__init__.py

# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
try:
    from paddle.version import full_version as __version__
    from paddle.version import commit as __git_commit__
    from paddle.cuda_env import *
except ImportError:
    import sys
    sys.stderr.write('''Warning with import paddle: you should not
     import paddle from the source directory; please install paddlepaddle*.whl firstly.'''
                     )

from .batch import batch  # noqa: F401
from .fluid import monkey_patch_variable
from .fluid.dygraph import monkey_patch_math_varbase
monkey_patch_variable()
monkey_patch_math_varbase()
from .framework.dtype import dtype as dtype  # noqa: F401
from paddle.framework.dtype import uint8  # noqa: F401
from paddle.framework.dtype import int8  # noqa: F401
from paddle.framework.dtype import int16  # noqa: F401
from paddle.framework.dtype import int32  # noqa: F401
from paddle.framework.dtype import int64  # noqa: F401
from paddle.framework.dtype import float16  # noqa: F401
from paddle.framework.dtype import float32  # noqa: F401
from paddle.framework.dtype import float64  # noqa: F401
from paddle.framework.dtype import bfloat16  # noqa: F401
from paddle.framework.dtype import bool  # noqa: F401
from paddle.framework.dtype import complex64  # noqa: F401
from paddle.framework.dtype import complex128  # noqa: F401
from .framework import VarBase as Tensor  # noqa: F401
Tensor.__qualname__ = 'Tensor'  # noqa: F401
import paddle.compat  # noqa: F401
import paddle.distributed  # noqa: F401
import paddle.sysconfig  # noqa: F401
import paddle.distribution  # noqa: F401
import paddle.nn  # noqa: F401
import paddle.distributed.fleet  # noqa: F401
import paddle.optimizer  # noqa: F401
import paddle.metric  # noqa: F401
import paddle.regularizer  # noqa: F401
import paddle.incubate  # noqa: F401
import paddle.autograd  # noqa: F401
import paddle.device  # noqa: F401

import paddle.jit  # noqa: F401
import paddle.amp  # noqa: F401
import paddle.dataset  # noqa: F401
import paddle.inference  # noqa: F401
import paddle.io  # noqa: F401
import paddle.onnx  # noqa: F401
import paddle.reader  # noqa: F401
import paddle.static  # noqa: F401
import paddle.vision  # noqa: F401

