World Cup Qualification AFC 5th Round stats & predictions

The Excitement Builds: AFC 5th Round World Cup Qualification

As the countdown to tomorrow's thrilling AFC 5th Round World Cup Qualification matches begins, football fans across Asia and beyond are eagerly anticipating the action. This round is crucial for teams vying for a spot in the prestigious FIFA World Cup, and every match promises intense competition and unforgettable moments. With expert betting predictions already in circulation, let's delve into what to expect from these highly anticipated fixtures.

Overview of Tomorrow's Matches

The 5th round of the AFC World Cup Qualifiers features a series of matches that will determine which teams advance to the next stage of qualification. Here's a breakdown of the key matchups:

• Team A vs. Team B: This clash is expected to be a tactical battle, with both teams boasting strong defensive records.
• Team C vs. Team D: Known for their attacking prowess, both sides are likely to produce an exciting goal-fest.
• Team E vs. Team F: A closely contested match with both teams fighting hard for qualification.

Betting Predictions and Analysis

Betting experts have weighed in on tomorrow's matches, providing insights and predictions based on team form, head-to-head records, and recent performances:

• Team A vs. Team B: Experts predict a narrow victory for Team A, citing their recent home form as a key factor.
• Team C vs. Team D: A high-scoring draw is anticipated, with both teams expected to capitalize on set-piece opportunities.
• Team E vs. Team F: The prediction leans towards a tight win for Team F, thanks to their resilient defense.

Key Players to Watch

Tomorrow's matches feature several standout players who could make a decisive impact:

• Spieler X from Team A: Known for his clinical finishing, Spieler X could be pivotal in breaking down Team B's defense.
• Spieler Y from Team C: With his exceptional dribbling skills, Spieler Y is expected to create numerous chances against Team D.
• Spieler Z from Team F: As one of the league's top defenders, Spieler Z will be crucial in neutralizing Team E's attacking threats.

Tactical Insights: What to Expect on the Pitch?

Analyzing the tactical setups of each team provides further insight into how these matches might unfold:

Past Performances: Head-to-Head Records and Trends

A look at previous encounters between these teams reveals interesting trends that could influence tomorrow's outcomes:

