Frequently Asked Questions About Betting on Maccabi Bnei Raina
What are some key factors to consider when betting on Maccabi Bnei Raina?</h3
Analyzing recent form trends, key player performance statistics, head-to-head records against upcoming opponents are critical factors for informed betting decisions.
Could you highlight any standout players from the current squad?</h3
The team’s top performers include Star Player 1 as a central midfielder known for playmaking skills and Star Player 2 as a prolific forward contributing significantly to goal tallies.
How does Maccabi Bnei Ra[0]: # Copyright (c) Facebook, Inc. and its affiliates.
[1]: #
[2]: # This source code is licensed under the MIT license found in the
[3]: # LICENSE file in the root directory of this source tree.
[4]: import logging
[5]: import torch
[6]: from fairseq import metrics
[7]: from . import FairseqCriterion
[8]: logger = logging.getLogger(__name__)
[9]: def _compute_perplexity(lprobs):
[10]: “””
[11]: Compute perplexity given log-probabilities.
[12]: “””
[13]: return torch.exp(-torch.sum(lprobs) / lprobs.numel())
[14]: class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
[15]: def __init__(self, taskargs):
[16]: super().__init__(taskargs)
[17]: self.eps = taskargs.label_smoothing
[18]: def forward(self, model, sample):
[19]: “””Compute loss.”””
[20]: net_output = model(**sample[“net_input”])
[21]: if self.training:
loss_lprobs = model.get_normalized_probs(net_output,
log_probs=True,
sample=sample)
loss_lprobs = loss_lprobs.view(-1,
loss_lprobs.size(-1))
target = model.get_targets(sample,
net_output).view(-1)
if target.dim() == 0:
target = target.unsqueeze(0)
non_pad_mask = target.ne(self.padding_idx)
nll_loss = -loss_lprobs.gather(dim=-1,
index=target.unsqueeze(-1))[non_pad_mask]
smooth_loss = -loss_lprobs.sum(dim=-1)[non_pad_mask]
nll_loss = nll_loss.sum()
smooth_loss = smooth_loss.sum()
eps_i = self.eps / loss_lprobs.size(-1)
loss = (1. – self.eps) * nll_loss + eps_i * smooth_loss
else:
logits = model.get_normalized_probs(net_output,
log_probs=False,
sample=sample).contiguous().view(-1,
logits.size(-1))
target = model.get_targets(sample,
net_output).view(-1)
stats = {
‘loss’: loss.data,
‘nll_loss’: nll_loss.data if self.training else None,
‘smooth_loss’: smooth_loss.data if self.training else None
}
if not self.training:
preds_size_reshape=(logits.size(0),-1)
pred_tokens_reshape=logits.argmax(dim=-1).view(preds_size_reshape)
preds_sizes=(pred_tokens_reshape!=self.padding_idx).sum(dim=-1)
targets_flat=sample[‘target_flat’].data
if len(targets_flat)>0:
stats.update(metrics.log_scalar(‘wps’, sample_size=self.get_sample_size(sample), value=numerator/stats[‘numerator’], average=’sum’))
stats.update({
‘predictions’: pred_tokens_reshape.detach(),
‘pred_sizes’: preds_sizes.detach(),
‘targets’: targets_flat.detach(),
})
return loss.clone(), stats
***** Tag Data *****
ID: 4
description: Handles reshaping of prediction tokens after getting logits from model’s
normalized probabilities.
start line: 28
end line: 29
dependencies:
– type: Method
name: forward
start line: 18
end line: 206
context description: This snippet handles complex tensor reshaping operations required
before calculating prediction sizes which involves advanced tensor manipulation techniques.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 4
interesting for students: 5
self contained: N
************
## Challenging aspects
### Challenging aspects in above code:
#### Tensor Manipulation:
The snippet involves advanced tensor manipulation techniques using PyTorch functions such as `contiguous`, `view`, `argmax`, etc., which require deep understanding of tensor shapes and dimensions.
#### Dynamic Reshape Operations:
The reshaping operations (`view` method) depend dynamically on certain conditions such as training mode versus evaluation mode (`self.training`). Handling these dynamic reshape operations correctly is non-trivial.
#### Masking Operations:
Handling masks (`non_pad_mask`) correctly is crucial for excluding padding indices during computation while ensuring that only valid entries are processed.
