from netCDF4 import Dataset, num2date
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
import argparse
import ast
import gc
import logging
import math

import sys
import time

import numpy as np
import pandas as pd
import psutil

import tensorflow as tf
from tensorflow.keras.mixed_precision import experimental as mixed_precision

try:
    import tensorflow_addons as tfa
except Exception as e:
    tfa = None

import data_generators
import custom_losses as cl
import hparameters
import models
import utility

tf.keras.backend.set_floatx('float16')
tf.keras.backend.set_epsilon(1e-3)

try:
    gpu_devices = tf.config.list_physical_devices('GPU')
except Exception as e:
    gpu_devices = tf.config.experimental.list_physical_devices('GPU')

policy = mixed_precision.Policy('mixed_float16')
mixed_precision.set_policy(policy)

def is_compatible_with(self, other):
    """Returns True if the `other` DType will be converted to this DType.
    Monkey patch: incompatibility issues between tfa.optimizers and mixed precision training
    The conversion rules are as follows:
    ```python
    DType(T)       .is_compatible_with(DType(T))        == True
    ```
    Args:
        other: A `DType` (or object that may be converted to a `DType`).
    Returns:
        True if a Tensor of the `other` `DType` will be implicitly converted to
        this `DType`.
    """
    other = tf.dtypes.as_dtype(other)
    if self._type_enum==19 and other.as_datatype_enum==1:
        return True

    return self._type_enum in (other.as_datatype_enum,
                                other.base_dtype.as_datatype_enum)
tf.DType.is_compatible_with = is_compatible_with

class WeatherModel():
    """Handles the Training of the Deep Learning Weather model

        Example of how to use:
        WeatherModel = WeatherModel( t_params, m_params)
        WeatherModel.initialize_scheme_era5Eobs()    #Initializes datasets for ERA5 and Eobs
        WeatherModel.train_model()                   #Trains and saves model
    """    
    def __init__(self, t_params, m_params): 
        """Train the TRU_NET Model
        """
        self.t_params = t_params
        self.m_params = m_params
        
    def initialize_scheme_era5Eobs(self):
        """Initialization scheme for the ERA5 and E-OBS datasets.
            This method creates the datasets
        """        
        # region ---- Parameters  related to training length and training reporting frequency 
        era5_eobs = data_generators.Era5_Eobs( self.t_params, self.m_params)

        # hparameters files calculates train_batches assuing we are only evaluating one location, 
            # therefore we must adjust got multiple locations (loc_count)
        self.t_params['train_batches'] = int(self.t_params['train_batches'] * era5_eobs.loc_count)
        self.t_params['val_batches'] = int(self.t_params['val_batches'] * era5_eobs.loc_count)

        # The fequency at which we report during training and validation i.e every 10% of minibatches report training loss and training mse
        self.train_batch_report_freq = max( int(self.t_params['train_batches']*self.t_params['reporting_freq']), 3)
        self.val_batch_report_freq = max( int(self.t_params['val_batches']*self.t_params['reporting_freq']), 3)
        #endregion

        # region ---- Restoring/Creating New Training Records and Restoring training progress
            #This training records keeps track of the losses on each epoch
        try:
            self.df_training_info = pd.read_csv( "checkpoints/{}/checkpoint_scores.csv".format(utility.model_name_mkr(m_params,t_params=self.t_params,htuning=m_params.get('htuning',False))), header=0, index_col=False) 
            self.df_training_info = self.df_training_info[['Epoch','Train_loss','Train_mse','Val_loss','Val_mse','Checkpoint_Path','Last_Trained_Batch']]
            self.start_epoch =  int(max([self.df_training_info['Epoch'][0]], default=0))
            last_batch = int( self.df_training_info.loc[self.df_training_info['Epoch']==self.start_epoch,'Last_Trained_Batch'].iloc[0] )
            if(last_batch in [-1, self.t_params['train_batches']] ):
                self.start_epoch = self.start_epoch + 1
                self.batches_to_skip = 0 
            else:
                self.batches_to_skip = last_batch
            print("Recovered training records")

        except FileNotFoundError as e:
            #If no file found, then make new training records file
            self.df_training_info = pd.DataFrame(columns=['Epoch','Train_loss','Train_mse','Val_loss','Val_mse','Checkpoint_Path','Last_Trained_Batch'] ) 
            self.batches_to_skip = 0
            self.start_epoch = 0
            print("Did not recover training records. Starting from scratch")
        # endregion

        # region ---- Defining Model / Optimizer / Losses / Metrics / Records / Checkpoints / Tensorboard 
        devices = tf.config.get_visible_devices() #tf.config.experimental.list_physical_devices('GPU')
        #gpus_names = [ device.name for device in devices if  device.device_type == "GPU" ]
        #self.strategy = tf.distribute.MirroredStrategy( devices=gpus_names ) #OneDeviceStrategy(device="/GPU:0") # 
        self.strategy = tf.distribute.MirroredStrategy( )
        assert self.t_params['batch_size'] % self.strategy.num_replicas_in_sync  == 0
        print("Number of Devices used in MirroredStrategy: {}".format(self.strategy.num_replicas_in_sync))
        with self.strategy.scope():   
            #Model
            self.strategy_gpu_count = self.strategy.num_replicas_in_sync    
            self.t_params['gpu_count'] = self.strategy.num_replicas_in_sync    
            self.model = models.model_loader( self.t_params, self.m_params )
            
            #Optimizer
            optimizer = tfa.optimizers.RectifiedAdam( **self.m_params['rec_adam_params'], total_steps=self.t_params['train_batches']*20) 

            self.optimizer = mixed_precision.LossScaleOptimizer( optimizer, loss_scale=tf.mixed_precision.experimental.DynamicLossScale() ) 
                    
            # These objects will aggregate losses and metrics across batches and epochs
            self.loss_agg_batch = tf.keras.metrics.Mean(name='loss_agg_batch' )
            self.loss_agg_epoch = tf.keras.metrics.Mean(name="loss_agg_epoch")

            self.mse_agg_epoch = tf.keras.metrics.Mean(name='mse_agg_epoch')
            
            self.loss_agg_val = tf.keras.metrics.Mean(name='loss_agg_val')
            self.mse_agg_val = tf.keras.metrics.Mean(name='mse_agg_val')
            
        #checkpoints  (For Epochs)
            #The CheckpointManagers can be called to serializae the weights within TRUNET
        checkpoint_path_epoch = "./checkpoints/{}/epoch".format(utility.model_name_mkr(m_params,t_params=self.t_params, htuning=m_params.get('htuning',False) ))
        os.makedirs(checkpoint_path_epoch,exist_ok=True)
        
        with self.strategy.scope():
            ckpt_epoch = tf.train.Checkpoint(model=self.model, optimizer=self.optimizer)
            self.ckpt_mngr_epoch = tf.train.CheckpointManager(ckpt_epoch, checkpoint_path_epoch, max_to_keep=self.t_params['checkpoints_to_keep'], keep_checkpoint_every_n_hours=None)    
        
