



def   a_	(			__a		,							__a		,							__a		,							__a		,							__a		,							__a		,							):
     A__				=   list(__a      )
     A__				=   list(__a      )
     A__				=   len(__a      )
     A__				=   len(__a      )

     A__				=   [
         [0 for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
     ]
     A__				=   [
         ['''0''' for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
     ]

     for i in range(1		,							len_source_seq + 1      ):
          A__				=   i * delete_cost
          A__				=   f'''D{source_seq[i - 1]:c}'''

     for i in range(1		,							len_destination_seq + 1      ):
          A__				=   i * insert_cost
          A__				=   f'''I{destination_seq[i - 1]:c}'''

     for i in range(1		,							len_source_seq + 1      ):
          for j in range(1		,							len_destination_seq + 1      ):
               if source_seq[i - 1] == destination_seq[j - 1]:
                    A__				=   costs[i - 1][j - 1] + copy_cost
                    A__				=   f'''C{source_seq[i - 1]:c}'''
               else:
                    A__				=   costs[i - 1][j - 1] + replace_cost
                    A__				=   f'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]      )

               if costs[i - 1][j] + delete_cost < costs[i][j]:
                    A__				=   costs[i - 1][j] + delete_cost
                    A__				=   f'''D{source_seq[i - 1]:c}'''

               if costs[i][j - 1] + insert_cost < costs[i][j]:
                    A__				=   costs[i][j - 1] + insert_cost
                    A__				=   f'''I{destination_seq[j - 1]:c}'''

     return costs, ops



def   a_	(			__a		,							__a		,							__a      ):
     if i == 0 and j == 0:
          return []
     else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
               A__				=   assemble_transformation(__a		,							i - 1		,							j - 1      )
               seq.append(ops[i][j]      )
               return seq
          elif ops[i][j][0] == "D":
               A__				=   assemble_transformation(__a		,							i - 1		,							__a      )
               seq.append(ops[i][j]      )
               return seq
          else:
               A__				=   assemble_transformation(__a		,							__a		,							j - 1      )
               seq.append(ops[i][j]      )
               return seq


if __name__ == "__main__":
 __snake_case	:				Optional[int]  					=			compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

 __snake_case	:				str  					=			len(operations)
 __snake_case	:				Any  					=			len(operations[0])
 __snake_case	:				List[str]  					=			assemble_transformation(operations, m - 1, n - 1)

 __snake_case	:				Tuple  					=			list('Python')
 __snake_case	:				Tuple  					=			0
 __snake_case	:				int  					=			0

 with open('min_cost.txt', 'w') as file:
  for op in sequence:
   print(''.join(string))

   if op[0] == "C":
    file.write('%-16s' % 'Copy %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost -= 1
   elif op[0] == "R":
    __snake_case	:				Dict  					=			op[2]

    file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
    file.write('\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 1
   elif op[0] == "D":
    string.pop(i)

    file.write('%-16s' % 'Delete %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 2
   else:
    string.insert(i, op[1])

    file.write('%-16s' % 'Insert %c' % op[1])
    file.write('\t\t\t' + ''.join(string))
    file.write('\r\n')

    cost += 2

   i += 1

  print(''.join(string))
  print('Cost: ', cost)

  file.write('\r\nMinimum cost: ' + str(cost))








"""simple docstring"""







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case	:				Optional[Any]  					=			500_000

__snake_case					,  __snake_case	:				Any  					=			os.path.split(__file__)
__snake_case	:				Optional[Any]  					=			os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.select(range(0		,							len(__a      )		,							2      )      )



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.sort('''numbers'''      )



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.shuffle()



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.train_test_split(0.1      )



@get_duration
def   a_	(			__a		,							__a=10      ):
     for shard_id in range(__a      ):
          A__				=   dataset.shard(__a		,							__a      )



def   a_	(			):
     A__				=   {'''num examples''': SPEED_TEST_N_EXAMPLES}
     A__				=   (select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('''generating dataset'''      )
          A__				=   datasets.Features({'''text''': datasets.Value('''string'''      ), '''numbers''': datasets.Value('''float32'''      )}      )
          A__				=   generate_example_dataset(
              os.path.join(__a		,							'''dataset.arrow'''      )		,							__a		,							num_examples=__a      )
          print('''Functions'''      )
          for func in functions:
               print(func.__name__      )
               A__				=   func(__a      )

     with open(__a		,							'''wb'''      ) as f:
          f.write(json.dumps(__a      ).encode('''utf-8'''      )      )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_indices_mapping()