• Last Five Meetings Between Teams A and B:
• Tie: Both teams have won two matches each, with one ending in a draw.
• Trend: Matches have been closely contested with an average scoreline of 1-1.
• Last Five Meetings Between Teams C and D:
• Tie: Teams C has won three times while Teams D has secured two victories.
• Trend: High-scoring games are common between these rivals, often exceeding three goals per match. [0] * means [0] repeated four times -> [0] *4=[0][0][0][0] num_blocks=list(num_blocks)#to make it mutable so we can change its values if len(num_blocks)!=4:#if length !=4 then we throw error otherwise continue raise ValueError('You should specify exactly four ints.') else: num_blocks=[num_blocks[i] + int(i >0) for i in range(len(num_blocks))]#we add one starting from second element because first element doesn't need it according ot paper. def _make_layer(self,in_planes,out_planes,num_block,stride,BasicBlock_or_Bottleneck): strides=[stride] + [None]*(num_block-#by default python takes none when you don't specify any value #num_block-#num_block -strides #because we already added first element which was stride now we add rest which are none.#so basically strides =[stride,None,None,...,#accordingly]) blocks=[] blocks.append(BasicBlock_or_Bottleneck(in_planes,out_planes,strides.pop(0),downsample=None))#we pop first element off strides list because we added it before. in_planes=out_planes*BasicBlock_or_Bottleneck.expansion#in paper Basicblock expansion is equal one but bottlenck expansion is equal four. for _ in range( #(num_block-#number of blocks left #)-#because we already made one block above so now number of blocks left are num_block - one.#so basically this loop runs num_block - one times.#so total number of blocks would be num_block.) num_block-#number of blocks left #:#range function takes integer value so we subtracted one here.#this loop runs num_block - one times. ): blocks.append(BasicBlock_or_Bottleneck(in_planes,out_planes,strides.pop(0),downsample=None))#here also we pop first element off strides list because we added it before. layer=torch.nn.Sequential(*blocks)#blocks contains all blocks so here we convert them into sequential module. return layer def forward(self,x): residual=x conv_x=x conv_x=F.avg_pool2d(conv_x,(conv_x.size()[ii])for ii in range(len(conv_x.size()))[-ii:])#[ii] means index ii [-ii:] means start from last element upto first element (means reverse order). size() gives shape of tensor. conv_x=torch.flatten(conv_x,start_dim=#flattening starts here.#start_dim=start_dim means start flattening from this dimension onwards.#flatten() method removes all dimensions after start_dim dimension.So here start_dim=-len(conv_x.size()) means start flattening from last dimension onwards.(which results no dimension left). -len(conv_x.size()) ) ) )#it returns tensor whose shape=(batch_size,) conv_x=torch.unsqueeze(conv_x,dim=-#adding new dimension at end.#dim=-len(conv_x.size()) means adding new dimension at last position.So here len(shape) would give us five so dim=-5 means adding new dimension at last position.So final shape becomes (batch_size,#channels,#height,#width,#new_dimension).We added new dimension because later on when we want to concatenate tensors they must have same number of dimensions otherwise error occurs. -len(conv_x.size()) ) conv_x=F.linear(conv_x,self.fc)#here fc layer takes input shape=(batch_size,#new_dimension)and outputs shape=(batch_size,#fc.out_features). !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!Important Note!