





"""simple docstring"""


from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
A_	: int      				=2_0_0
# 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.
A_	: int      				=5_0
# 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.
A_	: str      				=0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))





def SCREAMING_SNAKE_CASE_  (			snake_case  :   Dict		,    snake_case  :   Dict						)->						Optional[Any]:
       _lowerCamelCase             =   len([g for position, g in enumerate(snake_case						) if g == main_target[position]]						)
       return (item, float(snake_case						))





def SCREAMING_SNAKE_CASE_  (			snake_case  :   str		,    snake_case  :   int						)->						Any:
       _lowerCamelCase             =   random.randint(0		,    len(snake_case						) - 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 SCREAMING_SNAKE_CASE_  (			snake_case  :   Tuple		,    snake_case  :   str						)->						List[Any]:
       _lowerCamelCase             =   list(snake_case						)
       if random.uniform(0		,    1						) < MUTATION_PROBABILITY:
              _lowerCamelCase             =   random.choice(snake_case						)
       return "".join(snake_case						)





def SCREAMING_SNAKE_CASE_  (			snake_case  :   str		,    snake_case  :   Union[str, Any]		,    snake_case  :   Optional[Any]		,    )->						List[Any]:
       _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(snake_case						):
              _lowerCamelCase             =   population_score[random.randint(0		,    snake_case						)][0]

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





def SCREAMING_SNAKE_CASE_  (			snake_case  :   Optional[int]		,    snake_case  :   Optional[Any]		,    snake_case  :   str = True						)->						Tuple:

       if N_POPULATION < N_SELECTED:
              _lowerCamelCase             =   f'{N_POPULATION} must be bigger than {N_SELECTED}'
              raise ValueError(snake_case						)
       # 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(snake_case						)

       # Generate random starting population.
       _lowerCamelCase             =   []
       for _ in range(snake_case						):
              population.append(''.join([random.choice(snake_case						) for i in range(len(snake_case						)						)]						)						)

       # 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(snake_case						)

              # 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(snake_case		,    snake_case						) for item in population]

              # Check if there is a matching evolution.
              _lowerCamelCase             =   sorted(snake_case		,    key=lambda snake_case						: x[1]		,    reverse=snake_case						)
              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(snake_case						)
              # Normalize population score to be between 0 and 1.
              _lowerCamelCase             =   [
                  (item, score / len(snake_case						)) for item, score in population_score
              ]

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


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













import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      ['image_processor', 'tokenizer']
 __UpperCamelCase       =      'BlipImageProcessor'
 __UpperCamelCase       =      'AutoTokenizer'

 def __init__(  self,		A_,		A_,		A_		)	->       str:
    super().__init__(A_,		A_		)

    # add QFormer tokenizer
    UpperCAmelCase__						=qformer_tokenizer

 def __call__(  self,		A_ = None,		A_ = None,		A_ = True,		A_ = False,		A_ = None,		A_ = None,		A_ = 0,		A_ = None,		A_ = None,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = True,		A_ = None,		**A_,		)	->       BatchFeature:
    if images is None and text is None:
       raise ValueError("You have to specify at least images or text."		)

    UpperCAmelCase__						=BatchFeature()

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       encoding.update(A_		)
       UpperCAmelCase__						=self.qformer_tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       UpperCAmelCase__						=qformer_text_encoding.pop("input_ids"		)
       UpperCAmelCase__						=qformer_text_encoding.pop("attention_mask"		)

    if images is not None:
       UpperCAmelCase__						=self.image_processor(A_,		return_tensors=A_		)
       encoding.update(A_		)

    return encoding

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       List[Any]:
    return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)

 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.tokenizer.model_input_names
    UpperCAmelCase__						=self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)

 def  __UpperCAmelCase						(  self,		A_,		**A_		)	->       str:
    if os.path.isfile(A_		):
       raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
    os.makedirs(A_,		exist_ok=A_		)
    UpperCAmelCase__						=os.path.join(A_,		"qformer_tokenizer"		)
    self.qformer_tokenizer.save_pretrained(A_		)
    return super().save_pretrained(A_,		**A_		)






 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		**A_		)	->       int:
    UpperCAmelCase__						=AutoTokenizer.from_pretrained(A_,		subfolder="qformer_tokenizer"		)
    UpperCAmelCase__						=cls._get_arguments_from_pretrained(A_,		**A_		)
    args.append(A_		)
    return cls(*A_		)



