


import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map



def  _a			(						__UpperCamelCase ):
     a_    :     List[Any]										=    []
     if isinstance(__UpperCamelCase  ,  __UpperCamelCase ):
          for v in tree.values():
               shapes.extend(_fetch_dims(__UpperCamelCase ) )
     elif isinstance(__UpperCamelCase  ,  (list, tuple) ):
          for t in tree:
               shapes.extend(_fetch_dims(__UpperCamelCase ) )
     elif isinstance(__UpperCamelCase  ,  torch.Tensor ):
          shapes.append(tree.shape )
     else:
          raise ValueError("""Not supported""" )

     return shapes



@torch.jit.ignore
def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     Optional[Any]										=    []
     for d in reversed(__UpperCamelCase ):
          idx.append(flat_idx % d )
          a_    :     List[Any]										=    flat_idx // d

     return tuple(reversed(__UpperCamelCase ) )



@torch.jit.ignore
def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase = None  ,  __UpperCamelCase = None  ,  ):

     # start_edges and end_edges both indicate whether, starting from any given
     # dimension, the start/end index is at the top/bottom edge of the
     # corresponding tensor, modeled as a tree
     def reduce_edge_list(__UpperCamelCase ) -> None:
          a_    :     Dict										=    True
          for i in range(len(__UpperCamelCase ) ):
               a_    :     List[Any]										=    -1 * (i + 1)
               l[reversed_idx] &= tally
               a_    :     List[Any]										=    l[reversed_idx]

     if start_edges is None:
          a_    :     str										=    [s == 0 for s in start]
          reduce_edge_list(__UpperCamelCase )
     if end_edges is None:
          a_    :     Any										=    [e == (d - 1) for e, d in zip(__UpperCamelCase  ,  __UpperCamelCase )]
          reduce_edge_list(__UpperCamelCase )

     # Base cases. Either start/end are empty and we're done, or the final,
     # one-dimensional tensor can be simply sliced
     if len(__UpperCamelCase ) == 0:
          return [()]
     elif len(__UpperCamelCase ) == 1:
          return [(slice(start[0]  ,  end[0] + 1 ),)]

     a_    :     List[Tuple[slice, ...]]										=    []
     a_    :     List[slice]										=    []

     # Dimensions common to start and end can be selected directly
     for s, e in zip(__UpperCamelCase  ,  __UpperCamelCase ):
          if s == e:
               path_list.append(slice(__UpperCamelCase  ,  s + 1 ) )
          else:
               break

     a_    :     Tuple[slice, ...]										=    tuple(__UpperCamelCase )
     a_    :     Any										=    len(__UpperCamelCase )

     # start == end, and we're done
     if divergence_idx == len(__UpperCamelCase ):
          return [path]

     def upper() -> Tuple[Tuple[slice, ...], ...]:
          assert start_edges is not None
          assert end_edges is not None

          a_    :     Any										=    start[divergence_idx]
          return tuple(
              path + (slice(__UpperCamelCase  ,  sdi + 1 ),) + s
              for s in _get_minimal_slice_set(
                  start[divergence_idx + 1 :]  ,  [d - 1 for d in dims[divergence_idx + 1 :]]  ,  dims[divergence_idx + 1 :]  ,  start_edges=start_edges[divergence_idx + 1 :]  ,  end_edges=[True for _ in end_edges[divergence_idx + 1 :]]  ,  ) )

     def lower() -> Tuple[Tuple[slice, ...], ...]:
          assert start_edges is not None
          assert end_edges is not None

          a_    :     Dict										=    end[divergence_idx]
          return tuple(
              path + (slice(__UpperCamelCase  ,  edi + 1 ),) + s
              for s in _get_minimal_slice_set(
                  [0 for _ in start[divergence_idx + 1 :]]  ,  end[divergence_idx + 1 :]  ,  dims[divergence_idx + 1 :]  ,  start_edges=[True for _ in start_edges[divergence_idx + 1 :]]  ,  end_edges=end_edges[divergence_idx + 1 :]  ,  ) )