            #restoring last checkpoint if it exists
            if self.ckpt_mngr_epoch.latest_checkpoint: 
                # compat: Initializing model and optimizer before restoring from checkpoint
                try:
                    ckpt_epoch.restore(self.ckpt_mngr_epoch.latest_checkpoint).assert_consumed()            
                except AssertionError as e:
                    ckpt_epoch.restore(self.ckpt_mngr_epoch.latest_checkpoint)              
                print (' Restoring model from best checkpoint')
            else:
                print (' Initializing model from scratch')
        
        #Tensorboard
        os.makedirs("log_tensboard/{}".format(utility.model_name_mkr(m_params, t_params=self.t_params, htuning=self.m_params.get('htuning',False) )), exist_ok=True ) 
        #self.writer = tf.summary.create_file_writer( "log_tensboard/{}".format(utility.model_name_mkr(m_params,t_params=self.t_params, htuning=self.m_params.get('htuning',False) ) ) )
        # endregion
        
        # region ---- Making Datasets
        
        #caching dataset to file post pre-processing steps have been completed 
        cache_suffix = utility.cache_suffix_mkr( m_params, self.t_params )
        os.makedirs( './Data/data_cache/', exist_ok=True  )

        _ds_train_val, _  = era5_eobs.load_data_era5eobs( self.t_params['train_batches'] + self.t_params['val_batches'] , self.t_params['start_date'], self.t_params['parallel_calls'] )

        ds_train = _ds_train_val.take(self.t_params['train_batches'] )
        ds_val = _ds_train_val.skip(self.t_params['train_batches'] ).take(self.t_params['val_batches'])

        #TODO: undo cache
        ds_train = ds_train.cache('Data/data_cache/train'+cache_suffix ) 
        ds_val = ds_val.cache('Data/data_cache/val'+cache_suffix )

        ds_train = ds_train.unbatch().shuffle( self.t_params['batch_size']*int(self.t_params['train_batches']/5), reshuffle_each_iteration=True).batch(self.t_params['batch_size']) #.repeat(self.t_params['epochs']-self.start_epoch)

        ds_train_val = ds_train.concatenate(ds_val)
        ds_train_val = ds_train_val.repeat(self.t_params.get('epochs',100)-self.start_epoch)
        self.ds_train_val = self.strategy.experimental_distribute_dataset(dataset=ds_train_val)
        self.iter_train_val = enumerate(self.ds_train_val)

        bc_ds_in_train = int( self.t_params['train_batches']/era5_eobs.loc_count  ) #batch_count
        bc_ds_in_val = int( self.t_params['val_batches']/era5_eobs.loc_count )

        self.reset_idxs_training = np.cumsum( [bc_ds_in_train]*era5_eobs.loc_count )
        self.reset_idxs_validation = np.cumsum( [bc_ds_in_val]*era5_eobs.loc_count )        
        # endregion

    def train_model(self):
        """During training we produce a prediction for a (n by n) square patch. 
            But we caculate losses on a central (h, w) region within the (n by n) patch
            This central region is defined by "bounds" below
        """        

        bounds = cl.central_region_bounds(self.m_params['region_grid_params']) #list [ lower_h_bound[0], upper_h_bound[0], lower_w_bound[1], upper_w_bound[1] ]
        
        #Training for n epochs
        #self.t_params['train_batches'] = self.t_params['train_batches'] if self.m_params['time_sequential'] else int(self.t_params['train_batches']*self.t_params['lookback_target'] )
        #self.t_params['val_batches'] = self.t_params['val_batches'] if self.m_params['time_sequential'] else int(self.t_params['val_batches']*self.t_params['lookback_target'] )

        for epoch in range(self.start_epoch, int(self.t_params['epochs']) ):
            
            #region resetting metrics, losses, records, timers
            self.loss_agg_batch.reset_states()
            self.loss_agg_epoch.reset_states()
            self.mse_agg_epoch.reset_states()
            
            
            self.loss_agg_val.reset_states()
            self.mse_agg_val.reset_states()
            
            self.df_training_info = self.df_training_info.append( { 'Epoch':epoch, 'Last_Trained_Batch':0 }, ignore_index=True )
            
            start_epoch_train = time.time()
            start_batch_group_time = time.time()
            batch=0           
            
            print("\n\nStarting EPOCH {}".format(epoch ))
            #endregion 
            
            # --- Training Loops
            for batch in range(self.batches_to_skip+1,self.t_params['train_batches'] +1):
                               
                # get next set of training datums
                idx, (feature, target, mask) = next(self.iter_train_val)
                

                gradients = self.distributed_train_step( feature, target, mask, bounds, 0.0 )
                #print(gradients)
                
                # reporting
                if( batch % self.train_batch_report_freq==0 or batch == self.t_params['train_batches']):
                    batch_group_time =  time.time() - start_batch_group_time
                    est_completion_time_seconds = (batch_group_time/self.t_params['reporting_freq']) * (1 - batch/self.t_params['train_batches'])
                    est_completion_time_mins = est_completion_time_seconds/60

                    print("\t\tBatch:{}/{}\tTrain Loss: {:.8f} \t Batch Time:{:.4f}\tEpoch mins left:{:.1f}".format(batch, self.t_params['train_batches'], self.loss_agg_batch.result(), batch_group_time, est_completion_time_mins ) )
                    
                    # resetting time and losses
                    start_batch_group_time = time.time()

                    # Updating record of the last batch to be operated on in training epoch
                    self.df_training_info.loc[ ( self.df_training_info['Epoch']==epoch) , ['Last_Trained_Batch'] ] = batch
                    self.df_training_info.to_csv( path_or_buf="checkpoints/{}/checkpoint_scores.csv".format(utility.model_name_mkr(self.m_params,t_params=self.t_params, htuning=m_params.get('htuning',False) )), header=True, index=False )


                li_losses = [self.loss_agg_batch.result()]
                li_names = ['train_loss_batch']
                step = batch + (epoch)*self.t_params['train_batches']
                #utility.tensorboard_record( self.writer.as_default(), li_losses, li_names, step, gradients, self.model.trainable_variables )
                #utility.tensorboard_record( self.writer.as_default(), li_losses, li_names, step, None, None )
                self.loss_agg_batch.reset_states()

                if batch in self.reset_idxs_training:
                    self.model.reset_states()
                    