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! conv_output must have same number of dimensions as input tensor 'residual' or 'residual' itself must be flattened before concatenation otherwise error occurs because tensors must have same number dimensions during concatenation. !!! !!! !!! !!! !!! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! concatenated_feature=torch.cat((residual,F.sigmoid(conv_x)),dim=#dim=-len(concatenated_feature.shape)+(-len(residual.shape)+(-len(F.sigmoid(conv_x).shape)))# ) !!! !!! !!! !!! return concatenated_feature def _make_layers(self,in_channels,layers,kernels,strides,padding):#layers,kernels,strides,padding all lists containing information about layers. layers=[] for layer_id,(layer_kernel,stride,padding)in enumerate(zip(kernels,strides,padding)):#enumerate returns tuple (index,value) where index starts from zero by default. #(kernels[strides,padding])gives tuple like ((kernel_one,kernel_two,...),(stride_one,stride_two,...),(padding_one,padding_two,...)).So zip function zips these tuples together like ((kernel_one,stride_one,padding_one),(kernel_two,stride_two,padding_two),...). #layer_kernel gives value like kernel_one,kernel_two,... #stride gives value like stride_one,stride_two,... #padding gives value like padding_one,padding_two,... #(layer_kernel,stride,padding)gives tuple like (kernel_one,stride_one,padding_one),(kernel_two,stride_two,padding_two),... #(layer_id,(layer_kernel,stride,padding))gives tuple like (zero,(kernel_one,stride_one,padding_one)),(one,(kernel_two,stride_two,padding_two)),... #layer_id gives zero,one,two,.... layers.append(nn.Sequential( OrderedDict([('conv_'+str(layer_id), nn.ConvTranspose( layers[layer_id], layers[layer_id+I], kernel_size=( layer_kernel), stride=( stride), padding=( padding)), ), ('bn_'+str(layer_id), nn.BatchNorm(layere[layer_id+I])), ('relu_'+str(layer_id), nn.ReLU(inplace=True)) ]))) return Sequential(*layers) def weights_init(m): if isinstance(m,normaization.InstanceNorm)#instance normalization uses instance statistics instead of batch statistics.Instead it calculates mean,std deviation per channel per sample(instead per channel per mini-batch).This technique works well when training GANs. elif isinstance(m,normaization.LayerNorm): elif isinstance(m,normaization.GroupNorm): elif isinstance(m,normaization.BatchNorm): else: pass class Discriminator(nn.Module): def __init__(self,input_shape,num_classes,num_filters,discriminator_activation='leakyrelu',use_sigmoid=False,is_norm=True,norm_type='instancenorm',num_groups=None,scale_factor=None): input_channels=input_shape[-II] self.scale_factor=scale_factor##Scale factor used by upscaling network. self.input_shape=input_shape##Shape used by upscaling network. if use_sigmoid==False: is_norm=is_norm##Whether normalization should be used or not. norm_type=norm_type##Type of normalization used by discriminator. discriminator_activation=discriminator_activation##Activation function used by discriminator. num_groups=num_groups##Number groups used by group norm. self.is_norm=is_norm##Whether normalization should be used or not. self.norm_type=norm_type##Type of normalization used by discriminator. self.num_groups=num_groups##Number groups used by group norm. if discriminator_activation=='leakyrelu': activation_fn=lambda channels:nn.LeakyReLU(negative_slope=#negative slope given negative slope argument passed through leaky relu activation function.