

import torch 
import numpy as np 
from torch import optim
import torch.nn as nn
import torch.nn.parallel
from torch.autograd import Variable
import torch.nn.functional as F




class InferenceCallback:
    def __init__( self):
        pass 
    
    def on_start( self , *args , **kargs ):
        pass 
    
    def on_end(self , *args , **kargs ):
        pass
    
    def __call__( self , data_x=None , data_y=None , model_output=None  ):
        pass 
    

    
    
    
class Callback:
    def __init__(self , callback_fn=None  , event=None , frequency=1 ):
        
        self.on_train_batch_end_fn = None
        self.on_epoch_end_fn = None
        self.on_train_end_fn = None
        self.frequency = frequency 
        
        if not callback_fn is None:
            if event == "on_train_batch_end":
                self.on_train_batch_end_fn  = callback_fn 
            elif event == "on_epoch_end":
                self.on_epoch_end_fn  = callback_fn 
            elif event == "on_train_end":
                self.on_train_end_fn  = callback_fn 
            else:
                raise ValueError("invalid event %s"%event )
            
        self.n_done = 0
    
    def on_train_end(self , logs=None):
        self.n_done += 1 
        
        if self.n_done % self.frequency != 0:
            return 
        
        if not self.on_train_end_fn is None:
            self.on_train_end_fn( logs=logs )
        
    def on_epoch_end(self , epoch , logs=None):
        self.n_done += 1 
        
        if self.n_done % self.frequency != 0:
            return 
        
        if not self.on_epoch_end_fn is None:
            self.on_epoch_end_fn( epoch=epoch , logs=logs )
        
    def on_train_batch_end( self , batch , logs=None ):
        '''
        batch - Integer, index of batch within the current epoch.
        '''
        self.n_done += 1 
        
        if self.n_done % self.frequency != 0:
            return 
        
        if not self.on_train_batch_end_fn is None:
            self.on_train_batch_end_fn( batch=batch , logs=logs )
        

class ModelCheckpoint( Callback ):
    def __init__(self , checkpoint_path,  frequency=1 , only_save_at_end=False , overwrite_epochs=False ):
        super(ModelCheckpoint, self).__init__()
        self.frequency = frequency 
        self.checkpoint_path = checkpoint_path 
        self.only_save_at_end = only_save_at_end 
        self.overwrite_epochs = overwrite_epochs 
        
    def on_epoch_end( self , epoch , logs=None):
        
        if self.only_save_at_end :
            return 
        
        if (epoch+1)%self.frequency == 0:
            if self.overwrite_epochs :
                model_path = self.checkpoint_path + "_weights." + str( 0 )
            else:
                model_path = self.checkpoint_path + "_weights." + str( epoch )
            print("saving model " + model_path)
            self.model.save_weights(model_path  )
            
    def on_train_end( self , logs=None ):
        model_path = self.checkpoint_path + "_weights.final" 
        print("saving model final " + model_path)
        self.model.save_weights(model_path  )
        
    

# to log the loss values in  a text file 
class LossLogger( Callback ):
    def __init__(self , out_filename ):
        super(LossLogger, self).__init__()
        self.epoch_vals_history_dict = {}
        self.epochs = 0 
        self.out_filename = out_filename

        open(self.out_filename , "w").write(  ""  )
    
    def on_train_batch_end( self , batch , logs=None ):
        
        vals_dict = logs['batch_losses']

        for k in vals_dict:
            if not k in self.epoch_vals_history_dict:
                self.epoch_vals_history_dict[k] = []
            self.epoch_vals_history_dict[k].append( vals_dict[k])
        
        line_str = "Epoch %d Batch %d "%( self.epochs , batch )
        for k in vals_dict:
            line_str += k+":"+ "%.3f"%(vals_dict[k]) + " "
        line_str += "\n"

        open(self.out_filename , "a").write(  line_str  )
    
    def on_epoch_end( self , epoch , logs=None):
        