            # --- Tensorboard record          
            li_losses = [self.loss_agg_epoch.result(), self.mse_agg_epoch.result()]
            li_names = ['train_loss_epoch','train_mse_epoch']
            #utility.tensorboard_record( self.writer.as_default(), li_losses, li_names, epoch)
            
            
            print("\tStarting Validation")
            start_batch_group_time = time.time()

            # --- Validation Loops
            for batch in range(1, self.t_params['val_batches']+1):
                
                # next datum
                idx, (feature, target, mask) = next(self.iter_train_val)
                
                bool_cmpltd = self.distributed_val_step(feature, target, mask, bounds)

                # Reporting for validation
                if batch % self.val_batch_report_freq == 0 or batch==self.t_params['val_batches'] :
                    batch_group_time            =  time.time() - start_batch_group_time
                    est_completion_time_seconds = (batch_group_time/self.t_params['reporting_freq']) * (1 -  batch/self.t_params['val_batches'])
                    est_completion_time_mins    = est_completion_time_seconds/60

                    print("\t\tCompleted Validation Batch:{}/{} \t Time:{:.4f} \tEst Time Left:{:.1f}".format( batch, self.t_params['val_batches'], batch_group_time, est_completion_time_mins))
                                                
                    start_batch_group_time = time.time()
                
                if batch in self.reset_idxs_validation:
                    self.model.reset_states()

            # region - End of Epoch Reporting and Early iteration Callback
            print("\tEpoch:{}\t Train Loss:{:.8f}\t Train MSE:{:.5f}\t Val Loss:{:.5f}\t Val MSE:{:.5f}\t  Time:{:.5f}".format(epoch, self.loss_agg_epoch.result(), self.mse_agg_epoch.result(),
                         
                        self.loss_agg_val.result(), self.mse_agg_val.result()  ,time.time()-start_epoch_train  ) )
                    
            #utility.tensorboard_record( self.writer.as_default(), [self.loss_agg_val.result(), self.mse_agg_val.result()], ['Validation Loss', 'Validation MSE' ], epoch  )                    
            self.df_training_info = utility.update_checkpoints_epoch(self.df_training_info, epoch, self.loss_agg_epoch, self.loss_agg_val, self.ckpt_mngr_epoch, self.t_params, 
                    self.m_params, self.mse_agg_epoch ,self.mse_agg_val,  self.t_params['objective'] )
            
            # Early Stop Callback 
            if epoch > ( max( self.df_training_info.loc[:, 'Epoch'], default=0 ) + self.t_params['early_stopping_period']) :
                print("Model Stopping Early at EPOCH {}".format(epoch))
                print(self.df_training_info)
                break
            # endregion
        
        print("Model Training Finished")

    def train_step(self, feature, target, mask, bounds, _init):
        
        if _init==1.0:
            if self.m_params['time_sequential'] == True:
                inp_shape = [self.t_params['batch_size'], self.t_params['lookback_feature']] + self.m_params['region_grid_params']['outer_box_dims'] + [len(self.t_params['vars_for_feature'])]
            else:
                inp_shape = [self.t_params['batch_size'] ] + self.m_params['region_grid_params']['outer_box_dims'] + [ int(self.t_params['lookback_feature']*len(self.t_params['vars_for_feature'])) ]
           
            _ = self.model( tf.zeros( inp_shape, dtype=tf.float16), self.t_params['trainable'] )    #( bs, tar_seq_len, h, w)

            gradients = [ tf.zeros_like(t_var, dtype=tf.float32 ) for t_var in self.model.trainable_variables  ]
            self.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))
            return [0]

        with tf.GradientTape(persistent=False) as tape:
                                   
            # non conditional continuous training
            if self.m_params['model_type_settings']['discrete_continuous'] == False:
                
                #making predictions
                preds = self.model( feature, self.t_params['trainable'] ) #( bs, tar_seq_len, h, w)
                preds = tf.squeeze( preds,axis=[-1] )

                
                preds   = cl.extract_central_region(preds, bounds)
                mask    = cl.extract_central_region(mask, bounds)
                target  = cl.extract_central_region(target, bounds)

                #Applying mask
                preds_masked = tf.boolean_mask( preds, mask )
                target_masked = tf.boolean_mask( target, mask ) 

                # reversing standardization
                preds_masked = utility.standardize_ati( preds_masked, self.t_params['normalization_shift']['rain'], self.t_params['normalization_scales']['rain'], reverse=True)

                # getting losses for records and/or optimizer
                metric_mse = cl.mse(target_masked, preds_masked) 
                loss_to_optimize = metric_mse

            # conditional continuous training        
            elif self.m_params['model_type_settings']['discrete_continuous'] == True:
                
                # Producing predictions - conditional rain value and prob of rain
                preds   = self.model( feature, self.t_params['trainable'] ) # ( bs, seq_len, h, w, 1)
                preds   = tf.squeeze(preds, axis=[-1])
                preds, probs = tf.unstack(preds, axis=0) 

                # extracting the central region of interest
            
                preds   = cl.extract_central_region(preds, bounds)
                probs   = cl.extract_central_region(probs, bounds)
                mask    = cl.extract_central_region(mask, bounds)
                target  = cl.extract_central_region(target, bounds)

                # applying mask to predicted values
                preds_masked    = tf.boolean_mask(preds, mask )
                probs_masked    = tf.boolean_mask(probs, mask ) 
                target_masked   = tf.boolean_mask(target, mask )
                
                # Reverising standardization of predictions 
                preds_masked    = utility.standardize_ati( preds_masked, self.t_params['normalization_shift']['rain'], 
                                                        self.t_params['normalization_scales']['rain'], reverse=True) 
                                                        
                # Getting true labels and predicted labels for whether or not it rained [ 1 if if did rain, 0 if it did not rain]
                labels_true = tf.where( target_masked > 0.0, 1.0, 0.0 )
                labels_pred = probs_masked 

                all_count = tf.size( labels_true, out_type=tf.int64 )                
                
                # region Calculating Losses and Metrics
                metric_mse  = cl.mse( target_masked, cl.cond_rain(preds_masked, probs_masked, threshold=0.5) )   
                    # To calculate metric_mse for CC model we assume that pred_rain=0 if pred_prob<=0.5 

                # CC Normal loss
                loss_to_optimize = 0
                loss_to_optimize += cl.mse( target_masked, preds_masked, all_count )    
                loss_to_optimize += tf.reduce_mean( tf.keras.backend.binary_crossentropy(labels_true, labels_pred, from_logits=False) )         
                # endregion

            loss_to_optimize_agg = tf.grad_pass_through( lambda x:  x/self.strategy_gpu_count )(loss_to_optimize)
            scaled_loss = self.optimizer.get_scaled_loss( loss_to_optimize_agg )
            scaled_gradients = tape.gradient( scaled_loss, self.model.trainable_variables )
            unscaled_gradients = self.optimizer.get_unscaled_gradients(scaled_gradients)
             
            gradients, _ = tf.clip_by_global_norm( unscaled_gradients, clip_norm=self.m_params['clip_norm'] ) #gradient clipping
            self.optimizer.apply_gradients( zip(gradients, self.model.trainable_variables))
        