     # If both start and end are at the edges of the subtree rooted at
     # divergence_idx, we can just select the whole subtree at once
     if start_edges[divergence_idx] and end_edges[divergence_idx]:
          slices.append(path + (slice(start[divergence_idx]  ,  end[divergence_idx] + 1 ),) )
     # If just start is at the edge, we can grab almost all of the subtree,
     # treating only the ragged bottom edge as an edge case
     elif start_edges[divergence_idx]:
          slices.append(path + (slice(start[divergence_idx]  ,  end[divergence_idx] ),) )
          slices.extend(lower() )
     # Analogous to the previous case, but the top is ragged this time
     elif end_edges[divergence_idx]:
          slices.extend(upper() )
          slices.append(path + (slice(start[divergence_idx] + 1  ,  end[divergence_idx] + 1 ),) )
     # If both sides of the range are ragged, we need to handle both sides
     # separately. If there's contiguous meat in between them, we can index it
     # in one big chunk
     else:
          slices.extend(upper() )
          a_    :     Any										=    end[divergence_idx] - start[divergence_idx]
          if middle_ground > 1:
               slices.append(path + (slice(start[divergence_idx] + 1  ,  end[divergence_idx] ),) )
          slices.extend(lower() )

     return slices



@torch.jit.ignore
def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     Any										=    t.shape[:no_batch_dims]
     a_    :     Optional[Any]										=    list(_flat_idx_to_idx(__UpperCamelCase  ,  __UpperCamelCase ) )
     # _get_minimal_slice_set is inclusive
     a_    :     str										=    list(_flat_idx_to_idx(flat_end - 1  ,  __UpperCamelCase ) )

     # Get an ordered list of slices to perform
     a_    :     Dict										=    _get_minimal_slice_set(
         __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  )

     a_    :     List[str]										=    [t[s] for s in slices]

     return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase = False  ,  __UpperCamelCase = None  ,  __UpperCamelCase = False  ,  ):
     if not (len(__UpperCamelCase ) > 0):
          raise ValueError("""Must provide at least one input""" )

     a_    :     Dict										=    [shape[:no_batch_dims] for shape in _fetch_dims(__UpperCamelCase )]
     a_    :     List[str]										=    tuple([max(__UpperCamelCase ) for s in zip(*__UpperCamelCase )] )

     def _prep_inputs(__UpperCamelCase ) -> torch.Tensor:
          if not low_mem:
               if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
                    a_    :     int										=    t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
               a_    :     Dict										=    t.reshape(-1  ,  *t.shape[no_batch_dims:] )
          else:
               a_    :     Any										=    t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
          return t

     a_    :     Dict[str, Any]										=    tensor_tree_map(_prep_inputs  ,  __UpperCamelCase )
     a_    :     int										=    None
     if _out is not None:
          a_    :     Optional[int]										=    tensor_tree_map(lambda __UpperCamelCase : t.view([-1] + list(t.shape[no_batch_dims:] ) )  ,  _out )

     a_    :     Dict										=    1
     for d in orig_batch_dims:
          flat_batch_dim *= d

     a_    :     Any										=    flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

     def _select_chunk(__UpperCamelCase ) -> torch.Tensor:
          return t[i : i + chunk_size] if t.shape[0] != 1 else t

     a_    :     List[str]										=    0
     a_    :     int										=    prepped_outputs
     for _ in range(__UpperCamelCase ):
          # Chunk the input
          if not low_mem:
               a_    :     Any										=    _select_chunk
          else:
               a_    :     Tuple										=    partial(
                   _chunk_slice  ,  flat_start=__UpperCamelCase  ,  flat_end=min(__UpperCamelCase  ,  i + chunk_size )  ,  no_batch_dims=len(__UpperCamelCase )  ,  )

          a_    :     Dict[str, Any]										=    tensor_tree_map(__UpperCamelCase  ,  __UpperCamelCase )

          # Run the layer on the chunk
          a_    :     Optional[int]										=    layer(**__UpperCamelCase )

          # Allocate space for the output
          if out is None:
               a_    :     List[str]										=    tensor_tree_map(lambda __UpperCamelCase : t.new_zeros((flat_batch_dim,) + t.shape[1:] )  ,  __UpperCamelCase )

          # Put the chunk in its pre-allocated space
          if isinstance(__UpperCamelCase  ,  __UpperCamelCase ):

               def assign(__UpperCamelCase  ,  __UpperCamelCase ) -> None:
                    for k, v in da.items():
                         if isinstance(__UpperCamelCase  ,  __UpperCamelCase ):
                              assign(__UpperCamelCase  ,  da[k] )
                         else:
                              if _add_into_out:
                                   v[i : i + chunk_size] += da[k]
                              else:
                                   a_    :     Union[str, Any]										=    da[k]

               assign(__UpperCamelCase  ,  __UpperCamelCase )
          elif isinstance(__UpperCamelCase  ,  __UpperCamelCase ):
               for xa, xa in zip(__UpperCamelCase  ,  __UpperCamelCase ):
                    if _add_into_out:
                         xa[i : i + chunk_size] += xa
                    else:
                         a_    :     int										=    xa
          elif isinstance(__UpperCamelCase  ,  torch.Tensor ):
               if _add_into_out:
                    out[i : i + chunk_size] += output_chunk
               else:
                    a_    :     str										=    output_chunk
          else:
               raise ValueError("""Not supported""" )

