




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, 1_000))




def 					lowerCamelCase__       (						A__       :					str  ,			A__       :					str	):





       '''simple docstring'''



       __lowerCamelCase   =						len([g for position, g in enumerate(A__	) if g == main_target[position]]	)
       return (item, float(A__	))




def 					lowerCamelCase__       (						A__       :					str  ,			A__       :					str	):





       '''simple docstring'''



       __lowerCamelCase   =						random.randint(0  ,			len(A__	) - 1	)
       __lowerCamelCase   =						parent_a[:random_slice] + parent_a[random_slice:]
       __lowerCamelCase   =						parent_a[:random_slice] + parent_a[random_slice:]
       return (child_a, child_a)




def 					lowerCamelCase__       (						A__       :					str  ,			A__       :					list[str]	):





       '''simple docstring'''



       __lowerCamelCase   =						list(A__	)
       if random.uniform(0  ,			1	) < MUTATION_PROBABILITY:
              __lowerCamelCase   =						random.choice(A__	)
       return "".join(A__	)




def 					lowerCamelCase__       (						A__       :					tuple[str, float]  ,			A__       :					list[tuple[str, float]]  ,			A__       :					list[str]  ,			):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       # Generate more children proportionally to the fitness score.
       __lowerCamelCase   =						int(parent_a[1] * 100	) + 1
       __lowerCamelCase   =						10 if child_n >= 10 else child_n
       for _ in range(A__	):
              __lowerCamelCase   =						population_score[random.randint(0  ,			A__	)][0]

              __lowerCamelCase,   __lowerCamelCase   =						crossover(parent_a[0]  ,			A__	)
              # Append new string to the population list.
              pop.append(mutate(A__  ,			A__	)	)
              pop.append(mutate(A__  ,			A__	)	)
       return pop




def 					lowerCamelCase__       (						A__       :					str  ,			A__       :					list[str]  ,			A__       :					bool = True	):





       '''simple docstring'''



       if N_POPULATION < N_SELECTED:
              __lowerCamelCase   =						f'{N_POPULATION} must be bigger than {N_SELECTED}'
              raise ValueError(A__	)
       # Verify that the target contains no genes besides the ones inside genes variable.
       __lowerCamelCase   =						sorted({c for c in target if c not in genes}	)
       if not_in_genes_list:
              __lowerCamelCase   =						f'{not_in_genes_list} is not in genes list, evolution cannot converge'
              raise ValueError(A__	)

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

       # Just some logs to know what the algorithms is doing.
       __lowerCamelCase,   __lowerCamelCase   =						0, 0

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

              # 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   =						[evaluate(A__  ,			A__	) for item in population]

              # Check if there is a matching evolution.
              __lowerCamelCase   =						sorted(A__  ,			key=lambda A__	: x[1]  ,			reverse=A__	)
              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 % 10 == 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   =						population[: int(N_POPULATION / 3	)]
              population.clear()
              population.extend(A__	)
              # Normalize population score to be between 0 and 1.
              __lowerCamelCase   =						[
                  (item, score / len(A__	)) for item, score in population_score
              ]

              # This is selection
              for i in range(A__	):
                     population.extend(select(population_score[int(A__	)]  ,			A__  ,			A__	)	)
                     # 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(A__	) > 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}"""
       )







import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__:
      def __init__(				self: Any    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						question_encoder
             __lowerCamelCase   =						generator
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[Any]   ):
             if os.path.isfile(UpperCamelCase_   ):
                    raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'   )
             os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """question_encoder_tokenizer"""   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """generator_tokenizer"""   )
             self.question_encoder.save_pretrained(UpperCamelCase_   )
             self.generator.save_pretrained(UpperCamelCase_   )
      @classmethod
      def 			lowerCAmelCase__						(				cls: List[Any]    ,       UpperCamelCase_: Dict    ,       **UpperCamelCase_: Union[str, Any]   ):
             # dynamically import AutoTokenizer
             from ..auto.tokenization_auto import AutoTokenizer

             __lowerCamelCase   =						kwargs.pop("""config"""    ,       UpperCamelCase_   )

             if config is None:
                    __lowerCamelCase   =						RagConfig.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.question_encoder    ,       subfolder="""question_encoder_tokenizer"""   )
             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.generator    ,       subfolder="""generator_tokenizer"""   )
             return cls(question_encoder=UpperCamelCase_    ,       generator=UpperCamelCase_   )
      def __call__(				self: Tuple    ,       *UpperCamelCase_: int    ,       **UpperCamelCase_: int   ):
             return self.current_tokenizer(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       *UpperCamelCase_: List[Any]    ,       **UpperCamelCase_: List[Any]   ):
             return self.generator.batch_decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       *UpperCamelCase_: str    ,       **UpperCamelCase_: Union[str, Any]   ):
             return self.generator.decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.generator




      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Optional[List[str]] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: str = "longest"    ,       UpperCamelCase_: str = None    ,       UpperCamelCase_: bool = True    ,       **UpperCamelCase_: int    ,       ):
             warnings.warn(
                 """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
                 """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
                 """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
                 """details"""    ,       UpperCamelCase_    ,       )
             if max_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             if tgt_texts is None:
                    return model_inputs
             # Process tgt_texts
             if max_target_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 text_target=UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             __lowerCamelCase   =						labels["""input_ids"""]
             return model_inputs