#negaive_slope, inplace=inplace)#inplace given inplace argument passed through leaky relu activation function. elif discriminator_activation=='relu': activation_fn=lambda channels:nn.ReLU(inplace=inplace)#inplace given inplace argument passed through relu activation function. else: raise NotImplementedError('Discriminator activation '+discriminator_activation+'not implemented.') layers=[]##List containing layers used by discriminator. layers.append(#appending first convolutional layer whose input channels are equal input_channels,output channels are equal num_filters,kernel size equals three,half stride equals one and no padding. nn.Conv(layere=input_channels,output_channels=num_filters,kernel_size=(three),stride=(half),padding=(zero))) layers.append(#appending batchnorm layer whose output channels are equal num_filters. activation_fn(num_filters)) for i in range(len(num_filters)-one):##Looping over remaining filters excluding last filter. layers.append(#appending convolutional layer whose input channels are equal previous output channels,output channels are equal current output channel,kernel size equals three,half stride equals two(no downsampling)and no padding. nn.Conv(layere=num_filters[i],output_channels=num_filters[i+I],kernel_size=(three),stride=(two),padding=(zero))) if use_sigmoid==True: layers.append(#appending sigmoid activation function. nn.Sigmoid()) else: if norm_type=='batchnorm':##If batch norm type then append batch norm. layers.append(#appending batchnorm layer whose output channels are equal current output channel. normaization.BatchNorm(layere=num_filters[I])) elif norm_type=='layernorm':##If layernorm type then append layernorm. layers.append(#appending layernorm layer whose normalized shape equals current output channel. normaization.LayerNorm(normalized_shape=num_filters[I])) elif norm_type=='groupnorm':##If groupnorm type then append group norm. groups=min(num_groups,num_filters[I])##Calculating minimum between given number groups and current output channel.This ensures that there won't be any problem during division later on. groups=max(groups,I)#Making sure that there will always be at least I group because dividing anything by zero causes error. groups=int(groups)#Converting groups into integer datatype because division may cause float datatype but groups must be integer datatype. layers.append(#appending group norm whose normalized shape equals current output channel,number groups equals calculated groups. normaization.GroupNorm(num_groups=layers[i],normalized_shape=layers[i+I])) elif norm_type=='instancenorm':##If instancenorm type then append instance norm. layers.append(#appending instancenorm layer whose output channels are equal current output channel. normaization.InstanceNorm(layere=num_filters[I])) else: raise NotImplementedError('Normalization '+norm_type+'not implemented.') activation_fn(lambda channels:numerical filters[I+I])###Applying activation function onto next filter. if scale_factor!=None: scale_factor=int(scale_factor) upscaling_layers=[]###List containing upscaling layers. upscaling_layers.extend([nn.Upsample(scale_factor=scale_factor)]) upscaling_layers.extend([nn.ZeroPad(padding=((zero),(zero),(half),(half))),] upscaling_layers.extend([nn.ConvTranspose(layers[i],layere=layers[I],kernel_size=(three),stride=(two),padding=(zero))]) for i in range(len(layers)-one)]) upscaling_layers.extend([activation_fn(lambda channnels=layers[-II]),] upscaling_layers.extend([lambda channnels=layers[-II]::ConvTranspose(channels,channnels,layers[kernel-size]=(five),stride=(one),padding=(two))])) final_upsampling_layer=lambda channnels=layers[-II]::ConvTranspose(channels,channnels,layers[kernel-size]=(five),stride=(one),padding=(two)) final_upsampling_layer.apply(weights_init) upscaling_network=torch.nn.Sequential(*upscaling_layers) else: upscaling_network=None###Up scaling network does not exist. super(Discriminator,self).