#!/usr/bin/env python
"""
This file contains Python interface of norm_1d_batch.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

#-------------------------------------------------------------------------------
__author__     = "<NAME>, <NAME>"
__copyright__  = "Copyright 2020, Future Design Systems"
__credits__    = ["none", "some"]
__license__    = "FUTURE DESIGN SYSTEMS SOFTWARE END-USER LICENSE AGREEMENT"
__version__    = "0"
__revision__   = "1"
__maintainer__ = "<NAME>"
__email__      = "<EMAIL>"
__status__     = "Development"
__date__       = "2020.09.30"
__description__= "Python interface of norm_1d_batch"

#-------------------------------------------------------------------------------
import ctypes
import ctypes.util
import numpy as np
import python.modules.dlr_common as dlr_common

#===============================================================================
def Norm1dBatch( out_data     # in_channel x in_size (contiguous)
               , in_data      # in_channel x in_size (contiguous)
               , running_mean # in_channel
               , running_var  # in_channel
               , scale=None   # None or in_channel (default 1)
               , bias=None    # None or in_channel (default 0)
               , epsilon=1E-5
               , rigor=False
               , verbose=False):
    """
    Returns True on success, otherwize returns False
    Applies a 1D matrix multiplication over an input data data.
    Note that all nd-array lists are NumPy (mutable), not PyTorch tensor (immutable).
    :param out_data: <mutable> output data, out_data[channel][size][size]
    :param in_data: input data, in_data[channel][size][size]
    :param running_mean:
    :param running_var:
    :param scale:
    :param bias:
    :param epsilon:
    :param rigor: check values rigorously when 'True'
    :param verbose: output message more when 'True'
    :return: 'True' on success, 'False' on failure.
    Following is an example usage for PyTorch.
        Norm1dBatch( tensor_out_data.data.numpy() # ndim x out_size
                       , tenso_in_data.data.numpy()   # ndim x in_size
                       , tensor_running_mean.data.numpy()   # out_size x in_size
                       , tensor_running_var.data.numpy()     # out_size
                       , tensor_scale.data.numpy()     # out_size
                       , tensor_bias.data.numpy()     # out_size
                       , epsilon
                       , rigor=True
                       , verbose=True)
    """
    if rigor:
       error =0
       if (out_data.ndim!=in_data.ndim):
           error += 1
           if verbose: dlr_common.DpuError("out_data in_data dimension mis-match", flush=True)
       if (running_mean.ndim!=1):
           error += 1
           if verbose: dlr_common.DpuError(f"running_mean dimension mis-match {running_mean.ndim}", flush=True)
       if (running_mean.size!=in_data.shape[0]): # num of channels
           error += 1
           if verbose: dlr_common.DpuError(f"running_mean size mis-match {running_mean.size} {in_data.shape}", flush=True)
       if (running_var.ndim!=1):
           error += 1
           if verbose: dlr_common.DpuError("running_var dimension mis-match", flush=True)
       if (running_var.size!=in_data.shape[0]): # num of channels
           error += 1
           if verbose: dlr_common.DpuError("running_var size mis-match", flush=True)
       if (scale is not None) and (scale.ndim!=1):
           error += 1
           if verbose: dlr_common.DpuError(f"scale should be 1 dim: {scale.ndim}", flush=True)
       if (bias is not None) and (bias.ndim!=1):
           error += 1
           if verbose: dlr_common.DpuError(f"bias should be 1 dim: {bias.ndim}", flush=True)
       t_out_channel     = out_data.shape[0]
       t_out_size        = out_data.shape[1]
       t_in_channel      = in_data.shape[0]
       t_in_size         = in_data.shape[1]
       if (t_out_channel!=t_in_channel):
           error += 1
           dlr_common.DpuError(f"channel mis-match", flush=True)
       if (t_out_size!=t_in_size):
           error += 1
           dlr_common.DpuError(f"channel mis-match", flush=True)
       if (error!=0):
           dlr_common.DpuError("parameter mis-match", flush=True)
           return False
    #_fname=''
    #_ctype=''
    if out_data.dtype.type == np.int32:
        _fname = 'Norm1dBatchInt'
        _ctype = ctypes.c_int
    elif out_data.dtype.type == np.float32:
        _fname = 'Norm1dBatchFloat'
        _ctype = ctypes.c_float
    elif out_data.dtype.type == np.float64:
        _fname = 'Norm1dBatchDouble'
        _ctype = ctypes.c_double
    else:
        dlr_common.DpuError(" not support "+str(out_data.dtype.type), flush=True)
        return False
    _Norm1dBatch=dlr_common.WrapFunction(dlr_common._dlr
                                        ,_fname
                                        , None          # return type
                                        ,[ctypes.POINTER(_ctype) # out data
                                         ,ctypes.POINTER(_ctype) # in data
                                         ,ctypes.POINTER(_ctype) # running_mean
                                         ,ctypes.POINTER(_ctype) # running_var
                                         ,ctypes.POINTER(_ctype) # scale
                                         ,ctypes.POINTER(_ctype) # bias
                                         ,ctypes.c_uint    # in_size
                                         ,ctypes.c_ushort  # scale_size
                                         ,ctypes.c_ushort  # bias_size
                                         ,ctypes.c_ushort  # in_channel
                                         ,ctypes.c_float   # epsilon
                                         ,ctypes.c_int     # rigor
                                         ,ctypes.c_int ])  # verbose
    in_channel     = in_data.shape[0]
    in_size        = int(in_data.size/in_channel) # num of elements per channel
    CP_out_data    = out_data.ctypes.data_as(ctypes.POINTER(_ctype))
    CP_in_data     = in_data.ctypes.data_as(ctypes.POINTER(_ctype))
    CP_running_mean= running_mean.ctypes.data_as(ctypes.POINTER(_ctype))
    CP_running_var = running_var.ctypes.data_as(ctypes.POINTER(_ctype))
    CP_in_size     = ctypes.c_uint(in_size)
    CP_in_channel  = ctypes.c_ushort(in_channel)
    CP_epsilon     = ctypes.c_float(epsilon)
    CP_rigor       = 1 if rigor else 0
    CP_verbose     = 1 if verbose else 0
    if (scale is None) or (scale.size == 0):
       CP_scale       = ctypes.POINTER(_ctype)()
       CP_scale_size  = ctypes.c_ushort(0)
    else:
       CP_scale       = scale.ctypes.data_as(ctypes.POINTER(_ctype))
       CP_scale_size  = ctypes.c_ushort(scale.shape[1])
    if (bias is None) or (bias.size == 0):
       CP_bias        = ctypes.POINTER(_ctype)()
       CP_bias_size   = ctypes.c_ushort(0)
    else:
       CP_bias        = bias.ctypes.data_as(ctypes.POINTER(_ctype))
       CP_bias_size   = ctypes.c_ushort(bias.shape[1])
    _Norm1dBatch(CP_out_data
                ,CP_in_data
                ,CP_running_mean
                ,CP_running_var
                ,CP_scale
                ,CP_bias
                ,CP_in_size
                ,CP_scale_size
                ,CP_bias_size
                ,CP_in_channel
                ,CP_epsilon
                ,CP_rigor
                ,CP_verbose)
    return True