        open(self.out_filename , "a").write(  "=======\n"   )


        line_str = "Epoch %d "%( self.epochs  )
        for k in self.epoch_vals_history_dict:
            line_str += k+":"+ "%.3f"%(np.mean(self.epoch_vals_history_dict[k])) + " "
        line_str += "\n"

        open(self.out_filename , "a").write(  line_str  )
        open(self.out_filename , "a").write(  "=======\n\n"   )

        self.epoch_vals_history_dict = {}
        self.epochs += 1 




        
class TensorboardCallback( Callback ):
    def __init__(self):
        super(TensorboardCallback, self).__init__()
        # todo 




6bce16eebbdeec0e7293c5ac3ed9d889aa103d31
<mask token>


class Callback:
    <mask token>
    <mask token>
    <mask token>
    <mask token>


class ModelCheckpoint(Callback):

    def __init__(self, checkpoint_path, frequency=1, only_save_at_end=False,
        overwrite_epochs=False):
        super(ModelCheckpoint, self).__init__()
        self.frequency = frequency
        self.checkpoint_path = checkpoint_path
        self.only_save_at_end = only_save_at_end
        self.overwrite_epochs = overwrite_epochs

    def on_epoch_end(self, epoch, logs=None):
        if self.only_save_at_end:
            return
        if (epoch + 1) % self.frequency == 0:
            if self.overwrite_epochs:
                model_path = self.checkpoint_path + '_weights.' + str(0)
            else:
                model_path = self.checkpoint_path + '_weights.' + str(epoch)
            print('saving model ' + model_path)
            self.model.save_weights(model_path)

    def on_train_end(self, logs=None):
        model_path = self.checkpoint_path + '_weights.final'
        print('saving model final ' + model_path)
        self.model.save_weights(model_path)


class LossLogger(Callback):

    def __init__(self, out_filename):
        super(LossLogger, self).__init__()
        self.epoch_vals_history_dict = {}
        self.epochs = 0
        self.out_filename = out_filename
        open(self.out_filename, 'w').write('')

    def on_train_batch_end(self, batch, logs=None):
        vals_dict = logs['batch_losses']
        for k in vals_dict:
            if not k in self.epoch_vals_history_dict:
                self.epoch_vals_history_dict[k] = []
            self.epoch_vals_history_dict[k].append(vals_dict[k])
        line_str = 'Epoch %d Batch %d ' % (self.epochs, batch)
        for k in vals_dict:
            line_str += k + ':' + '%.3f' % vals_dict[k] + ' '
        line_str += '\n'
        open(self.out_filename, 'a').write(line_str)

    def on_epoch_end(self, epoch, logs=None):
        open(self.out_filename, 'a').write('=======\n')
        line_str = 'Epoch %d ' % self.epochs
        for k in self.epoch_vals_history_dict:
            line_str += k + ':' + '%.3f' % np.mean(self.
                epoch_vals_history_dict[k]) + ' '
        line_str += '\n'
        open(self.out_filename, 'a').write(line_str)
        open(self.out_filename, 'a').write('=======\n\n')
        self.epoch_vals_history_dict = {}
        self.epochs += 1


class TensorboardCallback(Callback):

    def __init__(self):
        super(TensorboardCallback, self).__init__()

<mask token>


class InferenceCallback:
    <mask token>
    <mask token>
    <mask token>
    <mask token>


class Callback:

    def __init__(self, callback_fn=None, event=None, frequency=1):
        self.on_train_batch_end_fn = None
        self.on_epoch_end_fn = None
        self.on_train_end_fn = None
        self.frequency = frequency
        if not callback_fn is None:
            if event == 'on_train_batch_end':
                self.on_train_batch_end_fn = callback_fn
            elif event == 'on_epoch_end':
                self.on_epoch_end_fn = callback_fn
            elif event == 'on_train_end':
                self.on_train_end_fn = callback_fn
            else:
                raise ValueError('invalid event %s' % event)
        self.n_done = 0

