

# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
 import jax
 import jaxlib

_UpperCamelCase 			=		get_logger()

_UpperCamelCase 			=		None







class   __lowercase      (TensorFormatter[Mapping, """jax.Array""", Mapping] ):
 def __init__(							self      ,							A_=None      ,							A_=None      ,							**A_					)					->List[Any]:



       '''simple docstring'''





       super().__init__(features=A_					)
       import jax
       from jaxlib.xla_client import Device

       if isinstance(A_      ,							A_					):
             raise ValueError(
                 f"""Expected {device} to be a `str` not {type(A_					)}, as `jaxlib.xla_extension.Device` """
                 '''is not serializable neither with `pickle` nor with `dill`. Instead you can surround '''
                 '''the device with `str()` to get its string identifier that will be internally mapped '''
                 '''to the actual `jaxlib.xla_extension.Device`.'''					)
       __lowerCAmelCase					:						Dict					=					device if isinstance(A_      ,							A_					) else str(jax.devices()[0]					)
       # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
       # with `pickle` nor with `dill`, so we need to use a global variable instead
       global DEVICE_MAPPING
       if DEVICE_MAPPING is None:
             __lowerCAmelCase					:						List[Any]					=					self._map_devices_to_str()
       if self.device not in list(DEVICE_MAPPING.keys()					):
             logger.warning(
                 f"""Device with string identifier {self.device} not listed among the available """
                 f"""devices: {list(DEVICE_MAPPING.keys()					)}, so falling back to the default """
                 f"""device: {str(jax.devices()[0]					)}."""					)
             __lowerCAmelCase					:						Optional[Any]					=					str(jax.devices()[0]					)
       __lowerCAmelCase					:						Dict					=					jnp_array_kwargs
 @staticmethod
 def 	UpperCamelCase__  (							)					->Dict[str, "jaxlib.xla_extension.Device"]:



       '''simple docstring'''





       import jax

       return {str(A_					): device for device in jax.devices()}
 def 	UpperCamelCase__  (							self      ,							A_					)					->List[str]:



       '''simple docstring'''





       import jax
       import jax.numpy as jnp

       if isinstance(A_      ,							A_					) and column:
             if all(
                 isinstance(A_      ,							jax.Array					) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column					):
                   return jnp.stack(A_      ,							axis=0					)
       return column
 def 	UpperCamelCase__  (							self      ,							A_					)					->List[Any]:



       '''simple docstring'''





       import jax
       import jax.numpy as jnp

       if isinstance(A_      ,							(str, bytes, type(A_					))					):
             return value
       elif isinstance(A_      ,							(np.character, np.ndarray)					) and np.issubdtype(value.dtype      ,							np.character					):
             return value.tolist()

       __lowerCAmelCase					:						Tuple					=					{}

       if isinstance(A_      ,							(np.number, np.ndarray)					) and np.issubdtype(value.dtype      ,							np.integer					):
             # the default int precision depends on the jax config
             # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
             if jax.config.jax_enable_xaa:
                   __lowerCAmelCase					:						Dict					=					{'''dtype''': jnp.intaa}
             else:
                   __lowerCAmelCase					:						List[str]					=					{'''dtype''': jnp.intaa}
       elif isinstance(A_      ,							(np.number, np.ndarray)					) and np.issubdtype(value.dtype      ,							np.floating					):
             __lowerCAmelCase					:						List[Any]					=					{'''dtype''': jnp.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
             import PIL.Image

             if isinstance(A_      ,							PIL.Image.Image					):
                   __lowerCAmelCase					:						List[str]					=					np.asarray(A_					)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
       global DEVICE_MAPPING
       if DEVICE_MAPPING is None:
             __lowerCAmelCase					:						Tuple					=					self._map_devices_to_str()

       with jax.default_device(DEVICE_MAPPING[self.device]					):
             # calling jnp.array on a np.ndarray does copy the data
             # see https://github.com/google/jax/issues/4486
             return jnp.array(A_      ,							**{**default_dtype, **self.jnp_array_kwargs}					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->Any:



       '''simple docstring'''





       import jax

       # support for torch, tf, jax etc.
       if config.TORCH_AVAILABLE and "torch" in sys.modules:
             import torch

             if isinstance(A_      ,							torch.Tensor					):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]					)
       if hasattr(A_      ,							'''__array__'''					) and not isinstance(A_      ,							jax.Array					):
             __lowerCAmelCase					:						str					=					data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(A_      ,							np.ndarray					):
             if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(A_					) for substruct in data_struct]					)
       elif isinstance(A_      ,							(list, tuple)					):
             return self._consolidate([self.recursive_tensorize(A_					) for substruct in data_struct]					)
       return self._tensorize(A_					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       return map_nested(self._recursive_tensorize      ,							A_      ,							map_list=A_					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->Mapping:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[Any]					=					self.numpy_arrow_extractor().extract_row(A_					)
       __lowerCAmelCase					:						str					=					self.python_features_decoder.decode_row(A_					)
       return self.recursive_tensorize(A_					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->"jax.Array":



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					self.numpy_arrow_extractor().extract_column(A_					)
       __lowerCAmelCase					:						Optional[int]					=					self.python_features_decoder.decode_column(A_      ,							pa_table.column_names[0]					)
       __lowerCAmelCase					:						Optional[int]					=					self.recursive_tensorize(A_					)
       __lowerCAmelCase					:						List[Any]					=					self._consolidate(A_					)
       return column





 def 	UpperCamelCase__  (							self      ,							A_					)					->Mapping:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					self.numpy_arrow_extractor().extract_batch(A_					)
       __lowerCAmelCase					:						Any					=					self.python_features_decoder.decode_batch(A_					)
       __lowerCAmelCase					:						Optional[int]					=					self.recursive_tensorize(A_					)
       for column_name in batch:
             __lowerCAmelCase					:						Dict					=					self._consolidate(batch[column_name]					)
       return batch





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_UpperCamelCase 			=		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.
_UpperCamelCase 			=		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.
_UpperCamelCase 			=		0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						List[str]					=					len([g for position, g in enumerate(lowercase__						) if g == main_target[position]]						)
      return (item, float(lowercase__						))







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						str					=					random.randint(0   ,	len(lowercase__						) - 1						)
      __lowerCAmelCase					:						int					=					parent_a[:random_slice] + parent_a[random_slice:]
      __lowerCAmelCase					:						Dict					=					parent_a[:random_slice] + parent_a[random_slice:]
      return (child_a, child_a)







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						List[str]					=					list(lowercase__						)
      if random.uniform(0   ,	1						) < MUTATION_PROBABILITY:
            __lowerCAmelCase					:						int					=					random.choice(lowercase__						)
      return "".join(lowercase__						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	):
      __lowerCAmelCase					:						str					=					[]
      # Generate more children proportionally to the fitness score.
      __lowerCAmelCase					:						str					=					int(parent_a[1] * 1_0_0						) + 1
      __lowerCAmelCase					:						Optional[Any]					=					1_0 if child_n >= 1_0 else child_n
      for _ in range(lowercase__						):
            __lowerCAmelCase					:						List[Any]					=					population_score[random.randint(0   ,	lowercase__						)][0]

            __lowerCAmelCase,							__lowerCAmelCase					:						Dict					=					crossover(parent_a[0]   ,	lowercase__						)
            # Append new string to the population list.
            pop.append(mutate(lowercase__   ,	lowercase__						)						)
            pop.append(mutate(lowercase__   ,	lowercase__						)						)
      return pop







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__ = True						):

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

      # Generate random starting population.
      __lowerCAmelCase					:						List[Any]					=					[]
      for _ in range(lowercase__						):
            population.append(''''''.join([random.choice(lowercase__						) for i in range(len(lowercase__						)						)]						)						)

      # Just some logs to know what the algorithms is doing.
      __lowerCAmelCase,							__lowerCAmelCase					:						Tuple					=					0, 0

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

            # 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.
            __lowerCAmelCase					:						Any					=					[evaluate(lowercase__   ,	lowercase__						) for item in population]

            # Check if there is a matching evolution.
            __lowerCAmelCase					:						Union[str, Any]					=					sorted(lowercase__   ,	key=lambda lowercase__						: x[1]   ,	reverse=lowercase__						)
            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.
            __lowerCAmelCase					:						Tuple					=					population[: int(N_POPULATION / 3						)]
            population.clear()
            population.extend(lowercase__						)
            # Normalize population score to be between 0 and 1.
            __lowerCAmelCase					:						List[Any]					=					[
                (item, score / len(lowercase__						)) for item, score in population_score
            ]

            # This is selection
            for i in range(lowercase__						):
                  population.extend(select(population_score[int(lowercase__						)]   ,	lowercase__   ,	lowercase__						)						)
                  # 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(lowercase__						) > N_POPULATION:
                        break


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