          i += chunk_size

     a_    :     Dict										=    tensor_tree_map(lambda __UpperCamelCase : t.view(orig_batch_dims + t.shape[1:] )  ,  __UpperCamelCase )

     return out


class   a__	:

       def __init__(   self      :     List[str]      ,							lowerCamelCase_      :     int = 5_1_2      ,							):
            a_    :     Tuple										=    max_chunk_size
            a_    :     Optional[int]										=    None
            a_    :     Optional[tuple]										=    None

       def 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     Callable      ,							lowerCamelCase_      :     tuple      ,							lowerCamelCase_      :     int    ):
            logging.info("""Tuning chunk size..."""    )

            if min_chunk_size >= self.max_chunk_size:
                 return min_chunk_size

            a_    :     List[int]										=    [2**l for l in range(int(math.log(self.max_chunk_size      ,							2    )    ) + 1    )]
            a_    :     str										=    [c for c in candidates if c > min_chunk_size]
            a_    :     int										=    [min_chunk_size] + candidates
            candidates[-1] += 4

            def test_chunk_size(lowerCamelCase_      :     int    ) -> bool:
                 try:
                      with torch.no_grad():
                           fn(*lowerCamelCase_      ,							chunk_size=lowerCamelCase_    )
                      return True
                 except RuntimeError:
                      return False

            a_    :     Dict										=    0
            a_    :     Union[str, Any]										=    len(lowerCamelCase_    ) - 1
            while i > min_viable_chunk_size_index:
                 a_    :     List[Any]										=    test_chunk_size(candidates[i]    )
                 if not viable:
                      a_    :     Union[str, Any]										=    (min_viable_chunk_size_index + i) // 2
                 else:
                      a_    :     Optional[Any]										=    i
                      a_    :     Tuple										=    (i + len(lowerCamelCase_    ) - 1) // 2

            return candidates[min_viable_chunk_size_index]

       def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     Iterable      ,							lowerCamelCase_      :     Iterable    ):
            a_    :     List[str]										=    True
            for aa, aa in zip(lowerCamelCase_      ,							lowerCamelCase_    ):
                 assert type(lowerCamelCase_    ) == type(lowerCamelCase_    )
                 if isinstance(lowerCamelCase_      ,							(list, tuple)    ):
                      consistent &= self._compare_arg_caches(lowerCamelCase_      ,							lowerCamelCase_    )
                 elif isinstance(lowerCamelCase_      ,							lowerCamelCase_    ):
                      a_    :     str										=    [v for _, v in sorted(aa.items()      ,							key=lambda lowerCamelCase_    : x[0]    )]
                      a_    :     Any										=    [v for _, v in sorted(aa.items()      ,							key=lambda lowerCamelCase_    : x[0]    )]
                      consistent &= self._compare_arg_caches(lowerCamelCase_      ,							lowerCamelCase_    )
                 else:
                      consistent &= aa == aa

            return consistent

       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     Callable      ,							lowerCamelCase_      :     tuple      ,							lowerCamelCase_      :     int      ,							):
            a_    :     int										=    True
            a_    :     tuple										=    tree_map(lambda lowerCamelCase_    : a.shape if isinstance(lowerCamelCase_      ,							torch.Tensor    ) else a      ,							lowerCamelCase_      ,							lowerCamelCase_    )
            if self.cached_arg_data is not None:
                 # If args have changed shape/value, we need to re-tune
                 assert len(self.cached_arg_data    ) == len(lowerCamelCase_    )
                 a_    :     Tuple										=    self._compare_arg_caches(self.cached_arg_data      ,							lowerCamelCase_    )
            else:
                 # Otherwise, we can reuse the precomputed value
                 a_    :     Dict										=    False

            if not consistent:
                 a_    :     List[str]										=    self._determine_favorable_chunk_size(
                     lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							)
                 a_    :     List[str]										=    arg_data

            assert self.cached_chunk_size is not None

            return self.cached_chunk_size







from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCamelCase        =     200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__lowerCamelCase        =     50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__lowerCamelCase        =     0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     List[Any]										=    len([g for position, g in enumerate(__UpperCamelCase ) if g == main_target[position]] )
     return (item, float(__UpperCamelCase ))



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     List[Any]										=    random.randint(0  ,  len(__UpperCamelCase ) - 1 )
     a_    :     Tuple										=    parent_a[:random_slice] + parent_a[random_slice:]
     a_    :     Optional[Any]										=    parent_a[:random_slice] + parent_a[random_slice:]
     return (child_a, child_a)