        # Metrics (batchwise, epoch)  
        self.loss_agg_batch( loss_to_optimize )
        self.loss_agg_epoch( loss_to_optimize )
        self.mse_agg_epoch( metric_mse )    
        
        val = cl.rNmse(target_masked, preds_masked, 10.0)

        

        return gradients
                
    def val_step(self, feature, target, mask, bounds):
                    
        # Non CC distribution
        if self.m_params['model_type_settings']['discrete_continuous'] == False:
            
            # Get predictions
            preds = self.model(feature, False )
            preds = tf.squeeze(preds)

            # Extracting central region for evaluation
            preds   = cl.extract_central_region(preds, bounds)
            mask    = cl.extract_central_region(mask, bounds)
            target  = cl.extract_central_region(target, bounds)
            
            # Applying masks to predictions
            preds_masked = tf.boolean_mask( preds, mask )
            target_masked = tf.boolean_mask( target, mask )
            preds_masked = utility.standardize_ati( preds_masked, self.t_params['normalization_shift']['rain'], 
                                                    self.t_params['normalization_scales']['rain'], reverse=True)
            # Updating losses
            mse = cl.mse( target_masked , preds_masked ) 
            loss = mse                

        # CC distribution
        elif self.m_params['model_type_settings']['discrete_continuous'] == True:
            
            # Get predictions
            preds = self.model(feature, training=False )
            preds = tf.squeeze(preds,axis=[-1])
            preds, probs = tf.unstack(preds, axis=0)

            # Extracting central region for evaluation
        
            preds   = cl.extract_central_region(preds, bounds)
            probs   = cl.extract_central_region(probs, bounds)
            mask    = cl.extract_central_region(mask,  bounds)
            target  = cl.extract_central_region(target,bounds)

            # Applying masks to predictions
            preds_masked    = tf.boolean_mask( preds, mask )
            probs_masked    = tf.boolean_mask( probs, mask)
            target_masked   = tf.boolean_mask( target, mask )
            preds_masked    = utility.standardize_ati( preds_masked, self.t_params['normalization_shift']['rain'], 
                                                    self.t_params['normalization_scales']['rain'], reverse=True)

            # Getting classification labels for whether or not it rained
            
            labels_true = tf.where( target_masked > 0.0, 1.0, 0.0 )
            labels_pred = probs_masked 

            all_count = tf.size( labels_true, out_type=tf.int64 )

            # calculating seperate mse for reporting
                # This mse metric assumes that if probability of rain is predicted below 0.5, the rain value is 0
            mse = cl.mse( target_masked, cl.cond_rain( preds_masked, probs_masked, threshold=0.5) )

            # Calculating cross entropy loss                         
            loss = tf.reduce_mean(  tf.keras.backend.binary_crossentropy( labels_true, labels_pred, from_logits=False) )

            # Calculating conditinal continuous loss
            loss    += cl.mse( target_masked, preds_masked, all_count )

        self.loss_agg_val(loss)
        self.mse_agg_val(mse)
                    
        return True
    
    @tf.function
    def distributed_train_step(self, feature, target, mask, bounds, _init):
        gradients = self.strategy.run( self.train_step, args=(feature, target, mask, bounds, _init) )
        return gradients
    
    @tf.function
    def distributed_val_step(self, feature, target, mask, bounds):
        bool_completed = self.strategy.run( self.val_step, args=(feature, target, mask, bounds))
        return bool_completed


if __name__ == "__main__":
    s_dir = utility.get_script_directory(sys.argv[0])
    args_dict = utility.parse_arguments(s_dir)

    # get training and model params
    t_params, m_params = utility.load_params(args_dict)
    
    # Initialize and  train model
    weather_model = WeatherModel(t_params, m_params)
    weather_model.initialize_scheme_era5Eobs()
    weather_model.train_model()
    

from netCDF4 import Dataset, num2date
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
import argparse
import ast
import gc
import logging
import math

import sys
import time

import numpy as np
import pandas as pd
import psutil

import tensorflow as tf
from tensorflow.keras.mixed_precision import experimental as mixed_precision

try:
    import tensorflow_addons as tfa
except Exception as e:
    tfa = None

import data_generators
import custom_losses as cl
import hparameters
import models
import utility

tf.keras.backend.set_floatx('float16')
tf.keras.backend.set_epsilon(1e-3)

try:
    gpu_devices = tf.config.list_physical_devices('GPU')
except Exception as e:
    gpu_devices = tf.config.experimental.list_physical_devices('GPU')

policy = mixed_precision.Policy('mixed_float16')
mixed_precision.set_policy(policy)

def is_compatible_with(self, other):
    """Returns True if the `other` DType will be converted to this DType.
    Monkey patch: incompatibility issues between tfa.optimizers and mixed precision training
    The conversion rules are as follows:
    ```python
    DType(T)       .is_compatible_with(DType(T))        == True
    ```
    Args:
        other: A `DType` (or object that may be converted to a `DType`).
    Returns:
        True if a Tensor of the `other` `DType` will be implicitly converted to
        this `DType`.
    """
    other = tf.dtypes.as_dtype(other)
    if self._type_enum==19 and other.as_datatype_enum==1:
        return True

    return self._type_enum in (other.as_datatype_enum,
                                other.base_dtype.as_datatype_enum)
tf.DType.is_compatible_with = is_compatible_with

class WeatherModel():
    """Handles the Training of the Deep Learning Weather model

        Example of how to use:
        WeatherModel = WeatherModel( t_params, m_params)
        WeatherModel.initialize_scheme_era5Eobs()    #Initializes datasets for ERA5 and Eobs
        WeatherModel.train_model()                   #Trains and saves model
    """    
    def __init__(self, t_params, m_params): 
        """Train the TRU_NET Model
        """
        self.t_params = t_params
        self.m_params = m_params
        
    def initialize_scheme_era5Eobs(self):
        """Initialization scheme for the ERA5 and E-OBS datasets.
            This method creates the datasets
        """        
        # region ---- Parameters  related to training length and training reporting frequency 
        era5_eobs = data_generators.Era5_Eobs( self.t_params, self.m_params)