#===============================================================================
if __name__=='__main__':
    def TestNorm1dBatch(_dtype):
        """
        _dtype: specify data type of data one of {np.int32, np.float32, np.float64}
        """
        in_channel = 2
        in_size    = 5
        in_dim     = [in_channel, in_size, in_size]
        in_data    = (100+100)*np.random.random(size=in_dim)-100
        out_data = np.empty(in_dim, dtype=_dtype)
        running_mean = (100+100)*np.random.random(size=(in_channel))-100
        running_var  = (100+100)*np.random.random(size=(in_channel))-100
        scale        = (10+10)*np.random.random(size=(in_channel))-10
        bias         = (10+10)*np.random.random(size=(in_channel))-10
        if _dtype is np.int32:
            in_data      = np.int32(in_data)
            running_mean = np.int32(running_mean)
            running_var  = np.int32(running_var)
            scale        = np.int32(scale)
            bias         = np.int32(bias)
        epsilon = 1e-5
        status = Norm1dBatch( out_data
                            , in_data
                            , running_mean=running_mean
                            , running_var =running_var
                            , scale       =scale
                            , bias        =bias
                            , epsilon     =epsilon
                            , rigor=True
                            , verbose=True
                            )
        if status:
            dlr_common.DpuPrint(f"out_data:\n{out_data}", flush=True)
            dlr_common.DpuPrint(f"in_data:\n{in_data}", flush=True)
            dlr_common.DpuPrint(f"running_mean:\n{running_mean}", flush=True)
            dlr_common.DpuPrint(f"running_var:\n{running_var}", flush=True)
            dlr_common.DpuPrint(f"scale:\n{scale}", flush=True)
            dlr_common.DpuPrint(f"bias:\n{bias}", flush=True)
            dlr_common.DpuPrint(f"epsilon:\n{epsilon}", flush=True)

