)
            y_np = y.numpy()
        data
        # array([[ 0.87165993, -1.0541513 , -0.37214822],
        #        [ 0.15647964,  0.32496083,  0.33045998]], dtype=float32)
        y_np
        # array([[ 0.70456535, -0.15380788, -0.13207214],
        #        [ 0.08796856,  0.20387867,  0.2080159 ]], dtype=float32)
r6   c                 C   sP   t  rt| S t  }t }| D ]\}}|d ur |||< qtdi |S rW   )r   r   r6   rB   rC   rD   rE   _erf_)r>   rG   rO   rH   rI   rA   rA   rJ   r6   3  s   
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:strong:`Erf Operator`
For more details, see [Error function](https://en.wikipedia.org/wiki/Error_function).

Equation:
    ..  math::
        out = \\frac{2}{\\sqrt{\\pi}} \\int_{0}^{x}e^{- \\eta^{2}}d\\eta

Args:

    x (Tensor): The input tensor, it's data type should be float32, float64.

Returns:

    Tensor: The output of Erf op, dtype: float32 or float64, the same as the input, shape: the same as the input.

Examples:
    
    .. code-block:: python
    
        import paddle
        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
        out = paddle.erf(x)
        print(out)
        # [-0.42839236 -0.22270259  0.11246292  0.32862676]
c                 C   s   t j| S )a  
    Calculates the lgamma of the given input tensor, element-wise.

    This operator performs elementwise lgamma for input $X$.
    :math:`out = log\Gamma(x)`


    Args:
        x (Tensor): Input Tensor. Must be one of the following types: float32, float64.
        name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        Tensor, the lgamma of the input Tensor, the shape and data type is the same with input.

    Examples:
        .. code-block:: python

            import paddle

            x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
            out = paddle.lgamma(x)
            print(out)
            # [1.31452441, 1.76149750, 2.25271273, 1.09579802]
    )paddleZTensorr/   )r>   rG   rA   rA   rJ   r/   [  s   r/   )r:   r;   N)N)NNN)F)3
__future__r   osZlayer_function_generatorr   r   r   r    r	   Z	frameworkr
   r   r   Zdata_feederr   r   r   r   Zpaddle.utilsr   rZ   r   r   Z__deprecated_func_name__Z__activations_noattr__Z__unary_func__Z__inplace_unary_func____all__setZ_OPglobalsZ_new_OPZ_funcrF   floatr0   __doc__rN   r1   rQ   r2   rT   r3   rV   r4   rX   r5   rY   r6   r/   rA   rA   rA   rJ   <module>   s:  	

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