    def on_train_end(self, logs=None):
        self.n_done += 1
        if self.n_done % self.frequency != 0:
            return
        if not self.on_train_end_fn is None:
            self.on_train_end_fn(logs=logs)

    def on_epoch_end(self, epoch, logs=None):
        self.n_done += 1
        if self.n_done % self.frequency != 0:
            return
        if not self.on_epoch_end_fn is None:
            self.on_epoch_end_fn(epoch=epoch, logs=logs)

    def on_train_batch_end(self, batch, logs=None):
        """
        batch - Integer, index of batch within the current epoch.
        """
        self.n_done += 1
        if self.n_done % self.frequency != 0:
            return
        if not self.on_train_batch_end_fn is None:
            self.on_train_batch_end_fn(batch=batch, logs=logs)


class ModelCheckpoint(Callback):

    def __init__(self, checkpoint_path, frequency=1, only_save_at_end=False,
        overwrite_epochs=False):
        super(ModelCheckpoint, self).__init__()
        self.frequency = frequency
        self.checkpoint_path = checkpoint_path
        self.only_save_at_end = only_save_at_end
        self.overwrite_epochs = overwrite_epochs

    def on_epoch_end(self, epoch, logs=None):
        if self.only_save_at_end:
            return
        if (epoch + 1) % self.frequency == 0:
            if self.overwrite_epochs:
                model_path = self.checkpoint_path + '_weights.' + str(0)
            else:
                model_path = self.checkpoint_path + '_weights.' + str(epoch)
            print('saving model ' + model_path)
            self.model.save_weights(model_path)

    def on_train_end(self, logs=None):
        model_path = self.checkpoint_path + '_weights.final'
        print('saving model final ' + model_path)
        self.model.save_weights(model_path)


class LossLogger(Callback):

    def __init__(self, out_filename):
        super(LossLogger, self).__init__()
        self.epoch_vals_history_dict = {}
        self.epochs = 0
        self.out_filename = out_filename
        open(self.out_filename, 'w').write('')

    def on_train_batch_end(self, batch, logs=None):
        vals_dict = logs['batch_losses']
        for k in vals_dict:
            if not k in self.epoch_vals_history_dict:
                self.epoch_vals_history_dict[k] = []
            self.epoch_vals_history_dict[k].append(vals_dict[k])
        line_str = 'Epoch %d Batch %d ' % (self.epochs, batch)
        for k in vals_dict:
            line_str += k + ':' + '%.3f' % vals_dict[k] + ' '
        line_str += '\n'
        open(self.out_filename, 'a').write(line_str)

    def on_epoch_end(self, epoch, logs=None):
        open(self.out_filename, 'a').write('=======\n')
        line_str = 'Epoch %d ' % self.epochs
        for k in self.epoch_vals_history_dict:
            line_str += k + ':' + '%.3f' % np.mean(self.
                epoch_vals_history_dict[k]) + ' '
        line_str += '\n'
        open(self.out_filename, 'a').write(line_str)
        open(self.out_filename, 'a').write('=======\n\n')
        self.epoch_vals_history_dict = {}
        self.epochs += 1


class TensorboardCallback(Callback):

    def __init__(self):
        super(TensorboardCallback, self).__init__()

<mask token>


class InferenceCallback:

    def __init__(self):
        pass

    def on_start(self, *args, **kargs):
        pass
    <mask token>
    <mask token>


class Callback:

    def __init__(self, callback_fn=None, event=None, frequency=1):
        self.on_train_batch_end_fn = None
        self.on_epoch_end_fn = None
        self.on_train_end_fn = None
        self.frequency = frequency
        if not callback_fn is None:
            if event == 'on_train_batch_end':
                self.on_train_batch_end_fn = callback_fn
            elif event == 'on_epoch_end':
                self.on_epoch_end_fn = callback_fn
            elif event == 'on_train_end':
                self.on_train_end_fn = callback_fn
            else:
                raise ValueError('invalid event %s' % event)
        self.n_done = 0