        # hparameters files calculates train_batches assuing we are only evaluating one location, 
            # therefore we must adjust got multiple locations (loc_count)
        self.t_params['train_batches'] = int(self.t_params['train_batches'] * era5_eobs.loc_count)
        self.t_params['val_batches'] = int(self.t_params['val_batches'] * era5_eobs.loc_count)

        # The fequency at which we report during training and validation i.e every 10% of minibatches report training loss and training mse
        self.train_batch_report_freq = max( int(self.t_params['train_batches']*self.t_params['reporting_freq']), 3)
        self.val_batch_report_freq = max( int(self.t_params['val_batches']*self.t_params['reporting_freq']), 3)
        #endregion

        # region ---- Restoring/Creating New Training Records and Restoring training progress
            #This training records keeps track of the losses on each epoch
        try:
            self.df_training_info = pd.read_csv( "checkpoints/{}/checkpoint_scores.csv".format(utility.model_name_mkr(m_params,t_params=self.t_params,htuning=m_params.get('htuning',False))), header=0, index_col=False) 
            self.df_training_info = self.df_training_info[['Epoch','Train_loss','Train_mse','Val_loss','Val_mse','Checkpoint_Path','Last_Trained_Batch']]
            self.start_epoch =  int(max([self.df_training_info['Epoch'][0]], default=0))
            last_batch = int( self.df_training_info.loc[self.df_training_info['Epoch']==self.start_epoch,'Last_Trained_Batch'].iloc[0] )
            if(last_batch in [-1, self.t_params['train_batches']] ):
                self.start_epoch = self.start_epoch + 1
                self.batches_to_skip = 0 
            else:
                self.batches_to_skip = last_batch
            print("Recovered training records")

        except FileNotFoundError as e:
            #If no file found, then make new training records file
            self.df_training_info = pd.DataFrame(columns=['Epoch','Train_loss','Train_mse','Val_loss','Val_mse','Checkpoint_Path','Last_Trained_Batch'] ) 
            self.batches_to_skip = 0
            self.start_epoch = 0
            print("Did not recover training records. Starting from scratch")
        # endregion

        # region ---- Defining Model / Optimizer / Losses / Metrics / Records / Checkpoints / Tensorboard 
        devices = tf.config.get_visible_devices() #tf.config.experimental.list_physical_devices('GPU')
        #gpus_names = [ device.name for device in devices if  device.device_type == "GPU" ]
        #self.strategy = tf.distribute.MirroredStrategy( devices=gpus_names ) #OneDeviceStrategy(device="/GPU:0") # 
        self.strategy = tf.distribute.MirroredStrategy( )
        assert self.t_params['batch_size'] % self.strategy.num_replicas_in_sync  == 0
        print("Number of Devices used in MirroredStrategy: {}".format(self.strategy.num_replicas_in_sync))
        with self.strategy.scope():   
            #Model
            self.strategy_gpu_count = self.strategy.num_replicas_in_sync    
            self.t_params['gpu_count'] = self.strategy.num_replicas_in_sync    
            self.model = models.model_loader( self.t_params, self.m_params )
            
            #Optimizer
            optimizer = tfa.optimizers.RectifiedAdam( **self.m_params['rec_adam_params'], total_steps=self.t_params['train_batches']*20) 

            self.optimizer = mixed_precision.LossScaleOptimizer( optimizer, loss_scale=tf.mixed_precision.experimental.DynamicLossScale() ) 
                    
            # These objects will aggregate losses and metrics across batches and epochs
            self.loss_agg_batch = tf.keras.metrics.Mean(name='loss_agg_batch' )
            self.loss_agg_epoch = tf.keras.metrics.Mean(name="loss_agg_epoch")

            self.mse_agg_epoch = tf.keras.metrics.Mean(name='mse_agg_epoch')
            
            self.loss_agg_val = tf.keras.metrics.Mean(name='loss_agg_val')
            self.mse_agg_val = tf.keras.metrics.Mean(name='mse_agg_val')
            
        #checkpoints  (For Epochs)
            #The CheckpointManagers can be called to serializae the weights within TRUNET
        checkpoint_path_epoch = "./checkpoints/{}/epoch".format(utility.model_name_mkr(m_params,t_params=self.t_params, htuning=m_params.get('htuning',False) ))
        os.makedirs(checkpoint_path_epoch,exist_ok=True)
        
        with self.strategy.scope():
            ckpt_epoch = tf.train.Checkpoint(model=self.model, optimizer=self.optimizer)
            self.ckpt_mngr_epoch = tf.train.CheckpointManager(ckpt_epoch, checkpoint_path_epoch, max_to_keep=self.t_params['checkpoints_to_keep'], keep_checkpoint_every_n_hours=None)    
        
            #restoring last checkpoint if it exists
            if self.ckpt_mngr_epoch.latest_checkpoint: 
                # compat: Initializing model and optimizer before restoring from checkpoint
                try:
                    ckpt_epoch.restore(self.ckpt_mngr_epoch.latest_checkpoint).assert_consumed()            
                except AssertionError as e:
                    ckpt_epoch.restore(self.ckpt_mngr_epoch.latest_checkpoint)              
                print (' Restoring model from best checkpoint')
            else:
                print (' Initializing model from scratch')
        
        #Tensorboard
        os.makedirs("log_tensboard/{}".format(utility.model_name_mkr(m_params, t_params=self.t_params, htuning=self.m_params.get('htuning',False) )), exist_ok=True ) 
        #self.writer = tf.summary.create_file_writer( "log_tensboard/{}".format(utility.model_name_mkr(m_params,t_params=self.t_params, htuning=self.m_params.get('htuning',False) ) ) )
        # endregion
        
        # region ---- Making Datasets
        
        #caching dataset to file post pre-processing steps have been completed 
        cache_suffix = utility.cache_suffix_mkr( m_params, self.t_params )
        os.makedirs( './Data/data_cache/', exist_ok=True  )

        _ds_train_val, _  = era5_eobs.load_data_era5eobs( self.t_params['train_batches'] + self.t_params['val_batches'] , self.t_params['start_date'], self.t_params['parallel_calls'] )

        ds_train = _ds_train_val.take(self.t_params['train_batches'] )
        ds_val = _ds_train_val.skip(self.t_params['train_batches'] ).take(self.t_params['val_batches'])