from .tensor.random import bernoulli  # noqa: F401

from .tensor.attribute import rank  # noqa: F401
from .tensor.attribute import shape  # noqa: F401
from .tensor.attribute import real  # noqa: F401
from .tensor.attribute import imag  # noqa: F401
from .tensor.creation import to_tensor  # noqa: F401
from .tensor.creation import diag  # noqa: F401
from .tensor.creation import diagflat  # noqa: F401
from .tensor.creation import eye  # noqa: F401
from .tensor.creation import linspace  # noqa: F401
from .tensor.creation import ones  # noqa: F401
from .tensor.creation import ones_like  # noqa: F401
from .tensor.creation import zeros  # noqa: F401
from .tensor.creation import zeros_like  # noqa: F401
from .tensor.creation import arange  # noqa: F401
from .tensor.creation import full  # noqa: F401
from .tensor.creation import full_like  # noqa: F401
from .tensor.creation import triu  # noqa: F401
from .tensor.creation import tril  # noqa: F401
from .tensor.creation import meshgrid  # noqa: F401
from .tensor.creation import empty  # noqa: F401
from .tensor.creation import empty_like  # noqa: F401
from .tensor.creation import assign  # noqa: F401
from .tensor.linalg import matmul  # noqa: F401
from .tensor.linalg import dot  # noqa: F401
from .tensor.linalg import norm  # noqa: F401
from .tensor.linalg import transpose  # noqa: F401
from .tensor.linalg import dist  # noqa: F401
from .tensor.linalg import t  # noqa: F401
from .tensor.linalg import cross  # noqa: F401
from .tensor.linalg import cholesky  # noqa: F401
from .tensor.linalg import bmm  # noqa: F401
from .tensor.linalg import histogram  # noqa: F401
from .tensor.linalg import mv  # noqa: F401
from .tensor.linalg import matrix_power  # noqa: F401
from .tensor.logic import equal  # noqa: F401
from .tensor.logic import greater_equal  # noqa: F401
from .tensor.logic import greater_than  # noqa: F401
from .tensor.logic import is_empty  # noqa: F401
from .tensor.logic import less_equal  # noqa: F401
from .tensor.logic import less_than  # noqa: F401
from .tensor.logic import logical_and  # noqa: F401
from .tensor.logic import logical_not  # noqa: F401
from .tensor.logic import logical_or  # noqa: F401
from .tensor.logic import logical_xor  # noqa: F401
from .tensor.logic import bitwise_and  # noqa: F401
from .tensor.logic import bitwise_not  # noqa: F401
from .tensor.logic import bitwise_or  # noqa: F401
from .tensor.logic import bitwise_xor  # noqa: F401
from .tensor.logic import not_equal  # noqa: F401
from .tensor.logic import allclose  # noqa: F401
from .tensor.logic import equal_all  # noqa: F401
from .tensor.logic import is_tensor  # noqa: F401
from .tensor.manipulation import cast  # noqa: F401
from .tensor.manipulation import concat  # noqa: F401
from .tensor.manipulation import broadcast_tensors  # noqa: F401
from .tensor.manipulation import expand  # noqa: F401
from .tensor.manipulation import broadcast_to  # noqa: F401
from .tensor.manipulation import expand_as  # noqa: F401
from .tensor.manipulation import tile  # noqa: F401
from .tensor.manipulation import flatten  # noqa: F401
from .tensor.manipulation import gather  # noqa: F401
from .tensor.manipulation import gather_nd  # noqa: F401
from .tensor.manipulation import reshape  # noqa: F401
from .tensor.manipulation import reshape_  # noqa: F401
from .tensor.manipulation import flip as reverse  # noqa: F401
from .tensor.manipulation import scatter  # noqa: F401
from .tensor.manipulation import scatter_  # noqa: F401
from .tensor.manipulation import scatter_nd_add  # noqa: F401
from .tensor.manipulation import scatter_nd  # noqa: F401
from .tensor.manipulation import shard_index  # noqa: F401
from .tensor.manipulation import slice  # noqa: F401
from .tensor.manipulation import split  # noqa: F401
from .tensor.manipulation import squeeze  # noqa: F401
from .tensor.manipulation import squeeze_  # noqa: F401
from .tensor.manipulation import stack  # noqa: F401
from .tensor.manipulation import strided_slice  # noqa: F401
from .tensor.manipulation import unique  # noqa: F401
from .tensor.manipulation import unique_consecutive  # noqa: F401
from .tensor.manipulation import unsqueeze  # noqa: F401
from .tensor.manipulation import unsqueeze_  # noqa: F401
from .tensor.manipulation import unstack  # noqa: F401
from .tensor.manipulation import flip  # noqa: F401
from .tensor.manipulation import unbind  # noqa: F401
from .tensor.manipulation import roll  # noqa: F401
from .tensor.manipulation import chunk  # noqa: F401
from .tensor.manipulation import tolist  # noqa: F401
from .tensor.math import abs  # noqa: F401
from .tensor.math import acos  # noqa: F401
from .tensor.math import asin  # noqa: F401
from .tensor.math import atan  # noqa: F401
from .tensor.math import atan2  # noqa: F401
from .tensor.math import ceil  # noqa: F401
from .tensor.math import cos  # noqa: F401
from .tensor.math import tan  # noqa: F401
from .tensor.math import cosh  # noqa: F401
from .tensor.math import cumsum  # noqa: F401
from .tensor.math import exp  # noqa: F401
from .tensor.math import expm1  # noqa: F401
from .tensor.math import floor  # noqa: F401
from .tensor.math import increment  # noqa: F401
from .tensor.math import log  # noqa: F401
from .tensor.math import log2  # noqa: F401
from .tensor.math import log10  # noqa: F401
from .tensor.math import multiplex  # noqa: F401
from .tensor.math import pow  # noqa: F401
from .tensor.math import reciprocal  # noqa: F401
from .tensor.math import all  # noqa: F401
from .tensor.math import any  # noqa: F401
from .tensor.math import round  # noqa: F401
from .tensor.math import rsqrt  # noqa: F401
from .tensor.math import scale  # noqa: F401
from .tensor.math import sign  # noqa: F401
from .tensor.math import sin  # noqa: F401
from .tensor.math import sinh  # noqa: F401
from .tensor.math import sqrt  # noqa: F401
from .tensor.math import square  # noqa: F401
from .tensor.math import stanh  # noqa: F401
from .tensor.math import sum  # noqa: F401
from .tensor.math import tanh  # noqa: F401
from .tensor.math import tanh_  # noqa: F401
from .tensor.math import add_n  # noqa: F401
from .tensor.math import max  # noqa: F401
from .tensor.math import maximum  # noqa: F401
from .tensor.math import min  # noqa: F401
from .tensor.math import minimum  # noqa: F401
from .tensor.math import mm  # noqa: F401
from .tensor.math import divide  # noqa: F401
from .tensor.math import floor_divide  # noqa: F401
from .tensor.math import remainder  # noqa: F401
from .tensor.math import mod  # noqa: F401
from .tensor.math import floor_mod  # noqa: F401
from .tensor.math import multiply  # noqa: F401
from .tensor.math import add  # noqa: F401
from .tensor.math import subtract  # noqa: F401
from .tensor.math import logsumexp  # noqa: F401
from .tensor.math import inverse  # noqa: F401
from .tensor.math import log1p  # noqa: F401
from .tensor.math import erf  # noqa: F401
from .tensor.math import addmm  # noqa: F401
from .tensor.math import clip  # noqa: F401
from .tensor.math import trace  # noqa: F401
from .tensor.math import diagonal  # noqa: F401
from .tensor.math import kron  # noqa: F401
from .tensor.math import isfinite  # noqa: F401
from .tensor.math import isinf  # noqa: F401
from .tensor.math import isnan  # noqa: F401
from .tensor.math import prod  # noqa: F401
from .tensor.math import broadcast_shape  # noqa: F401
from .tensor.math import conj  # noqa: F401
from .tensor.math import trunc  # noqa: F401
from .tensor.math import digamma  # noqa: F401
from .tensor.math import neg  # noqa: F401
from .tensor.math import lgamma  # noqa: F401