#===============================================================================
if __name__=='__main__':
    dlr_common.DpuPrint("Testing Norm1dBatch", flush=True)
    dlr_common.DpuPrint("*********************", flush=True)
    #TestNorm1dBatch(_dtype=np.int32)
    TestNorm1dBatch(_dtype=np.float32)
    #TestNorm1dBatch(_dtype=np.float64)

#===============================================================================
# Revision history:
#
# 2020.09.30: argument order of bias and bias_size changed
# 2020.04.25: Started by <NAME> (<EMAIL>)
#===============================================================================

#!/usr/bin/env python
"""
This file contains Python interface of norm_1d_batch.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

#-------------------------------------------------------------------------------
__author__     = "<NAME>, <NAME>"
__copyright__  = "Copyright 2020, Future Design Systems"
__credits__    = ["none", "some"]
__license__    = "FUTURE DESIGN SYSTEMS SOFTWARE END-USER LICENSE AGREEMENT"
__version__    = "0"
__revision__   = "1"
__maintainer__ = "<NAME>"
__email__      = "<EMAIL>"
__status__     = "Development"
__date__       = "2020.09.30"
__description__= "Python interface of norm_1d_batch"

#-------------------------------------------------------------------------------
import ctypes
import ctypes.util
import numpy as np
import python.modules.dlr_common as dlr_common