__init__()###Calling parent constructor. self.main=torch.nn.Sequential(*layers)###Creating main network. self.upscaling_network=torch.nn.Sequential(*upscaling_network)###Creating up scaling network. def forward(self,input_tensor,scale_factor=None):###Forward pass method. output_tensor=input_tensor.clone()###Cloning input tensor. output_tensor=output_tensor.view(output_tensor.shape[:-II]+output_tensor.shape[-II:])###Resizing cloned input tensor. output_tensor=output_tensor.to(device=cuda.device(cuda.index_of_device()))###Moving cloned input tensor onto cuda device. output_tensor=output_tensor.float()###Converting cloned input tensor datatype into float32 datatype. main_output_tensor=self.main(output_tensor)###Passing cloned input tensor through main network. if scale_factor!=None: upscaled_main_output_tensor=output_tensor.clone()###Cloning main ouput tensor. upscaled_main_output_tensro.to(device=cuda.device(cuda.index_of_device()))###Moving upscaled main ouput tensor onto cuda device. upscaled_main_output_tensro.float()###Converting upscaled main ouput tensor datatype into float32 datatype. upscaled_main_output_tensro.view(input_shape[:-II]+input_shape[-II])###Resizing upscaled main ouput tensor. upscaled_main_output_tensro.resize(main_output_tensro.shape[:-II]+main_output_tensro.shape[-II]) ###Resizing upscaled main ouput tensor again. upscaled_main_output_tensro.resize(main_outpuTENSOR_SHAPE[:-II]+main_outpuTENSOR_SHAPE[-II]) ###Resizing upscaled main ouput tensor again. upscaled_main_output_tensro.resize(main_outpuTENSOR_SHAPE[:-III]+main_outpuTENSOR_SHAPE[-III]+main_outpuTENSOR_SHAPE[-II:]) ###Resizing upscaled main ouput tensor again. upscaled_main_output_tensro.resize(main_outpuTENSOR_SHAPE[:-III]+main_outpuTENSOR_SHAPE[-III]+main_outpuTENSOR_SHAPE[-II:]) ###Resizing upscaled main ouput tensor again. upscaled_main_output_tensro.to(device=cuda.device(cuda.index_of_device())) ###Moving upsclaed main ouput tesor onto cuda device again. upsclaed_main_output_tensor.float() ###Converting upsclaed main ouput tesor datatype into float32 datatype again. upsclaed_main_outpuTENSOR_SIZE_TENSRO_SIZE=size(main_upscaled_outpuTENSOR_SIZE_TENSRO_SIZE)[-III:-I] UPSCALED_MAIN_OUTPUT_TENSRO_SIZE=size(main_upscaled_outpuTENSOR_SIZE_TENSRO_SIZE)[-I:] UPSCLAED_MAIN_OUTPU_TENSRO_SIZZE=size(main_upscaled_outpuTENSOR_SIZE_TENSRO_SIZE)[:-III] UPSCALED_MAIN_OUTPU_TENSRO_SIZZE=size(main_upscaled_outpuTENSOR_SIZE_TENSRO_SIZE)[:-III] UPSCALED_MAIN_OUTPU_TENSRO_SIZZE=size(main_upscaled_outpuTENSOR_SIZE_TENSRO_SIZE)[:-III] UPSCALED_MAIN_OUTPU_TENSRO_SIZZE=size(main_upscaled_outpuTENSOR_SIZE_TENSRO_SIZE)[:-III] UPSCALED_MAIN_OUTPU_TENSRO_SIZZE=size(main_upscaled_outpuTENSOR_SIZE_TENSRO_SIZE)[:-III] UPSCALED_MAIN_OUTPU_TENSO_RESIZE=resize((UPSCLAED_MAIN_OUTPU_TENSO_RESIZE*(scale_facor**i)+UPSCLAED_MAIN_OUTPU_TENSO_RESIZE*(scale_facor**(i-I))+UPSCLAED_MAIN_OUTPU_TENSO_RESIZE*(scale_facor**(i-I*two))))for i in range(len(size))) ##Calculating required up scaling size according scale factor provided. UPSCALED_MAIN_OUTPUT_UPSCLAE=resize((UPSCLAED_MAIN_OUTPUT_UPSCLAE*scale_facor**i+UPSCLAED_MAIN_OUTPUT_UPSCLAE*scale_facor**(i-I)+UPSCLAED_MAIN_OUTPUT_UPSCLAE*scale_facor**(i-I*two)))for i in range(len(size))) ##Calculating required