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     Optional[int]										=    list(__UpperCamelCase )
     if random.uniform(0  ,  1 ) < MUTATION_PROBABILITY:
          a_    :     int										=    random.choice(__UpperCamelCase )
     return "".join(__UpperCamelCase )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  ):
     a_    :     Tuple										=    []
     # Generate more children proportionally to the fitness score.
     a_    :     Any										=    int(parent_a[1] * 1_0_0 ) + 1
     a_    :     int										=    1_0 if child_n >= 1_0 else child_n
     for _ in range(__UpperCamelCase ):
          a_    :     Tuple										=    population_score[random.randint(0  ,  __UpperCamelCase )][0]

          a_			,			a_    :     List[str]										=    crossover(parent_a[0]  ,  __UpperCamelCase )
          # Append new string to the population list.
          pop.append(mutate(__UpperCamelCase  ,  __UpperCamelCase ) )
          pop.append(mutate(__UpperCamelCase  ,  __UpperCamelCase ) )
     return pop



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase = True ):

     # Verify if N_POPULATION is bigger than N_SELECTED
     if N_POPULATION < N_SELECTED:
          a_    :     Union[str, Any]										=    F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
          raise ValueError(__UpperCamelCase )
     # Verify that the target contains no genes besides the ones inside genes variable.
     a_    :     List[str]										=    sorted({c for c in target if c not in genes} )
     if not_in_genes_list:
          a_    :     Optional[Any]										=    F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
          raise ValueError(__UpperCamelCase )

     # Generate random starting population.
     a_    :     str										=    []
     for _ in range(__UpperCamelCase ):
          population.append("""""".join([random.choice(__UpperCamelCase ) for i in range(len(__UpperCamelCase ) )] ) )

     # Just some logs to know what the algorithms is doing.
     a_			,			a_    :     List[str]										=    0, 0

     # This loop will end when we find a perfect match for our target.
     while True:
          generation += 1
          total_population += len(__UpperCamelCase )

          # Random population created. Now it's time to evaluate.

          # Adding a bit of concurrency can make everything faster,
          #
          # import concurrent.futures
          # population_score: list[tuple[str, float]] = []
          # with concurrent.futures.ThreadPoolExecutor(
          #                                   max_workers=NUM_WORKERS) as executor:
          #     futures = {executor.submit(evaluate, item) for item in population}
          #     concurrent.futures.wait(futures)
          #     population_score = [item.result() for item in futures]
          #
          # but with a simple algorithm like this, it will probably be slower.
          # We just need to call evaluate for every item inside the population.
          a_    :     Tuple										=    [evaluate(__UpperCamelCase  ,  __UpperCamelCase ) for item in population]

          # Check if there is a matching evolution.
          a_    :     Optional[Any]										=    sorted(__UpperCamelCase  ,  key=lambda __UpperCamelCase : x[1]  ,  reverse=__UpperCamelCase )
          if population_score[0][0] == target:
               return (generation, total_population, population_score[0][0])

          # Print the best result every 10 generation.
          # Just to know that the algorithm is working.
          if debug and generation % 1_0 == 0:
               print(
                   F'''\nGeneration: {generation}'''
                   F'''\nTotal Population:{total_population}'''
                   F'''\nBest score: {population_score[0][1]}'''
                   F'''\nBest string: {population_score[0][0]}''' )

          # Flush the old population, keeping some of the best evolutions.
          # Keeping this avoid regression of evolution.
          a_    :     List[str]										=    population[: int(N_POPULATION / 3 )]
          population.clear()
          population.extend(__UpperCamelCase )
          # Normalize population score to be between 0 and 1.
          a_    :     Optional[Any]										=    [
              (item, score / len(__UpperCamelCase )) for item, score in population_score
          ]

          # This is selection
          for i in range(__UpperCamelCase ):
               population.extend(select(population_score[int(__UpperCamelCase )]  ,  __UpperCamelCase  ,  __UpperCamelCase ) )
               # Check if the population has already reached the maximum value and if so,
               # break the cycle.  If this check is disabled, the algorithm will take
               # forever to compute large strings, but will also calculate small strings in
               # a far fewer generations.
               if len(__UpperCamelCase ) > N_POPULATION:
                    break


if __name__ == "__main__":
       __lowerCamelCase        =     (
           '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
       )
       __lowerCamelCase        =     list(
           ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
           '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
       )
       __lowerCamelCase  ,       __lowerCamelCase  ,       __lowerCamelCase        =     basic(target_str, genes_list)
       print(
           f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
       )