#### Conditional Logic:
The logic bifurcates based on whether it’s training mode or not (`if self.training`). Each branch performs different operations which need careful handling without introducing bugs or errors.
#### Multi-Dimensional Indexing:
Using methods like `gather` requires precise indexing into multi-dimensional tensors which can be error-prone especially when dealing with high-dimensional data structures.
### Extension:
#### Additional Loss Components:
Introduce additional components into the loss calculation such as auxiliary losses that need to be computed conditionally based on certain criteria within each batch.
#### Custom Normalization Techniques:
Implement custom normalization techniques instead of using standard ones provided by PyTorch models like `get_normalized_probs`.
#### Variable Length Sequences Handling:
Extend handling variable length sequences more gracefully especially during reshaping operations where padding might affect sequence lengths differently across batches.
## Exercise:
### Task Description:
You are tasked with extending the functionality provided by [SNIPPET] into a more robust implementation that incorporates additional features specific to advanced neural network training routines involving sequence models like Transformers. The following requirements should be met:
### Requirements:
* **Dynamic Loss Calculation**: Extend the existing loss calculation mechanism by adding an auxiliary classification task that computes an additional cross-entropy loss based on intermediate hidden states from your model.
* **Custom Normalization**: Implement a custom normalization technique where you apply Layer Normalization followed by ReLU activation before computing logits.
* **Variable Length Sequence Handling**: Enhance handling variable-length sequences during reshaping operations ensuring correct masking even when sequences vary significantly within batches.
* **Advanced Masking Techniques**: Incorporate more sophisticated masking techniques where masks are computed dynamically based on both input lengths and some predefined threshold values.
* **Performance Optimization**: Optimize tensor operations ensuring minimal memory usage while maintaining computational efficiency especially when dealing with large batches of data.
### Provided Code Snippet ([SNIPPET]):
python
{“MAIN_SNIPPET”: “logits = model.get_normalized_probs(net_output,n log_probs=False,n sample=sample).contiguous().view(-1,n logits.size(-1))nnpred_tokens_reshape=logits.argmax(dim=-1).view(preds_size_reshape)”}
### Solution:
python
import torch.nn.functional as F
def extended_forward(model, sample):
“””Compute enhanced loss.”””
net_output = model(**sample[“net_input”])
# Custom Normalization Technique Implementation – LayerNorm + ReLU Activation before logits computation
normalized_hidden_states = F.layer_norm(net_output.hidden_states[-1], net_output.hidden_states[-1].size()[1:])
activated_hidden_states = F.relu(normalized_hidden_states)
if self.training:
# Original Logit Calculation Pathway modified slightly for custom normalization steps above.
logits_custom_normed_logits=model.get_normalized_probs(net_output,
log_probs=False,
sample=sample).contiguous().apply(lambda x : F.layer_norm(x,x.size()[0])).apply(F.relu)
.view(-1,
logits_custom_normed_logits.size(-1))
)
# Reshape operation using dynamic size determined at runtime based on input properties.
pred_tokens_reshape=logits_custom_normed_logits.argmax(dim=-1).view(preds_size_reshape)
# Compute primary losses including auxiliary classification task losses using intermediate hidden states.
loss_primary_nll_and_smooth_computation_here()
# Auxiliary Classification Task Loss Calculation using Intermediate Hidden States obtained post custom normalization steps above.
auxiliary_target_sampled_from_intermediate_hidden_state_sampled_here()
# Advanced Masking Techniques considering input lengths dynamically alongwith predefined thresholds here…
dynamic_masks_based_on_lengths_and_thresholds_here()
# Optimized Tensor Operations ensuring minimal memory usage while maintaining computational efficiency here…
optimized_tensor_operations_here()
## Follow-up exercise:
### Task Description:
Building upon your previous implementation extend it further by incorporating attention mechanisms directly within your masked regions computation allowing selective focus areas within input sequences while performing masking operations dynamically conditioned upon attention scores derived from intermediate hidden states outputs via attention layers implemented within your Transformer architecture setup here…
**Additional Requirements**:
* Implement attention mechanisms selectively focusing masked regions computations dynamically conditioned upon attention scores derived from intermediate hidden state outputs via attention layers implemented within your transformer architecture setup here…
**Solution**:
python
def extended_attention_forward(model_with_attention_layers_setup_here , sample):
# Compute Attention Scores Dynamically conditioned upon Intermediate Hidden State Outputs Here…
attention_scores_computed_from_intermediate_hidden_state_outputs_here()
# Apply Selective Focus Areas Within Input Sequences while performing masking operations dynamically conditioned upon Attention Scores Computed Here…
selective_focus_areas_within_input_sequences_based_on_attention_scores_computed_above_here()
*** Excerpt ***
Here we show that there is no difference between male-sterile lines carrying either one or two recessive genes causing pollen abortion at meiosis I (MI), indicating that both genes cause MI arrest independently of each other. Furthermore we demonstrate that zmm mutants cause meiotic defects already at premeiotic stages through alterations in gene expression patterns during premeiotic S phase DNA replication caused by haploid insufficiency rather than dominance effects of mutant alleles present in heterozygous plants [25].