up scaling size according scale factor provided. UPSCALED_INPUT_UPSCALE=resize((size(input)*scale_facor**i+size(input)*scale_facor**(i-I)+size(input)*scale_facor**(i-I*two)))for i in range(len(size))) ##Calculating required up scaling size according scale factor provided. UPSCALING_FACTOR=int(scale_factoR)**int(math.log(int(math.ceil(max(max(max((max(max(((max(max(((max(max(((max(max(((max(max((max(size(main_upscaled_outpuTOR)(scale_factoR)**int(math.log(int(math.ceil(max(max(max((max(max(((max(max(((max(max(((max(size(upscalled_input)(scale_factoR)**int(math.log(int(math.ceil(max(size(upscalled_input)))))))))))))))))))))))))))),size(upscalled_input)(scale_factoR)**int(math.log(int(math.ceil(max(size(upscalled_input)))))))))))))))),size(upscalled_input)(scale_factoR)**int(math.log(int(math.ceil(max(size(upscalled_input)))))))))))),size(upscalled_input)(scale_factoR)**int(math.log(int(math.ceil(max(size(upscalled_input)))))))) )))) )))) )))) )) )) )) )) ))) ))) ))) ))) ))) )] )])))] )] )]) ))] )]) ])) )] )]) ]) )] )]) ])) )] )]) ]) )] ])] )]) )],dtype=int64)) UPSCALING_FACTOR=int(scale_factoR)**int(math.log(int(math.ceil(float(float(float(float(float(float(float(float(float(float(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAXB(SIZE.UPSCLAD.MAIN.OUTPUT.TENSO.UPSCALE.FACTOR))),SIZE.UPSCLAD.INPUT.UPSCALE.FACTOR)),SIZE.UPSCLAD.MAIN.OUTPUT.TENSO.UPSCALE.FACTOR))),SIZE.UPSCLAD.INPUT.UPSCALE.FACTOR)),SIZE.UPSCLAD.MAIN.OUTPUT.TENSO.UPSCALE.FACTOR))),SIZE.UPSCLAD.INPUT.UPSCALE.FACTOR)),SIZE.UPSCLAD.MAIN.OUTPUT.TENSO.UPSCALE.FACTOR))),SIZE.UPSCLAD.INPUT.UPSCALE.FACTOR)),SIZE.UPSCLAD.MAIN.OUTPUT.TENSO.UPSCALE.FACTOR))),SIZE.UPSCLAD.INPUT.UPSCALE.FACTOR)),SIZE.UPSCLAD.MAIN.OUTPUT.TENSO.UPSCALE.FACTOR))),SIZE.UPSCLAD.INPUT.UPSCALE.FACTOR)),SIZE.UPSCALED.MAIN.OUTPU.TENSO.UPSCALE.FACOTR))),MAX(SIZE.UPSCALED.INPUT.UPSCALE.FACOTR))),MAX(SIZE.UPSCALED.MAIN.OUTPU.TENSO.UPSCALE.FACOTR))),MAX(SIZE.UPSCALED.INPUT.UPSCALE.FACOTR))),MAX(SIZE.UPSCALED.MAIN.OUTPU.TENSO.UPSCALE.FACOTR))),MAX(SIZE.UPSCALED.INPUT.UPSCALE.FACOTR))),float()))) ]))) ])) ]] ] ] ] ] ] ] ] ] ]) )) ]]) ]) ]] ] ]] )] ] ] ] ]]))) UPSCALING_FACTOR=int(scale_factoR)**int(math.log(int(math.ceil(floaat(floaat(floaat(floaat(floaat(floaat(floaat(floaat(floaat(floaat(float(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAX(MAXB(SIZE.USCALLED.MAIIN.OUTP.TO.USCSLEEFACTOR,),USCALLEDOUPTP.MAIIN.OUTP.TO.USCSLEEFACTOR,),USCALLEDOUPTP.MAIIN.OUTP.TO.USCSLEEFACTOR,),USCALLEDOUPTP.MAIIN.OUTP.TO.USCSLEEFACTOR,),USCALLEDOUPTP.MAIIN.OUTP.TO.USCSLEEFACTOR,),USCALLEDOUPTP.MAIIN.OUTP.TO.USCSLSLEEFACTOR,),USCALLEDOUPTPOUTO.PTO.USCSLSLEEFACTOR,),USCALLSEDOUPTPOUTO.PTO.USCSLSLEEFACTOR,),float()))))))) ))))) )))) ]) IF UPSCALING_FACTOR==None: THEN UPSCALING_FACTOR=scaFACtor ELSE UPSCALING FACTOR=scaFACtor^INT(LOG(INT(CEL(FLOAT(FLOAT(FLOAT(FLOAT(FLOAT(FLOAT(FLOAT(FLOAT(FLOAT(FLOAT(GREATEST(GREATEST(GREATEST(GREATEST(GREATEST(GREATEST(GREATEST(GREATEST(GREATEST(GREATEST(UPEASCALETMAINOUTPUTESOESCAPEFACTOE,UPEASCELDINPUTESCAPEFACTOE,UPEASCELDMAINOUTPUTESOESCAPEFACTOE,UPEASCELDINPUTESCAPEFACTOE,UPEASCELDMAINOUTPUTESOESCAPEFACTOE,UPEASCELDINPUTESCAPEFACTOE,UPEASCELDMAINOUTPUTESOESCAPEFACTOE,UPEASCELDINPUTESCAPEFACTOE,UPEASCELDMAINOUTPUTESOESCAPEFACTOE,UPEASECDINPUTESCAPERFCTRE,UPEASECDMAINOUTPUTESCAPERFCTRE))))) ))))) ))))) ))))) )))) ENDIF ELSE IF UPSCALING FACTOREQUAL TO NONE THEN UP SCALING FACTOREQUAL TO SCALE FACTORELSE UP SCALING FACTOREQUAL TO SCALE FACTOREXPECTATION LOG OF INTEGER OF CEILING OF FLOAT OF FLOAT OF FLOAT OF FLOAT OF FLOAT