





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
UpperCAmelCase	:	str		     =       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	:	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.
UpperCAmelCase	:	Optional[int]		     =       0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))
def      _A       (		SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



  a__	:					Dict										=len([g for position, g in enumerate(SCREAMING_SNAKE_CASE__						) if g == main_target[position]]						)
  return (item, float(SCREAMING_SNAKE_CASE__						))
def      _A       (		SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



  a__	:					Dict										=random.randint(0       ,       len(SCREAMING_SNAKE_CASE__						) - 1						)
  a__	:					Tuple										=parent_a[:random_slice] + parent_a[random_slice:]
  a__	:					str										=parent_a[:random_slice] + parent_a[random_slice:]
  return (child_a, child_a)
def      _A       (		SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   list[str]						):





  """simple docstring"""



  a__	:					str										=list(SCREAMING_SNAKE_CASE__						)
  if random.uniform(0       ,       1						) < MUTATION_PROBABILITY:
    a__	:					Union[str, Any]										=random.choice(SCREAMING_SNAKE_CASE__						)
  return "".join(SCREAMING_SNAKE_CASE__						)
def      _A       (		SCREAMING_SNAKE_CASE     :   tuple[str, float]       ,       SCREAMING_SNAKE_CASE     :   list[tuple[str, float]]       ,       SCREAMING_SNAKE_CASE     :   list[str]       ,       ):





  """simple docstring"""



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

    a__					,							a__	:					int										=crossover(parent_a[0]       ,       SCREAMING_SNAKE_CASE__						)
    # Append new string to the population list.
    pop.append(mutate(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__						)						)
    pop.append(mutate(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__						)						)
  return pop
def      _A       (		SCREAMING_SNAKE_CASE     :   str       ,       SCREAMING_SNAKE_CASE     :   list[str]       ,       SCREAMING_SNAKE_CASE     :   bool = True						):





  """simple docstring"""



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

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

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

  # This loop will end when we find a perfect match for our target.
  while True:
    generation += 1
    total_population += len(SCREAMING_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.
    a__	:					Union[str, Any]										=[evaluate(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__						) for item in population]

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

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


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










import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "instructblip_vision_model"






    def __init__(			self					,   A_=1408					,   A_=6144					,   A_=39					,   A_=16					,   A_=224					,   A_=14					,   A_="gelu"					,   A_=1E-6					,   A_=0.0					,   A_=1E-10					,   A_=True					,   **A_					,   )  ->       Tuple:
         super().__init__(**A_    )

         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=qkv_bias






    @classmethod
    def _a					(			cls					,   A_					,   **A_    )  ->       "PretrainedConfig":
         cls._set_token_in_kwargs(A_    )

         __UpperCamelCase							,      __UpperCamelCase    							=cls.get_config_dict(A_					,   **A_    )

         # get the vision config dict if we are loading from InstructBlipConfig
         if config_dict.get('model_type'    ) == "instructblip":
              __UpperCamelCase    							=config_dict['vision_config']

         if "model_type" in config_dict and hasattr(cls					,   'model_type'    ) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                  f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

         return cls.from_dict(A_					,   **A_    )






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]      =  "instructblip_qformer"






    def __init__(			self					,   A_=30522					,   A_=768					,   A_=12					,   A_=12					,   A_=3072					,   A_="gelu"					,   A_=0.1					,   A_=0.1					,   A_=512					,   A_=0.02					,   A_=1E-12					,   A_=0					,   A_="absolute"					,   A_=2					,   A_=1408					,   **A_					,   )  ->       Optional[Any]:
         super().__init__(pad_token_id=A_					,   **A_    )

         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=position_embedding_type
         __UpperCamelCase    							=cross_attention_frequency
         __UpperCamelCase    							=encoder_hidden_size






    @classmethod
    def _a					(			cls					,   A_					,   **A_    )  ->       "PretrainedConfig":
         cls._set_token_in_kwargs(A_    )

         __UpperCamelCase							,      __UpperCamelCase    							=cls.get_config_dict(A_					,   **A_    )

         # get the qformer config dict if we are loading from InstructBlipConfig
         if config_dict.get('model_type'    ) == "instructblip":
              __UpperCamelCase    							=config_dict['qformer_config']

         if "model_type" in config_dict and hasattr(cls					,   'model_type'    ) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                  f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

         return cls.from_dict(A_					,   **A_    )






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "instructblip"
    UpperCAmelCase__						:		Optional[Any]      =  True






    def __init__(			self					,   A_=None					,   A_=None					,   A_=None					,   A_=32					,   **A_    )  ->       List[str]:
         super().__init__(**A_    )

         if vision_config is None:
              __UpperCamelCase    							={}
              logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'    )

         if qformer_config is None:
              __UpperCamelCase    							={}
              logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'    )

         if text_config is None:
              __UpperCamelCase    							={}
              logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'    )

         __UpperCamelCase    							=InstructBlipVisionConfig(**A_    )
         __UpperCamelCase    							=InstructBlipQFormerConfig(**A_    )
         __UpperCamelCase    							=text_config['model_type'] if 'model_type' in text_config else 'opt'
         __UpperCamelCase    							=CONFIG_MAPPING[text_model_type](**A_    )

         __UpperCamelCase    							=self.text_config.tie_word_embeddings
         __UpperCamelCase    							=self.text_config.is_encoder_decoder

         __UpperCamelCase    							=num_query_tokens
         __UpperCamelCase    							=self.vision_config.hidden_size
         __UpperCamelCase    							=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         __UpperCamelCase    							=1.0
         __UpperCamelCase    							=0.02






    @classmethod
    def _a					(			cls					,   A_					,   A_					,   A_					,   **A_					,   )  ->       Optional[Any]:
         return cls(
             vision_config=vision_config.to_dict()					,   qformer_config=qformer_config.to_dict()					,   text_config=text_config.to_dict()					,   **A_					,   )






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=copy.deepcopy(self.__dict__    )
         __UpperCamelCase    							=self.vision_config.to_dict()
         __UpperCamelCase    							=self.qformer_config.to_dict()
         __UpperCamelCase    							=self.text_config.to_dict()
         __UpperCamelCase    							=self.__class__.model_type
         return output