    def on_train_end(self, logs=None):
        self.n_done += 1
        if self.n_done % self.frequency != 0:
            return
        if not self.on_train_end_fn is None:
            self.on_train_end_fn(logs=logs)

    def on_epoch_end(self, epoch, logs=None):
        self.n_done += 1
        if self.n_done % self.frequency != 0:
            return
        if not self.on_epoch_end_fn is None:
            self.on_epoch_end_fn(epoch=epoch, logs=logs)

    def on_train_batch_end(self, batch, logs=None):
        """
        batch - Integer, index of batch within the current epoch.
        """
        self.n_done += 1
        if self.n_done % self.frequency != 0:
            return
        if not self.on_train_batch_end_fn is None:
            self.on_train_batch_end_fn(batch=batch, logs=logs)


class ModelCheckpoint(Callback):

    def __init__(self, checkpoint_path, frequency=1, only_save_at_end=False,
        overwrite_epochs=False):
        super(ModelCheckpoint, self).__init__()
        self.frequency = frequency
        self.checkpoint_path = checkpoint_path
        self.only_save_at_end = only_save_at_end
        self.overwrite_epochs = overwrite_epochs

    def on_epoch_end(self, epoch, logs=None):
        if self.only_save_at_end:
            return
        if (epoch + 1) % self.frequency == 0:
            if self.overwrite_epochs:
                model_path = self.checkpoint_path + '_weights.' + str(0)
            else:
                model_path = self.checkpoint_path + '_weights.' + str(epoch)
            print('saving model ' + model_path)
            self.model.save_weights(model_path)

    def on_train_end(self, logs=None):
        model_path = self.checkpoint_path + '_weights.final'
        print('saving model final ' + model_path)
        self.model.save_weights(model_path)


class LossLogger(Callback):

    def __init__(self, out_filename):
        super(LossLogger, self).__init__()
        self.epoch_vals_history_dict = {}
        self.epochs = 0
        self.out_filename = out_filename
        open(self.out_filename, 'w').write('')

    def on_train_batch_end(self, batch, logs=None):
        vals_dict = logs['batch_losses']
        for k in vals_dict:
            if not k in self.epoch_vals_history_dict:
                self.epoch_vals_history_dict[k] = []
            self.epoch_vals_history_dict[k].append(vals_dict[k])
        line_str = 'Epoch %d Batch %d ' % (self.epochs, batch)
        for k in vals_dict:
            line_str += k + ':' + '%.3f' % vals_dict[k] + ' '
        line_str += '\n'
        open(self.out_filename, 'a').write(line_str)

    def on_epoch_end(self, epoch, logs=None):
        open(self.out_filename, 'a').write('=======\n')
        line_str = 'Epoch %d ' % self.epochs
        for k in self.epoch_vals_history_dict:
            line_str += k + ':' + '%.3f' % np.mean(self.
                epoch_vals_history_dict[k]) + ' '
        line_str += '\n'
        open(self.out_filename, 'a').write(line_str)
        open(self.out_filename, 'a').write('=======\n\n')
        self.epoch_vals_history_dict = {}
        self.epochs += 1


class TensorboardCallback(Callback):

    def __init__(self):
        super(TensorboardCallback, self).__init__()

<mask token>


class InferenceCallback:

    def __init__(self):
        pass

    def on_start(self, *args, **kargs):
        pass

    def on_end(self, *args, **kargs):
        pass

    def __call__(self, data_x=None, data_y=None, model_output=None):
        pass


class Callback:

    def __init__(self, callback_fn=None, event=None, frequency=1):
        self.on_train_batch_end_fn = None
        self.on_epoch_end_fn = None
        self.on_train_end_fn = None
        self.frequency = frequency
        if not callback_fn is None:
            if event == 'on_train_batch_end':
                self.on_train_batch_end_fn = callback_fn
            elif event == 'on_epoch_end':
                self.on_epoch_end_fn = callback_fn
            elif event == 'on_train_end':
                self.on_train_end_fn = callback_fn
            else:
                raise ValueError('invalid event %s' % event)
        self.n_done = 0