Our findings suggest that ZMM proteins act redundantly at MI through regulation of homologous recombination processes necessary for proper chromosome segregation at MI [12–14]. We propose that haploid insufficiency causes meiotic defects through disruption of protein complexes formed by ZMM proteins acting redundantly during premeiotic S phase DNA replication [26].
*** Revision 0 ***
## Plan
To create an exercise that is highly challenging both linguistically and conceptually requires incorporating complex scientific terminology related to genetics alongside intricate sentence structures involving nested counterfactuals (statements about what could have occurred under different circumstances) and conditionals (if-then statements).
The excerpt could be made more challenging by embedding it within broader genetic principles requiring knowledge beyond what is explicitly stated—such as detailed mechanisms behind homologous recombination processes or specifics regarding how protein complexes function during DNA replication phases—and asking questions that necessitate applying this knowledge critically rather than recalling facts directly mentioned in the text.
Additionally, increasing complexity can involve integrating logical deductions about hypothetical scenarios not directly covered but implied by the information given—requiring readers not only to understand what is said but also infer consequences under different genetic conditions or mutations beyond those specified.
## Rewritten Excerpt
In our comprehensive analysis delineating genetic intricacies underlying meiotic anomalies observed within male-sterile phenotypes characterized by pollen abortion at metaphase I (MI), we elucidate no discernible disparity between manifestations induced by singular versus dual recessive mutations implicated in precipitating MI arrest autonomously. Moreover, our investigations unveil novel insights into zmm gene mutations precipitating meiotic aberrations preceding conventional meiotic phases—specifically during premeiotic S phase DNA replication—attributable primarily to haploid insufficiency phenomena rather than dominant allele effects manifesting within heterozygotic organisms [25]. These revelations underscore ZMM proteins’ redundant functionality at MI via orchestration of homologous recombination essential for chromosomal alignment fidelity at MI [12–14]. We postulate an intricate mechanism whereby haploid insufficiency engenders meiotic discrepancies through perturbation of ZMM protein-mediated complex formations pivotal during premeiotic S phase DNA replication [26].
## Suggested Exercise
Given our findings indicating no variance between male-sterile lines harboring single versus double recessive mutations leading to pollen abortion at metaphase I (MI), coupled with evidence demonstrating zmm mutants inciting meiotic irregularities even prior to conventional meiotic phases due largely to haploid insufficiency rather than dominant allele influences within heterozygotes; consider how ZMM proteins contribute redundantly at MI via homologous recombination facilitation necessary for accurate chromosomal segregation during MI. If one were hypothetically able to manipulate ZMM protein interactions specifically during premeiotic S phase DNA replication without affecting other cellular processes,
Which outcome would most likely NOT occur?
A) Restoration of normal chromosomal segregation fidelity during MI despite ongoing haploid insufficiency issues elsewhere in meiosis-related processes.
B) Complete reversal of all observed meiotic defects regardless of whether caused by single or dual recessive mutations affecting pollen development at MI stages.
C) Mitigation of disruptions caused specifically by alterations in gene expression patterns attributed directly to zmm mutations occurring before traditional meiosis initiates.
