Reduces the num of hashes, dbGet and dbPut on cases D & E.

Current benchmarks:
```
CASE A, AddBatch was 9.101855 times faster than without AddBatch
	nCPU: 4, nLeafs: 1024, hash: Poseidon, db: memory
	dbgStats(hash: 2.047k, dbGet: 1, dbPut: 2.049k)
CASE B, AddBatch was 8.244078 times faster than without AddBatch
	nCPU: 4, nLeafs: 1024, hash: Poseidon, db: memory
	dbgStats(hash: 2.047k, dbGet: 198, dbPut: 2.049k)
CASE C, AddBatch was 8.511131 times faster than without AddBatch
	nCPU: 4, nLeafs: 1024, hash: Poseidon, db: memory
	dbgStats(hash: 2.047k, dbGet: 202, dbPut: 2.049k)
CASE D, AddBatch was 8.565696 times faster than without AddBatch
	nCPU: 4, nLeafs: 4096, hash: Poseidon, db: memory
	dbgStats(hash: 8.191k, dbGet: 1.800k, dbPut: 8.193k)
CASE E, AddBatch was 8.970056 times faster than without AddBatch
	nCPU: 4, nLeafs: 4096, hash: Poseidon, db: memory
	dbgStats(hash: 10.409k, dbGet: 2.668k, dbPut: 10.861k)
TestAddBatchBench: nCPU: 4, nLeafs: 50000, hash: Blake2b, db: leveldb
	Add loop:	10.616145533s
		dbgStats(hash: 824.884k, dbGet: 788.975k, dbPut: 924.884k)
	AddBatch:	942.34815ms
		dbgStats(hash: 122.051k, dbGet: 1, dbPut: 122.053k)
TestDbgStats
	add in loop    dbgStats(hash: 141.911k, dbGet: 134.650k, dbPut: 161.911k)
	addbatch caseA dbgStats(hash: 24.522k, dbGet: 1, dbPut: 24.524k)
	addbatch caseD dbgStats(hash: 26.985k, dbGet: 2.465k, dbPut: 26.989k)
```

Add dbgStats metrics to analyze number of Hashes, db Gets, and db Puts.

Current benchmarks:
```
CASE A, AddBatch was 8.841700 times faster than without AddBatch
	nCPU: 4, nLeafs: 1024, hash: Poseidon, db: memory
	dbgStats(hash: 2.044k, dbGet: 1, dbPut: 2.049k)
CASE B, AddBatch was 7.678766 times faster than without AddBatch
	nCPU: 4, nLeafs: 1024, hash: Poseidon, db: memory
	dbgStats(hash: 2.044k, dbGet: 199, dbPut: 2.049k)
CASE C, AddBatch was 8.401087 times faster than without AddBatch
	nCPU: 4, nLeafs: 1024, hash: Poseidon, db: memory
	dbgStats(hash: 2.044k, dbGet: 207, dbPut: 2.049k)
CASE D, AddBatch was 2.466346 times faster than without AddBatch
	nCPU: 4, nLeafs: 4096, hash: Poseidon, db: memory
	dbgStats(hash: 33.884k, dbGet: 30.697k, dbPut: 33.889k)
CASE E, AddBatch was 1.958160 times faster than without AddBatch
	nCPU: 4, nLeafs: 4096, hash: Poseidon, db: memory
	dbgStats(hash: 41.419k, dbGet: 37.558k, dbPut: 41.874k)
TestAddBatchBench: nCPU: 4, nLeafs: 50000, hash: Blake2b, db: leveldb
	Add loop:	10.089858449s
		dbgStats(hash: 825.285k, dbGet: 788.869k, dbPut: 925.285k)
	AddBatch:	904.647829ms
		dbgStats(hash: 122.458k, dbGet: 1, dbPut: 122.463k)
TestDbgStats
	add in loop    dbgStats(hash: 141.915k, dbGet: 134.602k, dbPut: 161.915k)
	addbatch caseA dbgStats(hash: 24.528k, dbGet: 1, dbPut: 24.533k)
	addbatch caseD dbgStats(hash: 115.506k, dbGet: 97.482k, dbPut: 115.516k)
```

TestAddBatchBench
	nCPU: 4, nLeafs: 50000, hash: Blake2b, db: leveldb
		Add loop:	9.304195479s
		AddBatch:	817.590526ms

- Remove cropping on power of two the kvs for AddBatch
  - As not needed to be power of two length with the Virtual Tree
- Fix keyValuesToKvs keyPath

- AddBatch use Virtual Tree for cases A,B,C
- ImportDump use AddBatch instead of adding one by one
- Reorg & add more virtual tree tests

VirtualTree (vt) computes a tree without computing any hash. With the
idea of once all the leafs are placed in their positions, the hashes can
be computed, avoiding computing a node hash more than one time.

- Replace naive AddBatch by optimized AddBatch
- Add blake2b hash support
- Expose needed methods for external usage (ReadLeafValue,
ReadIntermediateChilds)
- Return 'value' in GenProof

- Update CaseB to handle repeated keys cases
- Add test for AddBatch/CaseB with repeated keys
- AddBatch-tests abstract code reusage

AddBatch in CaseD, is parallelized (for each CPU) until almost the top
level, almost dividing the needed time by the number of CPUs.

CASE D: Already populated Tree
==============================
- Use A, B, C, D as subtree
- Sort the Keys in Buckets that share the initial part of the path
- For each subtree add there the new leafs

              R
             /  \
            /    \
           /      \
          *        *
         / |      / \
        /  |     /   \
       /   |    /     \
L:    A    B   C       D
     /\   /\  / \     / \
    ...  ... ... ... ... ...

buildTreeBottomUp splits the key-values into n Buckets (where n is the
number of CPUs), in parallel builds a subtree for each bucket, once all
the subtrees are built, uses the subtrees roots as keys for a new tree,
which as result will have the complete Tree build from bottom to up,
where until the log2(nCPU) level it has been computed in parallel.

As result of this, the tree construction can be parallelized until
almost the top level, almost dividing the time by the number of CPUs.

CASE C: ALMOST CASE B --> if Tree has few Leafs (but numLeafs>=minLeafsThreshold)
==============================================================================
- Use A, B, G, F as Roots of subtrees
- Do CASE B for each subtree
- Then go from L to the Root

              R
             /  \
            /    \
           /      \
          *        *
         / |      / \
        /  |     /   \
       /   |    /     \
L:    A    B   G       D
              / \
             /   \
            /     \
           C      *
                 / \
                /   \
               /     \
              ...    ... (nLeafs >= minLeafsThreshold)

CASE B: ALMOST CASE A, Almost empty Tree --> if Tree has numLeafs < minLeafsThreshold
==============================================================================
- Get the Leafs (key & value) (iterate the tree from the current root getting
the leafs)
- Create a new empty Tree
- Do CASE A for the new Tree, giving the already existing key&values (leafs)
from the original Tree + the new key&values to be added from the AddBatch call

       R                 R
      / \               /  \
     A   *             /    \
        / \           /      \
       B   C         *        *
                    / |      / \
                   /  |     /   \
                  /   |    /     \
           L:    A    B   G       D
                         / \
                        /   \
                       /     \
                      C      *
                            / \
                           /   \
                          /     \
                         ...     ... (nLeafs < minLeafsThreshold)

In case that the tree is empty, build the full tree from bottom to top
(from all the leaf to the root).