        #TODO: undo cache
        ds_train = ds_train.cache('Data/data_cache/train'+cache_suffix ) 
        ds_val = ds_val.cache('Data/data_cache/val'+cache_suffix )

        ds_train = ds_train.unbatch().shuffle( self.t_params['batch_size']*int(self.t_params['train_batches']/5), reshuffle_each_iteration=True).batch(self.t_params['batch_size']) #.repeat(self.t_params['epochs']-self.start_epoch)

        ds_train_val = ds_train.concatenate(ds_val)
        ds_train_val = ds_train_val.repeat(self.t_params.get('epochs',100)-self.start_epoch)
        self.ds_train_val = self.strategy.experimental_distribute_dataset(dataset=ds_train_val)
        self.iter_train_val = enumerate(self.ds_train_val)

        bc_ds_in_train = int( self.t_params['train_batches']/era5_eobs.loc_count  ) #batch_count
        bc_ds_in_val = int( self.t_params['val_batches']/era5_eobs.loc_count )

        self.reset_idxs_training = np.cumsum( [bc_ds_in_train]*era5_eobs.loc_count )
        self.reset_idxs_validation = np.cumsum( [bc_ds_in_val]*era5_eobs.loc_count )        
        # endregion

    def train_model(self):
        """During training we produce a prediction for a (n by n) square patch. 
            But we caculate losses on a central (h, w) region within the (n by n) patch
            This central region is defined by "bounds" below
        """        

        bounds = cl.central_region_bounds(self.m_params['region_grid_params']) #list [ lower_h_bound[0], upper_h_bound[0], lower_w_bound[1], upper_w_bound[1] ]
        
        #Training for n epochs
        #self.t_params['train_batches'] = self.t_params['train_batches'] if self.m_params['time_sequential'] else int(self.t_params['train_batches']*self.t_params['lookback_target'] )
        #self.t_params['val_batches'] = self.t_params['val_batches'] if self.m_params['time_sequential'] else int(self.t_params['val_batches']*self.t_params['lookback_target'] )

        for epoch in range(self.start_epoch, int(self.t_params['epochs']) ):
            
            #region resetting metrics, losses, records, timers
            self.loss_agg_batch.reset_states()
            self.loss_agg_epoch.reset_states()
            self.mse_agg_epoch.reset_states()
            
            
            self.loss_agg_val.reset_states()
            self.mse_agg_val.reset_states()
            
            self.df_training_info = self.df_training_info.append( { 'Epoch':epoch, 'Last_Trained_Batch':0 }, ignore_index=True )
            
            start_epoch_train = time.time()
            start_batch_group_time = time.time()
            batch=0           
            
            print("\n\nStarting EPOCH {}".format(epoch ))
            #endregion 
            
            # --- Training Loops
            for batch in range(self.batches_to_skip+1,self.t_params['train_batches'] +1):
                               
                # get next set of training datums
                idx, (feature, target, mask) = next(self.iter_train_val)
                

                gradients = self.distributed_train_step( feature, target, mask, bounds, 0.0 )
                #print(gradients)
                
                # reporting
                if( batch % self.train_batch_report_freq==0 or batch == self.t_params['train_batches']):
                    batch_group_time =  time.time() - start_batch_group_time
                    est_completion_time_seconds = (batch_group_time/self.t_params['reporting_freq']) * (1 - batch/self.t_params['train_batches'])
                    est_completion_time_mins = est_completion_time_seconds/60

                    print("\t\tBatch:{}/{}\tTrain Loss: {:.8f} \t Batch Time:{:.4f}\tEpoch mins left:{:.1f}".format(batch, self.t_params['train_batches'], self.loss_agg_batch.result(), batch_group_time, est_completion_time_mins ) )
                    
                    # resetting time and losses
                    start_batch_group_time = time.time()

                    # Updating record of the last batch to be operated on in training epoch
                    self.df_training_info.loc[ ( self.df_training_info['Epoch']==epoch) , ['Last_Trained_Batch'] ] = batch
                    self.df_training_info.to_csv( path_or_buf="checkpoints/{}/checkpoint_scores.csv".format(utility.model_name_mkr(self.m_params,t_params=self.t_params, htuning=m_params.get('htuning',False) )), header=True, index=False )


                li_losses = [self.loss_agg_batch.result()]
                li_names = ['train_loss_batch']
                step = batch + (epoch)*self.t_params['train_batches']
                #utility.tensorboard_record( self.writer.as_default(), li_losses, li_names, step, gradients, self.model.trainable_variables )
                #utility.tensorboard_record( self.writer.as_default(), li_losses, li_names, step, None, None )
                self.loss_agg_batch.reset_states()

                if batch in self.reset_idxs_training:
                    self.model.reset_states()
                    
            # --- Tensorboard record          
            li_losses = [self.loss_agg_epoch.result(), self.mse_agg_epoch.result()]
            li_names = ['train_loss_epoch','train_mse_epoch']
            #utility.tensorboard_record( self.writer.as_default(), li_losses, li_names, epoch)
            
            
            print("\tStarting Validation")
            start_batch_group_time = time.time()

            # --- Validation Loops
            for batch in range(1, self.t_params['val_batches']+1):
                
                # next datum
                idx, (feature, target, mask) = next(self.iter_train_val)
                
                bool_cmpltd = self.distributed_val_step(feature, target, mask, bounds)

                # Reporting for validation
                if batch % self.val_batch_report_freq == 0 or batch==self.t_params['val_batches'] :
                    batch_group_time            =  time.time() - start_batch_group_time
                    est_completion_time_seconds = (batch_group_time/self.t_params['reporting_freq']) * (1 -  batch/self.t_params['val_batches'])
                    est_completion_time_mins    = est_completion_time_seconds/60

                    print("\t\tCompleted Validation Batch:{}/{} \t Time:{:.4f} \tEst Time Left:{:.1f}".format( batch, self.t_params['val_batches'], batch_group_time, est_completion_time_mins))
                                                
                    start_batch_group_time = time.time()
                
                if batch in self.reset_idxs_validation:
                    self.model.reset_states()

            # region - End of Epoch Reporting and Early iteration Callback
            print("\tEpoch:{}\t Train Loss:{:.8f}\t Train MSE:{:.5f}\t Val Loss:{:.5f}\t Val MSE:{:.5f}\t  Time:{:.5f}".format(epoch, self.loss_agg_epoch.result(), self.mse_agg_epoch.result(),
                         
                        self.loss_agg_val.result(), self.mse_agg_val.result()  ,time.time()-start_epoch_train  ) )
                    
            #utility.tensorboard_record( self.writer.as_default(), [self.loss_agg_val.result(), self.mse_agg_val.result()], ['Validation Loss', 'Validation MSE' ], epoch  )                    
            self.df_training_info = utility.update_checkpoints_epoch(self.df_training_info, epoch, self.loss_agg_epoch, self.loss_agg_val, self.ckpt_mngr_epoch, self.t_params, 
                    self.m_params, self.mse_agg_epoch ,self.mse_agg_val,  self.t_params['objective'] )
            