#===============================================================================
def Norm1dBatch( out_data     # in_channel x in_size (contiguous)
               , in_data      # in_channel x in_size (contiguous)
               , running_mean # in_channel
               , running_var  # in_channel
               , scale=None   # None or in_channel (default 1)
               , bias=None    # None or in_channel (default 0)
               , epsilon=1E-5
               , rigor=False
               , verbose=False):
    """
    Returns True on success, otherwize returns False
    Applies a 1D matrix multiplication over an input data data.
    Note that all nd-array lists are NumPy (mutable), not PyTorch tensor (immutable).
    :param out_data: <mutable> output data, out_data[channel][size][size]
    :param in_data: input data, in_data[channel][size][size]
    :param running_mean:
    :param running_var:
    :param scale:
    :param bias:
    :param epsilon:
    :param rigor: check values rigorously when 'True'
    :param verbose: output message more when 'True'
    :return: 'True' on success, 'False' on failure.
    Following is an example usage for PyTorch.
        Norm1dBatch( tensor_out_data.data.numpy() # ndim x out_size
                       , tenso_in_data.data.numpy()   # ndim x in_size
                       , tensor_running_mean.data.numpy()   # out_size x in_size
                       , tensor_running_var.data.numpy()     # out_size
                       , tensor_scale.data.numpy()     # out_size
                       , tensor_bias.data.numpy()     # out_size
                       , epsilon
                       , rigor=True
                       , verbose=True)
    """
    if rigor:
       error =0
       if (out_data.ndim!=in_data.ndim):
           error += 1
           if verbose: dlr_common.DpuError("out_data in_data dimension mis-match", flush=True)
       if (running_mean.ndim!=1):
           error += 1
           if verbose: dlr_common.DpuError(f"running_mean dimension mis-match {running_mean.ndim}", flush=True)
       if (running_mean.size!=in_data.shape[0]): # num of channels
           error += 1
           if verbose: dlr_common.DpuError(f"running_mean size mis-match {running_mean.size} {in_data.shape}", flush=True)
       if (running_var.ndim!=1):
           error += 1
           if verbose: dlr_common.DpuError("running_var dimension mis-match", flush=True)
       if (running_var.size!=in_data.shape[0]): # num of channels
           error += 1
           if verbose: dlr_common.DpuError("running_var size mis-match", flush=True)
       if (scale is not None) and (scale.ndim!=1):
           error += 1
           if verbose: dlr_common.DpuError(f"scale should be 1 dim: {scale.ndim}", flush=True)
       if (bias is not None) and (bias.ndim!=1):
           error += 1
           if verbose: dlr_common.DpuError(f"bias should be 1 dim: {bias.ndim}", flush=True)
       t_out_channel     = out_data.shape[0]
       t_out_size        = out_data.shape[1]
       t_in_channel      = in_data.shape[0]
       t_in_size         = in_data.shape[1]
       if (t_out_channel!=t_in_channel):
           error += 1
           dlr_common.DpuError(f"channel mis-match", flush=True)
       if (t_out_size!=t_in_size):
           error += 1
           dlr_common.DpuError(f"channel mis-match", flush=True)
       if (error!=0):
           dlr_common.DpuError("parameter mis-match", flush=True)
           return False
    #_fname=''
    #_ctype=''
    if out_data.dtype.type == np.int32:
        _fname = 'Norm1dBatchInt'
        _ctype = ctypes.c_int
    elif out_data.dtype.type == np.float32:
        _fname = 'Norm1dBatchFloat'
        _ctype = ctypes.c_float
    elif out_data.dtype.type == np.float64:
        _fname = 'Norm1dBatchDouble'
        _ctype = ctypes.c_double
    else:
        dlr_common.DpuError(" not support "+str(out_data.dtype.type), flush=True)
        return False
    _Norm1dBatch=dlr_common.WrapFunction(dlr_common._dlr
                                        ,_fname
                                        , None          # return type
                                        ,[ctypes.POINTER(_ctype) # out data
                                         ,ctypes.POINTER(_ctype) # in data
                                         ,ctypes.POINTER(_ctype) # running_mean
                                         ,ctypes.POINTER(_ctype) # running_var
                                         ,ctypes.POINTER(_ctype) # scale
                                         ,ctypes.POINTER(_ctype) # bias
                                         ,ctypes.c_uint    # in_size
                                         ,ctypes.c_ushort  # scale_size
                                         ,ctypes.c_ushort  # bias_size
                                         ,ctypes.c_ushort  # in_channel
                                         ,ctypes.c_float   # epsilon
                                         ,ctypes.c_int     # rigor
                                         ,ctypes.c_int ])  # verbose
    in_channel     = in_data.shape[0]
    in_size        = int(in_data.size/in_channel) # num of elements per channel
    CP_out_data    = out_data.ctypes.data_as(ctypes.POINTER(_ctype))
    CP_in_data     = in_data.ctypes.data_as(ctypes.POINTER(_ctype))
    CP_running_mean= running_mean.ctypes.data_as(ctypes.POINTER(_ctype))
    CP_running_var = running_var.ctypes.data_as(ctypes.POINTER(_ctype))
    CP_in_size     = ctypes.c_uint(in_size)
    CP_in_channel  = ctypes.c_ushort(in_channel)
    CP_epsilon     = ctypes.c_float(epsilon)
    CP_rigor       = 1 if rigor else 0
    CP_verbose     = 1 if verbose else 0
    if (scale is None) or (scale.size == 0):
       CP_scale       = ctypes.POINTER(_ctype)()
       CP_scale_size  = ctypes.c_ushort(0)
    else:
       CP_scale       = scale.ctypes.data_as(ctypes.POINTER(_ctype))
       CP_scale_size  = ctypes.c_ushort(scale.shape[1])
    if (bias is None) or (bias.size == 0):
       CP_bias        = ctypes.POINTER(_ctype)()
       CP_bias_size   = ctypes.c_ushort(0)
    else:
       CP_bias        = bias.ctypes.data_as(ctypes.POINTER(_ctype))
       CP_bias_size   = ctypes.c_ushort(bias.shape[1])
    _Norm1dBatch(CP_out_data
                ,CP_in_data
                ,CP_running_mean
                ,CP_running_var
                ,CP_scale
                ,CP_bias
                ,CP_in_size
                ,CP_scale_size
                ,CP_bias_size
                ,CP_in_channel
                ,CP_epsilon
                ,CP_rigor
                ,CP_verbose)
    return True

