"""simple docstring"""




from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
_a      :		Union[str, Any]           =   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.
_a      :		List[Any]           =   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.
_a      :		List[Any]           =   0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       str	)       ->      tuple[str, float]:
  _lowerCAmelCase						:       List[Any]  			=     len([g for position, g in enumerate(_lowerCamelCase	) if g == main_target[position]]	)
  return (item, float(_lowerCamelCase	))





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       str	)       ->      tuple[str, str]:
  _lowerCAmelCase						:       Dict  			=     random.randint(0			,len(_lowerCamelCase	) - 1	)
  _lowerCAmelCase						:       Tuple  			=     parent_a[:random_slice] + parent_a[random_slice:]
  _lowerCAmelCase						:       Optional[int]  			=     parent_a[:random_slice] + parent_a[random_slice:]
  return (child_a, child_a)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       list[str]	)       ->      str:
  _lowerCAmelCase						:       Optional[Any]  			=     list(_lowerCamelCase	)
  if random.uniform(0			,1	) < MUTATION_PROBABILITY:
    _lowerCAmelCase						:       Optional[int]  			=     random.choice(_lowerCamelCase	)
  return "".join(_lowerCamelCase	)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       tuple[str, float]			,_lowerCamelCase						:       list[tuple[str, float]]			,_lowerCamelCase						:       list[str]			,)       ->      list[str]:
  _lowerCAmelCase						:       Optional[Any]  			=     []
  # Generate more children proportionally to the fitness score.
  _lowerCAmelCase						:       Optional[Any]  			=     int(parent_a[1] * 100	) + 1
  _lowerCAmelCase						:       Dict  			=     10 if child_n >= 10 else child_n
  for _ in range(_lowerCamelCase	):
    _lowerCAmelCase						:       Union[str, Any]  			=     population_score[random.randint(0			,_lowerCamelCase	)][0]

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





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       list[str]			,_lowerCamelCase						:       bool = True	)       ->      tuple[int, int, str]:

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

  # Generate random starting population.
  _lowerCAmelCase						:       Optional[int]  			=     []
  for _ in range(_lowerCamelCase	):
    population.append("""""".join([random.choice(_lowerCamelCase	) for i in range(len(_lowerCamelCase	)	)]	)	)

  # 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(_lowerCamelCase	)

    # 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						:       Dict  			=     [evaluate(_lowerCamelCase			,_lowerCamelCase	) for item in population]

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

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


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





"""simple docstring"""




from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_a      :		Dict           =   logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_		)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     *a__				,     **a__  ):
       super().__init__(*a__				,     **a__  )
       requires_backends(self				,     """vision"""  )
       self.check_model_type(
           TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING  )






     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None  ):
       _lowerCAmelCase						:       List[str]  			=     {}
       _lowerCAmelCase						:       Union[str, Any]  			=     {}

       if prompt is not None:
         _lowerCAmelCase						:       List[Any]  			=     prompt

       if generate_kwargs is not None:
         _lowerCAmelCase						:       List[str]  			=     generate_kwargs
       if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
           _lowerCAmelCase						:       str  			=     {}
         if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
           raise ValueError(
               """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
               """ please use only one"""  )
         _lowerCAmelCase						:       Optional[Any]  			=     max_new_tokens
       return preprocess_params, forward_kwargs, {}






     def __call__(		self				,     a__				,     **a__  ):
       return super().__call__(a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__=None  ):
       _lowerCAmelCase						:       Tuple  			=     load_image(a__  )

       if prompt is not None:
         if not isinstance(a__				,     a__  ):
           raise ValueError(
               F"Received an invalid text input, got - {type(a__  )} - but expected a single string. "
               """Note also that one single text can be provided for conditional image to text generation."""  )

         _lowerCAmelCase						:       Optional[int]  			=     self.model.config.model_type

         if model_type == "git":
           _lowerCAmelCase						:       Optional[Any]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       List[str]  			=     self.tokenizer(text=a__				,     add_special_tokens=a__  ).input_ids
           _lowerCAmelCase						:       Union[str, Any]  			=     [self.tokenizer.cls_token_id] + input_ids
           _lowerCAmelCase						:       Dict  			=     torch.tensor(a__  ).unsqueeze(0  )
           model_inputs.update({"""input_ids""": input_ids}  )

         elif model_type == "pix2struct":
           _lowerCAmelCase						:       Tuple  			=     self.image_processor(images=a__				,     header_text=a__				,     return_tensors=self.framework  )

         elif model_type != "vision-encoder-decoder":
           # vision-encoder-decoder does not support conditional generation
           _lowerCAmelCase						:       Optional[int]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       Optional[int]  			=     self.tokenizer(a__				,     return_tensors=self.framework  )
           model_inputs.update(a__  )

         else:
           raise ValueError(F"Model type {model_type} does not support conditional text generation"  )

       else:
         _lowerCAmelCase						:       Any  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )

       if self.model.config.model_type == "git" and prompt is None:
         _lowerCAmelCase						:       Union[str, Any]  			=     None

       return model_inputs






     def     __A  (		self				,     a__				,     a__=None  ):
       # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
       # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
       if (
           "input_ids" in model_inputs
           and isinstance(model_inputs["""input_ids"""]				,     a__  )
           and all(x is None for x in model_inputs["""input_ids"""]  )
       ):
         _lowerCAmelCase						:       Optional[int]  			=     None

       if generate_kwargs is None:
         _lowerCAmelCase						:       List[str]  			=     {}
       # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
       #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
       #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
       #  in the `_prepare_model_inputs` method.
       _lowerCAmelCase						:       Tuple  			=     model_inputs.pop(self.model.main_input_name  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.model.generate(a__				,     **a__				,     **a__  )
       return model_outputs






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     []
       for output_ids in model_outputs:
         _lowerCAmelCase						:       Any  			=     {
             """generated_text""": self.tokenizer.decode(
                 a__				,     skip_special_tokens=a__				,     )
         }
         records.append(a__  )
       return records