            # Early Stop Callback 
            if epoch > ( max( self.df_training_info.loc[:, 'Epoch'], default=0 ) + self.t_params['early_stopping_period']) :
                print("Model Stopping Early at EPOCH {}".format(epoch))
                print(self.df_training_info)
                break
            # endregion
        
        print("Model Training Finished")

    def train_step(self, feature, target, mask, bounds, _init):
        
        if _init==1.0:
            if self.m_params['time_sequential'] == True:
                inp_shape = [self.t_params['batch_size'], self.t_params['lookback_feature']] + self.m_params['region_grid_params']['outer_box_dims'] + [len(self.t_params['vars_for_feature'])]
            else:
                inp_shape = [self.t_params['batch_size'] ] + self.m_params['region_grid_params']['outer_box_dims'] + [ int(self.t_params['lookback_feature']*len(self.t_params['vars_for_feature'])) ]
           
            _ = self.model( tf.zeros( inp_shape, dtype=tf.float16), self.t_params['trainable'] )    #( bs, tar_seq_len, h, w)

            gradients = [ tf.zeros_like(t_var, dtype=tf.float32 ) for t_var in self.model.trainable_variables  ]
            self.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))
            return [0]

        with tf.GradientTape(persistent=False) as tape:
                                   
            # non conditional continuous training
            if self.m_params['model_type_settings']['discrete_continuous'] == False:
                
                #making predictions
                preds = self.model( feature, self.t_params['trainable'] ) #( bs, tar_seq_len, h, w)
                preds = tf.squeeze( preds,axis=[-1] )

                
                preds   = cl.extract_central_region(preds, bounds)
                mask    = cl.extract_central_region(mask, bounds)
                target  = cl.extract_central_region(target, bounds)

                #Applying mask
                preds_masked = tf.boolean_mask( preds, mask )
                target_masked = tf.boolean_mask( target, mask ) 

                # reversing standardization
                preds_masked = utility.standardize_ati( preds_masked, self.t_params['normalization_shift']['rain'], self.t_params['normalization_scales']['rain'], reverse=True)

                # getting losses for records and/or optimizer
                metric_mse = cl.mse(target_masked, preds_masked) 
                loss_to_optimize = metric_mse

            # conditional continuous training        
            elif self.m_params['model_type_settings']['discrete_continuous'] == True:
                
                # Producing predictions - conditional rain value and prob of rain
                preds   = self.model( feature, self.t_params['trainable'] ) # ( bs, seq_len, h, w, 1)
                preds   = tf.squeeze(preds, axis=[-1])
                preds, probs = tf.unstack(preds, axis=0) 

                # extracting the central region of interest
            
                preds   = cl.extract_central_region(preds, bounds)
                probs   = cl.extract_central_region(probs, bounds)
                mask    = cl.extract_central_region(mask, bounds)
                target  = cl.extract_central_region(target, bounds)

                # applying mask to predicted values
                preds_masked    = tf.boolean_mask(preds, mask )
                probs_masked    = tf.boolean_mask(probs, mask ) 
                target_masked   = tf.boolean_mask(target, mask )
                
                # Reverising standardization of predictions 
                preds_masked    = utility.standardize_ati( preds_masked, self.t_params['normalization_shift']['rain'], 
                                                        self.t_params['normalization_scales']['rain'], reverse=True) 
                                                        
                # Getting true labels and predicted labels for whether or not it rained [ 1 if if did rain, 0 if it did not rain]
                labels_true = tf.where( target_masked > 0.0, 1.0, 0.0 )
                labels_pred = probs_masked 

                all_count = tf.size( labels_true, out_type=tf.int64 )                
                
                # region Calculating Losses and Metrics
                metric_mse  = cl.mse( target_masked, cl.cond_rain(preds_masked, probs_masked, threshold=0.5) )   
                    # To calculate metric_mse for CC model we assume that pred_rain=0 if pred_prob<=0.5 

                # CC Normal loss
                loss_to_optimize = 0
                loss_to_optimize += cl.mse( target_masked, preds_masked, all_count )    
                loss_to_optimize += tf.reduce_mean( tf.keras.backend.binary_crossentropy(labels_true, labels_pred, from_logits=False) )         
                # endregion

            loss_to_optimize_agg = tf.grad_pass_through( lambda x:  x/self.strategy_gpu_count )(loss_to_optimize)
            scaled_loss = self.optimizer.get_scaled_loss( loss_to_optimize_agg )
            scaled_gradients = tape.gradient( scaled_loss, self.model.trainable_variables )
            unscaled_gradients = self.optimizer.get_unscaled_gradients(scaled_gradients)
             
            gradients, _ = tf.clip_by_global_norm( unscaled_gradients, clip_norm=self.m_params['clip_norm'] ) #gradient clipping
            self.optimizer.apply_gradients( zip(gradients, self.model.trainable_variables))
        
        # Metrics (batchwise, epoch)  
        self.loss_agg_batch( loss_to_optimize )
        self.loss_agg_epoch( loss_to_optimize )
        self.mse_agg_epoch( metric_mse )    
        
        val = cl.rNmse(target_masked, preds_masked, 10.0)

        

        return gradients
                
    def val_step(self, feature, target, mask, bounds):
                    
        # Non CC distribution
        if self.m_params['model_type_settings']['discrete_continuous'] == False:
            
            # Get predictions
            preds = self.model(feature, False )
            preds = tf.squeeze(preds)

            # Extracting central region for evaluation
            preds   = cl.extract_central_region(preds, bounds)
            mask    = cl.extract_central_region(mask, bounds)
            target  = cl.extract_central_region(target, bounds)
            
            # Applying masks to predictions
            preds_masked = tf.boolean_mask( preds, mask )
            target_masked = tf.boolean_mask( target, mask )
            preds_masked = utility.standardize_ati( preds_masked, self.t_params['normalization_shift']['rain'], 
                                                    self.t_params['normalization_scales']['rain'], reverse=True)
            # Updating losses
            mse = cl.mse( target_masked , preds_masked ) 
            loss = mse                

        # CC distribution
        elif self.m_params['model_type_settings']['discrete_continuous'] == True:
            
            # Get predictions
            preds = self.model(feature, training=False )
            preds = tf.squeeze(preds,axis=[-1])
            preds, probs = tf.unstack(preds, axis=0)

            # Extracting central region for evaluation
        
            preds   = cl.extract_central_region(preds, bounds)
            probs   = cl.extract_central_region(probs, bounds)
            mask    = cl.extract_central_region(mask,  bounds)
            target  = cl.extract_central_region(target,bounds)

            # Applying masks to predictions
            preds_masked    = tf.boolean_mask( preds, mask )
            probs_masked    = tf.boolean_mask( probs, mask)
            target_masked   = tf.boolean_mask( target, mask )
            preds_masked    = utility.standardize_ati( preds_masked, self.t_params['normalization_shift']['rain'], 
                                                    self.t_params['normalization_scales']['rain'], reverse=True)