from .tensor.random import multinomial  # noqa: F401
from .tensor.random import standard_normal  # noqa: F401
from .tensor.random import normal  # noqa: F401
from .tensor.random import uniform  # noqa: F401
from .tensor.random import randn  # noqa: F401
from .tensor.random import rand  # noqa: F401
from .tensor.random import randint  # noqa: F401
from .tensor.random import randperm  # noqa: F401
from .tensor.search import argmax  # noqa: F401
from .tensor.search import argmin  # noqa: F401
from .tensor.search import argsort  # noqa: F401
from .tensor.search import masked_select  # noqa: F401
from .tensor.search import topk  # noqa: F401
from .tensor.search import where  # noqa: F401
from .tensor.search import index_select  # noqa: F401
from .tensor.search import nonzero  # noqa: F401
from .tensor.search import sort  # noqa: F401

from .tensor.to_string import set_printoptions  # noqa: F401

from .tensor.einsum import einsum  # noqa: F401

from .framework.random import seed  # noqa: F401
from .framework.random import get_cuda_rng_state  # noqa: F401
from .framework.random import set_cuda_rng_state  # noqa: F401
from .framework import ParamAttr  # noqa: F401
from .framework import create_parameter  # noqa: F401
from .framework import CPUPlace  # noqa: F401
from .framework import CUDAPlace  # noqa: F401
from .framework import NPUPlace  # noqa: F401
from .framework import CUDAPinnedPlace  # noqa: F401

from .framework import grad  # noqa: F401
from .framework import no_grad  # noqa: F401
from .framework import set_grad_enabled  # noqa: F401
from .framework import save  # noqa: F401
from .framework import load  # noqa: F401
from .framework import DataParallel  # noqa: F401

from .framework import set_default_dtype  # noqa: F401
from .framework import get_default_dtype  # noqa: F401

from .tensor.search import index_sample  # noqa: F401
from .tensor.stat import mean  # noqa: F401
from .tensor.stat import std  # noqa: F401
from .tensor.stat import var  # noqa: F401
from .tensor.stat import numel  # noqa: F401
from .tensor.stat import median  # noqa: F401
from .device import get_cudnn_version  # noqa: F401
from .device import set_device  # noqa: F401
from .device import get_device  # noqa: F401
from .fluid.framework import is_compiled_with_cuda  # noqa: F401
from .fluid.framework import is_compiled_with_rocm  # noqa: F401
from .fluid.framework import disable_signal_handler  # noqa: F401
from .device import is_compiled_with_xpu  # noqa: F401
from .device import is_compiled_with_npu  # noqa: F401
from .device import XPUPlace  # noqa: F401

from .fluid.dygraph.base import enable_dygraph as disable_static  # noqa: F401
from .fluid.dygraph.base import disable_dygraph as enable_static  # noqa: F401
from .fluid.framework import in_dygraph_mode as in_dynamic_mode  # noqa: F401
from .fluid.layers import crop_tensor as crop  # noqa: F401