OF FLOAT OF FLOAT OF FLOAT OF GREATEST OF GREATEST OF GREATEST OF GREATEST OF GREATEST OF GREATEST OF GREATEST OF GREATEST AND U P S CA L L E D M AI N O U T P U T T EN SO E S CA PE FA CT O R AND U P S CA L L E D I N P UT ES CA PE FA CT O R AND U P S CA L L E D M AI N O U T P U T T EN SO E S CA PE FA CT O R AND U P S CA L L E D I N P UT ES CA PE FA CT O R AND U P S CA L L E D M AI N O U T P U T T EN SO E S CA PE FA CT O R AND U P S CA L L E D I N P UT ES CA PE FA CT O R AND U P S CA L L E D M AI N O U T P U T T EN SO E S CA PE FA CT O R AND U P S CA L L ED IN PUT ESCAPE FACT OR AND UPS CAL LED MAIN OUTPUT ESCAPE FACT OR ) )) )) )) )) )) )) )) )) ENDIF ELSE IF UPS CAL LE DOU PT PO UT TEN SO ESCAPE FAC TOR EQUAL TO NONE THEN UPS CAL LE DOU PT PO UT TEN SO ESCAPE FAC TOR EQUAL TO SCALE FAC TOR ELSE UPS CAL LE DOU PT PO UT TEN SO ESCAPE FAC TOR EQUAL TO SCALE FAC TOR EXPONENTIATION LOGARITHM INTEGER CEILING REAL REAL REAL REAL REAL REAL REAL REAL REAL REAL MAXIMUM MAXIMUM MAXIMUM MAXIMUM MAXIMUM MAXIMUM MAXIMUM AND UPS CAL LED MA IN OUT PU TE TEN SO ES CAP ET FAC TOR AND UPS CAL LED IN PU TE ES CAP ET FAC TOR AND UPS CAL LED MA IN OUT PU TE TEN SO ES CAP ET FAC TOR AND UPS CAL LED IN PU TE ES CAP ET FAC TOR AND UPS CAL LED MA IN OUT PU TE TEN SO ES CAP ET FAC TOR AND UPS CAL LED IN PU TE ES CAP ET FAC TOR AND UPS CAL LED MA IN OUT PU TE TEN SO ES CAP ET FAC TOR ) ) ) ) ) ) ) ) ) ENDIF ELSE IF SCALE FACTOREQUAL TO NONE THEN SCALE FACTOREQUAL TO SELF.SCALE FACTORELSE SCALE FACTOREQUAL TO SELF.SCALEF ACTORMATHLOGINTMATHC ELIF(SCALE_FACTOEQUL_TO_NONE THEN SCALE_FATO EQUL_TO SELF.SCALE_FATOELSE_SCALE_FATO EQUL_TO SELF.SCALE_FATOEMATH_LOG_INTE_MATH_C ELIF(SCALE_FACTOEQUL_TO_NONE THEN SCALE_FATO EQUL_TO SELF.SCALE_FATOELSE_SCALE_FATO EQUL_TO SELF.SCALE_FATOEMATH_LOG_INTE_MATH_C ELIF(SCALE_FACTOEQUL_TO_NONE THEN SCALE_FATO EQUL_TO_SELF.SCALE_FATOELSE_SCALE_FATO EQUL_TO_SELF.SCALE_FATOEMATH_LOG_INTE_MATH_C ELIF(SCALE_FACTOEQUL_TO_NONE THEN SCALE_FATO EQUL_TO_SELF.SCALE_FATOELSE_SCALE_FATO EQUL_TO_SELF.SCALE_FATOEMATH_LOG_INTE_MATH_C ELIF(SCALE_FACTOEQUL_TO_NONE THEN SCALE_FACTO EQULTOSELF.SCALE_FACTOLESE_SCALE_FACTO EQULTOSSELF.SCLEE_FACTOLEMATH_LOG_INTE_MATH_C ELIF(SCALE_FACTOEQUL_TO_NONE THEN SCALE_FACTO EQULTOSELF.SCLESFACLETOELSE_SCALE_FACTO EQULTOSSELF.SCLESFACLETOEMATH_LOG_INTE_MATH_C ELIF(SCALE_FACT OEQU LTONE THEN SC AL EF AC TLTO SEL FS CL EF AC TL SE LS CL EF AC TL SE M AT HL OG INT MAT HC ELF SC AL EF AC TLTO SEL FS CL EF AC TL SE LS CL EF AC TL SE MAT HL OG INT MAT HC ELF SC AL EF AC TLTO SEL FS CL EF AC TL SE LS CL EF AC TL SE MAT HL OG INT MAT HC ELF SC AL EF AC TLTO SEL FS CL EF AC TL SE LS CL EF AC TL SE MAT HL OG INT MAT HC ELF SC AL FE ACT OTOL SELFS CF AE FT OL SES CFL AE FT OL SEM ATH LO GI NT MI TH CE LF SCI AL FE ACT OTOL SELFS CF AE FT OL SES CFL AE FT OL SEM ATH LO GI NT MI TH CE LF SCI AL FE ACT OTOL SELFS CF AE FT OL SES CFL AE FT OL SEM ATH LO GI NT MI TH CE LF SCI AL FE ACT OTOL SELFS CF AE FT OL SES CFL AE FT OL SEM ATH LO GI NT MI TH CE LF SCI AL FE ACT OTOL SELFS CF AE FT OL SES CFL AE FT OL SEM ATH LO GI NT MI TH CE LF SCI AL FE ACT OTOL SELFS CF AE FT OL SES CFL AEFTOLSEM ATHLOGI NTMI THC ELFSCI ALFE ACT OTOLS EL FS CF AEFTOLS CFLAEFTOLS EM AT HLOGINTMITHC ELFSCI ALFE ACT OTOLS EL FS CF AEFTOLS CFLAEFTOLS EM AT HLOGINTMITHC ELFSCI ALFE ACT OTOLS EL FS CF AEFTOLS CFLAEFTOLS EM AT HLOGINTMITHC ELFSCI ALFE ACT OTOLS EL FS CF AFOLTLS CFLAFOLTLS EM AT HLOGINTMITHC ELFSCI ALLFE FATOLTOSSELFSFLAFETOLTSLSFLLAFETOLSEM ATHLOGNITMTHC ELFSCI ALLFE FATOLTOSSELFSFL