            # Getting classification labels for whether or not it rained
            
            labels_true = tf.where( target_masked > 0.0, 1.0, 0.0 )
            labels_pred = probs_masked 

            all_count = tf.size( labels_true, out_type=tf.int64 )

            # calculating seperate mse for reporting
                # This mse metric assumes that if probability of rain is predicted below 0.5, the rain value is 0
            mse = cl.mse( target_masked, cl.cond_rain( preds_masked, probs_masked, threshold=0.5) )

            # Calculating cross entropy loss                         
            loss = tf.reduce_mean(  tf.keras.backend.binary_crossentropy( labels_true, labels_pred, from_logits=False) )

            # Calculating conditinal continuous loss
            loss    += cl.mse( target_masked, preds_masked, all_count )

        self.loss_agg_val(loss)
        self.mse_agg_val(mse)
                    
        return True
    
    @tf.function
    def distributed_train_step(self, feature, target, mask, bounds, _init):
        gradients = self.strategy.run( self.train_step, args=(feature, target, mask, bounds, _init) )
        return gradients
    
    @tf.function
    def distributed_val_step(self, feature, target, mask, bounds):
        bool_completed = self.strategy.run( self.val_step, args=(feature, target, mask, bounds))
        return bool_completed


if __name__ == "__main__":
    s_dir = utility.get_script_directory(sys.argv[0])
    args_dict = utility.parse_arguments(s_dir)

    # get training and model params
    t_params, m_params = utility.load_params(args_dict)
    
    # Initialize and  train model
    weather_model = WeatherModel(t_params, m_params)
    weather_model.initialize_scheme_era5Eobs()
    weather_model.train_model()
    

Returns True if the `other` DType will be converted to this DType.
     Monkey patch: incompatibility issues between tfa.optimizers and mixed precision training
     The conversion rules are as follows:
     ```python
     DType(T)       .is_compatible_with(DType(T))        == True
     ```
     Args:
         other: A `DType` (or object that may be converted to a `DType`).
     Returns:
         True if a Tensor of the `other` `DType` will be implicitly converted to
         this `DType`. Handles the Training of the Deep Learning Weather model
 
         Example of how to use:
         WeatherModel = WeatherModel( t_params, m_params)
         WeatherModel.initialize_scheme_era5Eobs()    #Initializes datasets for ERA5 and Eobs
         WeatherModel.train_model()                   #Trains and saves model Train the TRU_NET Model Initialization scheme for the ERA5 and E-OBS datasets.
             This method creates the datasets # region ---- Parameters  related to training length and training reporting frequency # hparameters files calculates train_batches assuing we are only evaluating one location, # therefore we must adjust got multiple locations (loc_count) # The fequency at which we report during training and validation i.e every 10% of minibatches report training loss and training mse #endregion # region ---- Restoring/Creating New Training Records and Restoring training progress #This training records keeps track of the losses on each epoch #If no file found, then make new training records file # endregion # region ---- Defining Model / Optimizer / Losses / Metrics / Records / Checkpoints / Tensorboard #tf.config.experimental.list_physical_devices('GPU') #gpus_names = [ device.name for device in devices if  device.device_type == "GPU" ] #self.strategy = tf.distribute.MirroredStrategy( devices=gpus_names ) #OneDeviceStrategy(device="/GPU:0") # #Model #Optimizer # These objects will aggregate losses and metrics across batches and epochs #checkpoints  (For Epochs) #The CheckpointManagers can be called to serializae the weights within TRUNET #restoring last checkpoint if it exists # compat: Initializing model and optimizer before restoring from checkpoint #Tensorboard #self.writer = tf.summary.create_file_writer( "log_tensboard/{}".format(utility.model_name_mkr(m_params,t_params=self.t_params, htuning=self.m_params.get('htuning',False) ) ) ) # endregion # region ---- Making Datasets #caching dataset to file post pre-processing steps have been completed #TODO: undo cache #.repeat(self.t_params['epochs']-self.start_epoch) #batch_count # endregion During training we produce a prediction for a (n by n) square patch. 
             But we caculate losses on a central (h, w) region within the (n by n) patch
             This central region is defined by "bounds" below #list [ lower_h_bound[0], upper_h_bound[0], lower_w_bound[1], upper_w_bound[1] ] #Training for n epochs #self.t_params['train_batches'] = self.t_params['train_batches'] if self.m_params['time_sequential'] else int(self.t_params['train_batches']*self.t_params['lookback_target'] ) #self.t_params['val_batches'] = self.t_params['val_batches'] if self.m_params['time_sequential'] else int(self.t_params['val_batches']*self.t_params['lookback_target'] ) #region resetting metrics, losses, records, timers #endregion # --- Training Loops # get next set of training datums #print(gradients) # reporting # resetting time and losses # Updating record of the last batch to be operated on in training epoch #utility.tensorboard_record( self.writer.as_default(), li_losses, li_names, step, gradients, self.model.trainable_variables ) #utility.tensorboard_record( self.writer.as_default(), li_losses, li_names, step, None, None ) # --- Tensorboard record #utility.tensorboard_record( self.writer.as_default(), li_losses, li_names, epoch) # --- Validation Loops # next datum # Reporting for validation # region - End of Epoch Reporting and Early iteration Callback #utility.tensorboard_record( self.writer.as_default(), [self.loss_agg_val.result(), self.mse_agg_val.result()], ['Validation Loss', 'Validation MSE' ], epoch  ) # Early Stop Callback # endregion #( bs, tar_seq_len, h, w) # non conditional continuous training #making predictions #( bs, tar_seq_len, h, w) #Applying mask # reversing standardization # getting losses for records and/or optimizer # conditional continuous training # Producing predictions - conditional rain value and prob of rain # ( bs, seq_len, h, w, 1) # extracting the central region of interest # applying mask to predicted values # Reverising standardization of predictions # Getting true labels and predicted labels for whether or not it rained [ 1 if if did rain, 0 if it did not rain] # region Calculating Losses and Metrics # To calculate metric_mse for CC model we assume that pred_rain=0 if pred_prob<=0.5 # CC Normal loss # endregion #gradient clipping # Metrics (batchwise, epoch) # Non CC distribution # Get predictions # Extracting central region for evaluation # Applying masks to predictions # Updating losses # CC distribution # Get predictions # Extracting central region for evaluation # Applying masks to predictions # Getting classification labels for whether or not it rained # calculating seperate mse for reporting # This mse metric assumes that if probability of rain is predicted below 0.5, the rain value is 0 # Calculating cross entropy loss # Calculating conditinal continuous loss # get training and model params # Initialize and  train model