#===============================================================================
if __name__=='__main__':
    def TestNorm1dBatch(_dtype):
        """
        _dtype: specify data type of data one of {np.int32, np.float32, np.float64}
        """
        in_channel = 2
        in_size    = 5
        in_dim     = [in_channel, in_size, in_size]
        in_data    = (100+100)*np.random.random(size=in_dim)-100
        out_data = np.empty(in_dim, dtype=_dtype)
        running_mean = (100+100)*np.random.random(size=(in_channel))-100
        running_var  = (100+100)*np.random.random(size=(in_channel))-100
        scale        = (10+10)*np.random.random(size=(in_channel))-10
        bias         = (10+10)*np.random.random(size=(in_channel))-10
        if _dtype is np.int32:
            in_data      = np.int32(in_data)
            running_mean = np.int32(running_mean)
            running_var  = np.int32(running_var)
            scale        = np.int32(scale)
            bias         = np.int32(bias)
        epsilon = 1e-5
        status = Norm1dBatch( out_data
                            , in_data
                            , running_mean=running_mean
                            , running_var =running_var
                            , scale       =scale
                            , bias        =bias
                            , epsilon     =epsilon
                            , rigor=True
                            , verbose=True
                            )
        if status:
            dlr_common.DpuPrint(f"out_data:\n{out_data}", flush=True)
            dlr_common.DpuPrint(f"in_data:\n{in_data}", flush=True)
            dlr_common.DpuPrint(f"running_mean:\n{running_mean}", flush=True)
            dlr_common.DpuPrint(f"running_var:\n{running_var}", flush=True)
            dlr_common.DpuPrint(f"scale:\n{scale}", flush=True)
            dlr_common.DpuPrint(f"bias:\n{bias}", flush=True)
            dlr_common.DpuPrint(f"epsilon:\n{epsilon}", flush=True)

#===============================================================================
if __name__=='__main__':
    dlr_common.DpuPrint("Testing Norm1dBatch", flush=True)
    dlr_common.DpuPrint("*********************", flush=True)
    #TestNorm1dBatch(_dtype=np.int32)
    TestNorm1dBatch(_dtype=np.float32)
    #TestNorm1dBatch(_dtype=np.float64)

#===============================================================================
# Revision history:
#
# 2020.09.30: argument order of bias and bias_size changed
# 2020.04.25: Started by <NAME> (<EMAIL>)
#===============================================================================

#!/usr/bin/env python This file contains Python interface of norm_1d_batch. #------------------------------------------------------------------------------- #------------------------------------------------------------------------------- #=============================================================================== # in_channel x in_size (contiguous) # in_channel x in_size (contiguous) # in_channel # in_channel # None or in_channel (default 1) # None or in_channel (default 0) Returns True on success, otherwize returns False     Applies a 1D matrix multiplication over an input data data.     Note that all nd-array lists are NumPy (mutable), not PyTorch tensor (immutable).     :param out_data: <mutable> output data, out_data[channel][size][size]     :param in_data: input data, in_data[channel][size][size]     :param running_mean:     :param running_var:     :param scale:     :param bias:     :param epsilon:     :param rigor: check values rigorously when 'True'     :param verbose: output message more when 'True'     :return: 'True' on success, 'False' on failure.     Following is an example usage for PyTorch.         Norm1dBatch( tensor_out_data.data.numpy() # ndim x out_size                        , tenso_in_data.data.numpy()   # ndim x in_size                        , tensor_running_mean.data.numpy()   # out_size x in_size                        , tensor_running_var.data.numpy()     # out_size                        , tensor_scale.data.numpy()     # out_size                        , tensor_bias.data.numpy()     # out_size                        , epsilon                        , rigor=True                        , verbose=True) # num of channels # num of channels #_fname='' #_ctype='' # return type # out data # in data # running_mean # running_var # scale # bias # in_size # scale_size # bias_size # in_channel # epsilon # rigor # verbose # num of elements per channel #=============================================================================== _dtype: specify data type of data one of {np.int32, np.float32, np.float64} #=============================================================================== #TestNorm1dBatch(_dtype=np.int32) #TestNorm1dBatch(_dtype=np.float64) #=============================================================================== # Revision history: # # 2020.09.30: argument order of bias and bias_size changed # 2020.04.25: Started by <NAME> (<EMAIL>) #===============================================================================