    def on_train_end(self, logs=None):
        self.n_done += 1
        if self.n_done % self.frequency != 0:
            return
        if not self.on_train_end_fn is None:
            self.on_train_end_fn(logs=logs)

    def on_epoch_end(self, epoch, logs=None):
        self.n_done += 1
        if self.n_done % self.frequency != 0:
            return
        if not self.on_epoch_end_fn is None:
            self.on_epoch_end_fn(epoch=epoch, logs=logs)

    def on_train_batch_end(self, batch, logs=None):
        """
        batch - Integer, index of batch within the current epoch.
        """
        self.n_done += 1
        if self.n_done % self.frequency != 0:
            return
        if not self.on_train_batch_end_fn is None:
            self.on_train_batch_end_fn(batch=batch, logs=logs)


class ModelCheckpoint(Callback):

    def __init__(self, checkpoint_path, frequency=1, only_save_at_end=False,
        overwrite_epochs=False):
        super(ModelCheckpoint, self).__init__()
        self.frequency = frequency
        self.checkpoint_path = checkpoint_path
        self.only_save_at_end = only_save_at_end
        self.overwrite_epochs = overwrite_epochs

    def on_epoch_end(self, epoch, logs=None):
        if self.only_save_at_end:
            return
        if (epoch + 1) % self.frequency == 0:
            if self.overwrite_epochs:
                model_path = self.checkpoint_path + '_weights.' + str(0)
            else:
                model_path = self.checkpoint_path + '_weights.' + str(epoch)
            print('saving model ' + model_path)
            self.model.save_weights(model_path)

    def on_train_end(self, logs=None):
        model_path = self.checkpoint_path + '_weights.final'
        print('saving model final ' + model_path)
        self.model.save_weights(model_path)


class LossLogger(Callback):

    def __init__(self, out_filename):
        super(LossLogger, self).__init__()
        self.epoch_vals_history_dict = {}
        self.epochs = 0
        self.out_filename = out_filename
        open(self.out_filename, 'w').write('')

    def on_train_batch_end(self, batch, logs=None):
        vals_dict = logs['batch_losses']
        for k in vals_dict:
            if not k in self.epoch_vals_history_dict:
                self.epoch_vals_history_dict[k] = []
            self.epoch_vals_history_dict[k].append(vals_dict[k])
        line_str = 'Epoch %d Batch %d ' % (self.epochs, batch)
        for k in vals_dict:
            line_str += k + ':' + '%.3f' % vals_dict[k] + ' '
        line_str += '\n'
        open(self.out_filename, 'a').write(line_str)

    def on_epoch_end(self, epoch, logs=None):
        open(self.out_filename, 'a').write('=======\n')
        line_str = 'Epoch %d ' % self.epochs
        for k in self.epoch_vals_history_dict:
            line_str += k + ':' + '%.3f' % np.mean(self.
                epoch_vals_history_dict[k]) + ' '
        line_str += '\n'
        open(self.out_filename, 'a').write(line_str)
        open(self.out_filename, 'a').write('=======\n\n')
        self.epoch_vals_history_dict = {}
        self.epochs += 1


class TensorboardCallback(Callback):

    def __init__(self):
        super(TensorboardCallback, self).__init__()



import torch 
import numpy as np 
from torch import optim
import torch.nn as nn
import torch.nn.parallel
from torch.autograd import Variable
import torch.nn.functional as F




class InferenceCallback:
    def __init__( self):
        pass 
    
    def on_start( self , *args , **kargs ):
        pass 
    
    def on_end(self , *args , **kargs ):
        pass
    
    def __call__( self , data_x=None , data_y=None , model_output=None  ):
        pass 
    