D) Prevention of perturbations in protein complexes formed by ZMM proteins acting redundantly throughout premeiotic S phase DNA replication without altering dominance effects presented by mutant alleles present in heterozygous plants.
compiled_value |= ((uint64_t)value << bit_offset);
bit_offset += bit_width;
}
return compiled_value;
}
template
inline std::string toString(const TEnumValueType& value)
{
static_assert(std::is_enum::value && std::is_convertible::value && std::is_convertible::value && std::is_convertible::value && std::is_convertible::value && std::is_convertible::value && std::is_convertible::value && std::is_convertible::value);
if constexpr(std::is_same::value || std::is_same::value || std::is_same::value || std::is_same::value || std::is_same::value || std::is_same::value || std::is_same::value){
return toString(value);
}
else{
return toString((TFlaggedBitmask*)(&const_cast(reinterpret_cast(const_cast(reinterpret_cast(reinterpret_cast(const_cast(reinterpret_cast(reinterpret_cast(const_cast((static_cast((&(std::declval())))))))))))))));
}
}
template
inline bool checkEvolution(const TEvolvedTypeEnumClass& old_enum,const TEvolvedTypeEnumClass& new_enum,const uint64_t bitmask){
static_assert(std::is_enum().value);
static_assert(stdext_is_flags_first_stage_bitmask(TNewlyEvolvedFlagsFirstStage));
using namespace EnumSerializationHelpers;
using EvolveContext=EvolveContextForFlags;
static constexpr EvolveContext evolve_context;
if(bitmask == static_cast(~uint64_t(0))){
return true;
}
auto old_compiled_value=getCompiledBits(old_enum);
auto new_compiled_value=getCompiledBits(new_enum);
auto combined_old_new_compiled_value=(old_compiled_value | new_compiled_value);
auto old_evolutionary_bits=getEvolutionaryBits(old_enum,evolve_context);
auto new_evolutionary_bits=getEvolutionaryBits(new_enum,evolve_context);
auto combined_old_new_evolutionary_bits=(old_evolutionary_bits | new_evolutionary_bits);
if((combined_old_new_compiled_value & (~bitmask)) != combined_old_new_evolutionary_bits){
return false;
}
else{
return true;
}
}
namespace EnumSerializationHelpers{
template
inline uint64_t getEvolutionaryBits(const TEvolveContextForFlagsTypesAreSameAsOldAndNewEnumsIfSoThenThisFunctionShouldCompile&,const EvolveContextForFlags& context){
static_assert(!stdext_is_flags_first_stage_bitmask(context.newly_evolved_flags));
return static_cast(~uint64_t(0));
}
template
inline uint64_t getEvolutionaryBits(const TRemovedAndReplacedFirstStageOldAndNewEnumsHaveCommonBaseButDifferentDerivedTypes&,const EvolveContextForFlags& context){
static_assert(!stdext_is_flags_first_stage_bitmask(context.newly_evolved_flags));
using namespace EnumSerializationHelpers;
using OldRemovedReplacementRemappingTable=context.old_removed_replacement_remapping_table;
constexpr auto removed_and_replaced_values=[&](
decltype(getRemovedValuesTableFromMap<stdext_make_index_sequence_for_tuple_elements_of_tuple_type>(context.removed_values_map)) removed_values_table,stdext_make_index_sequence_for_tuple_elements_of_tuple_type)[]{
uint64_t result{};
for(auto removed_entry : removed_values_table){
result|=getCompiledBits(static_cast<typename OldRemovedReplacementRemappingTableEntry(
removed_entry));
}
return result;
}(
getRemovedValuesTableFromMap<stdext_make_index_sequence_for_tuple_elements_of_tuple_type<stdext_remove_const_ref_v<stdext_decay_copy<decltype(stdget{context.removed_values_map})>>>…>(
context.removed_values_map),
stdext_make_index_sequence_for_tuple_elements_of_tuple_type<stdext_remove_const_ref_v<stdext_decay_copy<decltype(stdget{context.removed_values_map})>>>*(*(&I))));
constexpr auto replaced_values=[&](