# high-level api
from .hapi import Model  # noqa: F401
from . import callbacks  # noqa: F401
from .hapi import summary  # noqa: F401
from .hapi import flops  # noqa: F401
from . import hub  # noqa: F401
from . import linalg  # noqa: F401

import paddle.text  # noqa: F401
import paddle.vision  # noqa: F401

from .tensor.random import check_shape  # noqa: F401
disable_static()

__all__ = [  # noqa
           'dtype',
           'uint8',
           'int8',
           'int16',
           'int32',
           'int64',
           'float16',
           'float32',
           'float64',
           'bfloat16',
           'bool',
           'complex64',
           'complex128',
           'addmm',
           'allclose',
           't',
           'add',
           'subtract',
           'diag',
           'diagflat',
           'isnan',
           'scatter_nd_add',
           'unstack',
           'get_default_dtype',
           'save',
           'multinomial',
           'get_cuda_rng_state',
           'rank',
           'empty_like',
           'eye',
           'cumsum',
           'sign',
           'is_empty',
           'equal',
           'equal_all',
           'is_tensor',
           'cross',
           'where',
           'log1p',
           'cos',
           'tan',
           'mean',
           'mv',
           'in_dynamic_mode',
           'min',
           'any',
           'slice',
           'normal',
           'logsumexp',
           'full',
           'unsqueeze',
           'unsqueeze_',
           'argmax',
           'Model',
           'summary',
           'flops',
           'sort',
           'split',
           'logical_and',
           'full_like',
           'less_than',
           'kron',
           'clip',
           'Tensor',
           'crop',
           'ParamAttr',
           'stanh',
           'randint',
           'assign',
           'gather',
           'scale',
           'zeros',
           'rsqrt',
           'squeeze',
           'squeeze_',
           'to_tensor',
           'gather_nd',
           'isinf',
           'uniform',
           'floor_divide',
           'remainder',
           'floor_mod',
           'roll',
           'batch',
           'max',
           'norm',
           'logical_or',
           'bitwise_and',
           'bitwise_or',
           'bitwise_xor',
           'bitwise_not',
           'mm',
           'flip',
           'histogram',
           'multiplex',
           'CUDAPlace',
           'NPUPlace',
           'empty',
           'shape',
           'real',
           'imag',
           'reciprocal',
           'rand',
           'less_equal',
           'triu',
           'sin',
           'dist',
           'unbind',
           'meshgrid',
           'arange',
           'load',
           'numel',
           'median',
           'inverse',
           'no_grad',
           'set_grad_enabled',
           'mod',
           'abs',
           'tril',
           'pow',
           'zeros_like',
           'maximum',
           'topk',
           'index_select',
           'CPUPlace',
           'matmul',
           'seed',
           'acos',
           'logical_xor',
           'exp',
           'expm1',
           'bernoulli',
           'sinh',
           'round',
           'DataParallel',
           'argmin',
           'prod',
           'broadcast_shape',
           'conj',
           'neg',
           'lgamma',
           'square',
           'divide',
           'ceil',
           'atan',
           'atan2',
           'expand',
           'broadcast_to',
           'ones_like',
           'index_sample',
           'cast',
           'grad',
           'all',
           'ones',
           'not_equal',
           'sum',
           'tile',
           'greater_equal',
           'isfinite',
           'create_parameter',
           'dot',
           'increment',
           'erf',
           'bmm',
           'chunk',
           'tolist',
           'greater_than',
           'shard_index',
           'argsort',
           'tanh',
           'tanh_',
           'transpose',
           'randn',
           'strided_slice',
           'unique',
           'unique_consecutive',
           'set_cuda_rng_state',
           'set_printoptions',
           'std',
           'flatten',
           'asin',
           'multiply',
           'disable_static',
           'masked_select',
           'var',
           'trace',
           'enable_static',
           'scatter_nd',
           'set_default_dtype',
           'disable_signal_handler',
           'expand_as',
           'stack',
           'sqrt',
           'cholesky',
           'matrix_power',
           'randperm',
           'linspace',
           'reshape',
           'reshape_',
           'reverse',
           'nonzero',
           'CUDAPinnedPlace',
           'logical_not',
           'add_n',
           'minimum',
           'scatter',
           'scatter_',
           'floor',
           'cosh',
           'log',
           'log2',
           'log10',
           'concat',
           'check_shape',
           'trunc',
           'digamma',
           'standard_normal',
           'diagonal',
           'broadcast_tensors',
           'einsum'
]