






"""simple docstring"""





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCamelCase							=  2_00
# 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.
__lowerCamelCase							=  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.
__lowerCamelCase							=  0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str  )    ->							tuple[str, float]:
						__magic_name__:	Any							=       len([g for position, g in enumerate(__UpperCAmelCase  ) if g == main_target[position]]  )
						return (item, float(__UpperCAmelCase  ))



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    str  )    ->							tuple[str, str]:
						__magic_name__:	Tuple							=       random.randint(0      ,   len(__UpperCAmelCase  ) - 1  )
						__magic_name__:	Optional[Any]							=       parent_a[:random_slice] + parent_a[random_slice:]
						__magic_name__:	Optional[Any]							=       parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)



def   a    (     __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    list[str]  )    ->							str:
						__magic_name__:	Union[str, Any]							=       list(__UpperCAmelCase  )
						if random.uniform(0      ,   1  ) < MUTATION_PROBABILITY:
												__magic_name__:	Any							=       random.choice(__UpperCAmelCase  )
						return "".join(__UpperCAmelCase  )



def   a    (     __UpperCAmelCase      :    tuple[str, float]      ,   __UpperCAmelCase      :    list[tuple[str, float]]      ,   __UpperCAmelCase      :    list[str]      ,   )    ->							list[str]:
						__magic_name__:	Union[str, Any]							=       []
						# Generate more children proportionally to the fitness score.
						__magic_name__:	List[str]							=       int(parent_a[1] * 1_0_0  ) + 1
						__magic_name__:	Union[str, Any]							=       1_0 if child_n >= 1_0 else child_n
						for _ in range(__UpperCAmelCase  ):
												__magic_name__:	Union[str, Any]							=       population_score[random.randint(0      ,   __UpperCAmelCase  )][0]

												__magic_name__,			__magic_name__:	List[str]							=       crossover(parent_a[0]      ,   __UpperCAmelCase  )
												# Append new string to the population list.
												pop.append(mutate(__UpperCAmelCase      ,   __UpperCAmelCase  )  )
												pop.append(mutate(__UpperCAmelCase      ,   __UpperCAmelCase  )  )
						return pop



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

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

						# Generate random starting population.
						__magic_name__:	Optional[Any]							=       []
						for _ in range(__UpperCAmelCase  ):
												population.append("""""".join([random.choice(__UpperCAmelCase  ) for i in range(len(__UpperCAmelCase  )  )]  )  )

						# Just some logs to know what the algorithms is doing.
						__magic_name__,			__magic_name__:	Optional[int]							=       0, 0

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

												# 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.
												__magic_name__:	Tuple							=       [evaluate(__UpperCAmelCase      ,   __UpperCAmelCase  ) for item in population]

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

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


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







import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_lowercase							:   Union[str, Any]						=						{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class    _UpperCamelCase     (       nn.Module					):

				"""simple docstring"""







				def __init__(    self					, a__ )						->					Dict:
					super().__init__()
					A             =					torchvision.models.resnetaaa(pretrained=a__ )
					A             =					list(model.children() )[:-2]
					A             =					nn.Sequential(*a__ )
					A             =					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )

				def   _UpperCAmelCase       (    self					, a__ )						->					List[str]:
					# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
					A             =					self.pool(self.model(a__ ) )
					A             =					torch.flatten(a__					, start_dim=2 )
					A             =					out.transpose(1					, 2 ).contiguous()
					return out  # BxNx2048






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, a__					, a__					, a__ )						->					Union[str, Any]:
					A             =					[json.loads(a__ ) for l in open(a__ )]
					A             =					os.path.dirname(a__ )
					A             =					tokenizer
					A             =					labels
					A             =					len(a__ )
					A             =					max_seq_length

					A             =					transforms

				def __len__(    self )						->					Tuple:
					return len(self.data )

				def __getitem__(    self					, a__ )						->					Optional[int]:
					A             =					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					, add_special_tokens=a__ ) )
					A       ,      A       ,      A             =					sentence[0], sentence[1:-1], sentence[-1]
					A             =					sentence[: self.max_seq_length]

					A             =					torch.zeros(self.n_classes )
					A             =					1

					A             =					Image.open(os.path.join(self.data_dir					, self.data[index]["""img"""] ) ).convert("""RGB""" )
					A             =					self.transforms(a__ )

					return {
					    "image_start_token": start_token,
					    "image_end_token": end_token,
					    "sentence": sentence,
					    "image": image,
					    "label": label,
					}

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					Counter()
					for row in self.data:
						label_freqs.update(row["""label"""] )
					return label_freqs





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> List[str]:





	"""simple docstring"""







	A             =					[len(row["""sentence"""]    ) for row in batch]
	A       ,      A             =					len(UpperCamelCase__    ), max(UpperCamelCase__    )

	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )
	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )

	for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__				,       UpperCamelCase__    )    ):
		A             =					input_row["""sentence"""]
		A             =					1

	A             =					torch.stack([row["""image"""] for row in batch]    )
	A             =					torch.stack([row["""label"""] for row in batch]    )
	A             =					torch.stack([row["""image_start_token"""] for row in batch]    )
	A             =					torch.stack([row["""image_end_token"""] for row in batch]    )

	return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





def 	_lowerCAmelCase					( )			-> Union[str, Any]:





	"""simple docstring"""







	return [
	    "Crime",
	    "Drama",
	    "Thriller",
	    "Action",
	    "Comedy",
	    "Romance",
	    "Documentary",
	    "Short",
	    "Mystery",
	    "History",
	    "Family",
	    "Adventure",
	    "Fantasy",
	    "Sci-Fi",
	    "Western",
	    "Horror",
	    "Sport",
	    "War",
	    "Music",
	    "Musical",
	    "Animation",
	    "Biography",
	    "Film-Noir",
	]





def 	_lowerCAmelCase					( )			-> Optional[Any]:





	"""simple docstring"""







	return transforms.Compose(
	    [
	        transforms.Resize(2_56    ),
	        transforms.CenterCrop(2_24    ),
	        transforms.ToTensor(),
	        transforms.Normalize(
	            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]				,       std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]				,       ),
	    ]    )