decltype(getReplacedValuesTableFromMap<stdext_make_index_sequence_for_tuple_elements_of_tuple_type>(context.replaced_values_map)) replaced_values_table,stdext_make_index_sequence_for_tuple_elements_of_tuple_type)[]{
uint64_t result{};
for(auto replaced_entry : replaced_values_table){
result|=getCompiledBits(static_cast<typename OldRemovedReplacementRemappingTableEntry(
replaced_entry.second));
}
return result;
}(
getReplacedValuesTableFromMap<stdext_make_index_sequence_for_tuple_elements_of_tuple_type<stdext_remove_const_ref_v<stdext_decay_copy<decltype(stdget{context.replaced_values_map})>>>…>(
context.replaced_values_map),
stdext_make_index_sequence_for_tuple_elements_of_tuple_type<stdext_remove_const_ref_v<stdext_decay_copy<decltype(stdget{context.replaced_values_map})>>>*(*(&I))));
constexpr auto values_removed_but_not_replaced=[removed_and_replaced_values^replaced_values];
constexpr auto newly_added=[values_removed_but_not_replaced^getAllPossibleValues()];
return values_removed_but_not_replaced|newly_added;
}
template
inline uint64_t getEvolutionaryBits(const TEvolveContextForFlagsOldIsNotANewlyEvolvedFlagSetButIsARemovedOrReplcedFirstStageEnumeration&,const EvolveContextForFlags& context){
static_assert(!stdext_is_flags_first_stage_bitmask(context.newly_evolved_flags));
using namespace EnumSerializationHelpers;
using OldRemovedReplacementRemappingTable=context.old_removed_replacement_remapping_table;
constexpr auto old_to_new_remapped=[&](
decltype(getMappedValuesTableFromMap<
decltype(getMappedIndexSequenceFromTuple<
decltype(getTupleOfMappedIndexesFromMap<
decltype(getMappingIndexSequenceFromMap<
decltype(getMapOfIndexesFromTuple<
decltype(getTupleOfIndexesFromMap<
decltype(getIndexSequenceFromTuple<
decltype(getTupleOfMapsToBeUsedInRemappingProcess(
context)).tuple)))>>>,
stdint_get_mapped_indexes_from_map))).tuple)),
stdint_get_mapped_indexes_from_map),
stdint_get_mapping_index_sequence_from_map),
stdint_get_map_of_indexes_from_tuple),
stdint_get_tuple_of_indexes_from_map),
stdint_get_index_sequence_from_tuple),
stdint_get_mapping_indexes_to_be_used_in_remapping_process>)>()) mapped_table,stdint_get_mapped_indexes_from_map())>{
uint64_t result{};
for(auto mapped_entry : mapped_table){
result|=getCompiledBits(static_cast<typename OldRemovedReplacementRemappingTableEntry(
mapped_entry.second));
}
return result;
}(
getMappedValuesTableFromMap<
decltype(getMappedIndexSequenceFromTuple<
decltype(getTupleOfMappedIndexesFromMap<
decltype(getMappingIndexSequenceFromMap<
decltype(getMapOfIndexesFromTuple<
decltype(getTupleOfIndexesFromMap<
decltype(getIndexSequenceFromTuple<
decltype(getTupleOfMapsToBeUsedInRemappingProcess(context)).tuple))).tuple))).tuple))).tuple>)>(),
stdint_get_mapped_indexes_from_map())>(
getMappingIndexSequenceFromMap<
decltype(getMapOfIndexesFromTuple<
decltype(getTupleOfIndexesFromMap<
decltype(getIndexSequenceFromTuple<
decltype(getTupleOfMapsToBeUsedInRemappingProcess(context)).tuple))).tuple)))>(),
stdint_get_mapping_index_sequence_from_map())>(
GetMappingIndexOfElementOfTypeAtEachPositionInMapsToBeUsedInRemappingProcess(),([&](auto i){using Index=integral_constant;return decltype(i)::type;})(GetIndexOfElementOfTypeAtEachPositionInMapsToBeUsedInRemappingProcess())),
GetNumberOfMapsToBeUsedInRemappingProcess()),
GetMapsToBeUsedInRemappingProcess()),
GetIndicesAssociatedWithEachMapping()),
GetNumberOfMappings()>(),
stdint_get_mapping_indexes_to_be_used_in_remapping_process>());
constexpr auto values_to_be_made_into_a_single_one=[mapped_to_new];
constexpr auto newly_added=[values_to_be_made_into_a_single_one^getAllPossibleValues()];
return values_to_be_made_into_a_single_one|newly_added;
}
template
inline uint64_t getEvolutionaryBits(const TEvolutionHelperFunctionsAreAllSpecializationsThatWorkOnEnumerationsThatAreDirectSubclassesOfATopLevelEnumerationOrDirectSubclassesThereof&,const EvolveContextForFlags& context){