    
    
    
class Callback:
    def __init__(self , callback_fn=None  , event=None , frequency=1 ):
        
        self.on_train_batch_end_fn = None
        self.on_epoch_end_fn = None
        self.on_train_end_fn = None
        self.frequency = frequency 
        
        if not callback_fn is None:
            if event == "on_train_batch_end":
                self.on_train_batch_end_fn  = callback_fn 
            elif event == "on_epoch_end":
                self.on_epoch_end_fn  = callback_fn 
            elif event == "on_train_end":
                self.on_train_end_fn  = callback_fn 
            else:
                raise ValueError("invalid event %s"%event )
            
        self.n_done = 0
    
    def on_train_end(self , logs=None):
        self.n_done += 1 
        
        if self.n_done % self.frequency != 0:
            return 
        
        if not self.on_train_end_fn is None:
            self.on_train_end_fn( logs=logs )
        
    def on_epoch_end(self , epoch , logs=None):
        self.n_done += 1 
        
        if self.n_done % self.frequency != 0:
            return 
        
        if not self.on_epoch_end_fn is None:
            self.on_epoch_end_fn( epoch=epoch , logs=logs )
        
    def on_train_batch_end( self , batch , logs=None ):
        '''
        batch - Integer, index of batch within the current epoch.
        '''
        self.n_done += 1 
        
        if self.n_done % self.frequency != 0:
            return 
        
        if not self.on_train_batch_end_fn is None:
            self.on_train_batch_end_fn( batch=batch , logs=logs )
        

class ModelCheckpoint( Callback ):
    def __init__(self , checkpoint_path,  frequency=1 , only_save_at_end=False , overwrite_epochs=False ):
        super(ModelCheckpoint, self).__init__()
        self.frequency = frequency 
        self.checkpoint_path = checkpoint_path 
        self.only_save_at_end = only_save_at_end 
        self.overwrite_epochs = overwrite_epochs 
        
    def on_epoch_end( self , epoch , logs=None):
        
        if self.only_save_at_end :
            return 
        
        if (epoch+1)%self.frequency == 0:
            if self.overwrite_epochs :
                model_path = self.checkpoint_path + "_weights." + str( 0 )
            else:
                model_path = self.checkpoint_path + "_weights." + str( epoch )
            print("saving model " + model_path)
            self.model.save_weights(model_path  )
            
    def on_train_end( self , logs=None ):
        model_path = self.checkpoint_path + "_weights.final" 
        print("saving model final " + model_path)
        self.model.save_weights(model_path  )
        
    

# to log the loss values in  a text file 
class LossLogger( Callback ):
    def __init__(self , out_filename ):
        super(LossLogger, self).__init__()
        self.epoch_vals_history_dict = {}
        self.epochs = 0 
        self.out_filename = out_filename

        open(self.out_filename , "w").write(  ""  )
    
    def on_train_batch_end( self , batch , logs=None ):
        
        vals_dict = logs['batch_losses']

        for k in vals_dict:
            if not k in self.epoch_vals_history_dict:
                self.epoch_vals_history_dict[k] = []
            self.epoch_vals_history_dict[k].append( vals_dict[k])
        
        line_str = "Epoch %d Batch %d "%( self.epochs , batch )
        for k in vals_dict:
            line_str += k+":"+ "%.3f"%(vals_dict[k]) + " "
        line_str += "\n"

        open(self.out_filename , "a").write(  line_str  )
    
    def on_epoch_end( self , epoch , logs=None):
        
        open(self.out_filename , "a").write(  "=======\n"   )


        line_str = "Epoch %d "%( self.epochs  )
        for k in self.epoch_vals_history_dict:
            line_str += k+":"+ "%.3f"%(np.mean(self.epoch_vals_history_dict[k])) + " "
        line_str += "\n"

        open(self.out_filename , "a").write(  line_str  )
        open(self.out_filename , "a").write(  "=======\n\n"   )

        self.epoch_vals_history_dict = {}
        self.epochs += 1 




        
class TensorboardCallback( Callback ):
    def __init__(self):
        super(TensorboardCallback, self).__init__()
        # todo 