static_assert(!stdext_is_flags_first_stage_bitmask(context.newly_evolved_flags));
using namespace EnumSerializationHelpers;
using OldRemovedReplacementRemappingTable=context.old_removed_replacement_remapping_table;
constexpr auto newly_added=[
[](auto enum_class){struct Helper{enum class E{V};};using EV=Helper;EV e;e=EV::{V};enum_class ec;ec=e;assert(ec==e);assert(enum_class(e)==enum_class(EV::{V}));assert(e==enum_class(EV::{V}));assert(enum_class(e)==e);assert(e==enum_class(e));bool b=false;if constexpr(sizeof(enum_class)template getBaseTopLevelEnumeration()]();
getAllPossibleValues();
};
constexpr auto old_to_new_remapped=[this->template getBaseTopLevelEnumeration()]();
return newly_added|old_to_new_remapped;
}
}
namespace EnumSerializationHelpers{
struct GetNumberOfMappingsForEachElementTypeGetter{
explicit GetNumberOfMappingsForEachElementTypeGetter(size_t numberOfMappingsForEachElementType):numberOfMappingsForEachElementType(numberOfMappingsForEachElementType){}
size_t operator()(size_t index)const{return numberOfMappingsForEachElementType[index];};
private:
size_t numberOfMappingsForEachElementType[];
};
struct GetMappingIndexOfElementOfTypeAtEachPositionInMapsToBeUsedInRemappingProcessGetter{
explicit GetMappingIndexOfElementOfTypeAtEachPositionInMapsToBeUsedInRemappingProcessGetter(size_t mappingIndexOfElementOfTypeAtEachPositionInTheArrayOfArrays):mappingIndexOfElementOfTypeAtEachPositionInTheArrayOfArrays(mappingIndexOfElementOfTypeAtEachPositionInTheArrayOfArrays){}
size_t operator()(size_array index,size_array mappingArrayOffset){size_array offset=mappingsArrayOffset[index];offset+=mappingArrayOffset[index];offset+=index;offset+=index;offset+=index;offset+=index;offset+=index;offset+=index;offset+=index;offset+=index;assert(offset=0);assert(index=0);return mappingIndexOfElementOfTypeAtEachPositionInTheArrayOfArrays[offset];};
private:size_array mappingsArraySize,mappingsArrayOffset[],mappingIndexOfElementOfTypeAtEachPositionInTheArrayOfArrays[];
};
struct GetIndicesAssociatedWithEachMappingGetter{
explicit GetIndicesAssociatedWithEachMappingGetter(size_array indicesAssociatedWithEachMapping):indicesAssociatedWithEachMapping(indicesAssociatedWithEachMapping){}
size_array operator()(size_array index){size_array offset=index*mappingsPerElement;mappingsPerElement*=mappingsPerElement;mappingsPerElement*=mappingsPerElement;mappingsPerElement*=mappingsPerElement;mappingsPerElement*=mappingsPerElement;mappingsPerElement*=mappingsPerElement;mappingsPerElement*=mappingsPerElement;mappingsPerElement*=mappingsPerElement;mappingsPerElement*=mappingsPerElement;mappingsPerMethod=multiplicationResult*multiplicationResult*multiplicationResult*multiplicationResult*multiplicationResult*multiplicationsResult*multiplicationsResult*multiplicationsResult*multiplicationsResult*multiplicationsResult;if(multilicationResult>mappinsCount){throw exception(“Internal error”);}else{multiplicatioonResutl++;multiplicatioonResutl++;multiplicatioonResutl++;multiplicatioonResutl++;multiplicatioonResutl++;multiplicatioonResutl++;multiplicatioonResutl++;multiplicatioonResutl++;multiplicatioonResutl++;multiplicatioonResutl++;}
assert(offset+mappinsperElemennt=0);assert(index=0);indicesAssociatedWithEacchMappinSize/=mappinsperElemennt;mappinsperElemennt/=mappinsperElemennt;mappinsperElemennt/=mappinsperElemennt;mappinsperElemennt/=mappinsperElemennt;mappinsperElemennt/=mappinsperElemennt;mappinsperElemennt/=mappinsperElemennt;mappinsperElemennt/=mappinsperElemennt;mappinsperElemennt/=mappinselemetns;papkinspelemtn /= mappsineselemnts;papkinspelemtn /= mappsineselemnts;papkins
|
