



"""simple docstring"""


from __future__ import annotations

import random

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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						list[str]				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						tuple[str, float]					,     _SCREAMING_SNAKE_CASE   :						list[tuple[str, float]]					,     _SCREAMING_SNAKE_CASE   :						list[str]					,     ):






				'''simple docstring'''



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

								_UpperCAmelCase					=    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  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						list[str]					,     _SCREAMING_SNAKE_CASE   :						bool = True				):






				'''simple docstring'''



				if N_POPULATION < N_SELECTED:
								_UpperCAmelCase					=    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.
				_UpperCAmelCase					=    sorted({c for c in target if c not in genes}				)
				if not_in_genes_list:
								_UpperCAmelCase					=    f'{not_in_genes_list} is not in genes list, evolution cannot converge'
								raise ValueError(_SCREAMING_SNAKE_CASE				)

				# Generate random starting population.
				_UpperCAmelCase					=    []
				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.
				_UpperCAmelCase					=    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.
								_UpperCAmelCase					=    [evaluate(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) for item in population]

								# Check if there is a matching evolution.
								_UpperCAmelCase					=    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.
								_UpperCAmelCase					=    population[: int(N_POPULATION / 3				)]
								population.clear()
								population.extend(_SCREAMING_SNAKE_CASE				)
								# Normalize population score to be between 0 and 1.
								_UpperCAmelCase					=    [
								    (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__":
						__A   :							Optional[Any]										=   (
						    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
						)
						__A   :							str										=   list(
						    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
						    "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
						)
						__A   :							Dict										=   basic(target_str, genes_list)
						print(
						    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
						)








"""simple docstring"""


from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A   :							Any										=   logging.get_logger(__name__)

__A   :							Dict										=   {
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""informer"""
			UpperCamelCase__			      =	{
			    """hidden_size""": """d_model""",
			    """num_attention_heads""": """encoder_attention_heads""",
			    """num_hidden_layers""": """encoder_layers""",
			}



			def __init__(		self     :    List[str]				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    str = "student_t"				,   __UpperCamelCase     :    str = "nll"				,   __UpperCamelCase     :    int = 1				,   __UpperCamelCase     :    List[int] = None				,   __UpperCamelCase     :    Optional[Union[str, bool]] = "mean"				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    Optional[List[int]] = None				,   __UpperCamelCase     :    Optional[List[int]] = None				,   __UpperCamelCase     :    int = 6_4				,   __UpperCamelCase     :    int = 3_2				,   __UpperCamelCase     :    int = 3_2				,   __UpperCamelCase     :    int = 2				,   __UpperCamelCase     :    int = 2				,   __UpperCamelCase     :    int = 2				,   __UpperCamelCase     :    int = 2				,   __UpperCamelCase     :    bool = True				,   __UpperCamelCase     :    str = "gelu"				,   __UpperCamelCase     :    float = 0.0_5				,   __UpperCamelCase     :    float = 0.1				,   __UpperCamelCase     :    float = 0.1				,   __UpperCamelCase     :    float = 0.1				,   __UpperCamelCase     :    float = 0.1				,   __UpperCamelCase     :    int = 1_0_0				,   __UpperCamelCase     :    float = 0.0_2				,   __UpperCamelCase     :    Optional[Any]=True				,   __UpperCamelCase     :    str = "prob"				,   __UpperCamelCase     :    int = 5				,   __UpperCamelCase     :    bool = True				,   **__UpperCamelCase     :    Union[str, Any]				,   )->Dict:
							# time series specific configuration
							_UpperCAmelCase					=    prediction_length
							_UpperCAmelCase					=    context_length or prediction_length
							_UpperCAmelCase					=    distribution_output
							_UpperCAmelCase					=    loss
							_UpperCAmelCase					=    input_size
							_UpperCAmelCase					=    num_time_features
							_UpperCAmelCase					=    lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
							_UpperCAmelCase					=    scaling
							_UpperCAmelCase					=    num_dynamic_real_features
							_UpperCAmelCase					=    num_static_real_features
							_UpperCAmelCase					=    num_static_categorical_features

							# set cardinality
							if cardinality and num_static_categorical_features > 0:
											if len(__UpperCamelCase   ) != num_static_categorical_features:
															raise ValueError(
															    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''   )
											_UpperCAmelCase					=    cardinality
							else:
											_UpperCAmelCase					=    [0]

							# set embedding_dimension
							if embedding_dimension and num_static_categorical_features > 0:
											if len(__UpperCamelCase   ) != num_static_categorical_features:
															raise ValueError(
															    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''   )
											_UpperCAmelCase					=    embedding_dimension
							else:
											_UpperCAmelCase					=    [min(5_0				,   (cat + 1) // 2   ) for cat in self.cardinality]

							_UpperCAmelCase					=    num_parallel_samples

							# Transformer architecture configuration
							_UpperCAmelCase					=    input_size * len(self.lags_sequence   ) + self._number_of_features
							_UpperCAmelCase					=    d_model
							_UpperCAmelCase					=    encoder_attention_heads
							_UpperCAmelCase					=    decoder_attention_heads
							_UpperCAmelCase					=    encoder_ffn_dim
							_UpperCAmelCase					=    decoder_ffn_dim
							_UpperCAmelCase					=    encoder_layers
							_UpperCAmelCase					=    decoder_layers

							_UpperCAmelCase					=    dropout
							_UpperCAmelCase					=    attention_dropout
							_UpperCAmelCase					=    activation_dropout
							_UpperCAmelCase					=    encoder_layerdrop
							_UpperCAmelCase					=    decoder_layerdrop

							_UpperCAmelCase					=    activation_function
							_UpperCAmelCase					=    init_std

							_UpperCAmelCase					=    use_cache

							# Informer
							_UpperCAmelCase					=    attention_type
							_UpperCAmelCase					=    sampling_factor
							_UpperCAmelCase					=    distil

							super().__init__(is_encoder_decoder=__UpperCamelCase				,   **__UpperCamelCase   )



			@property
			def  lowercase__						(		self     :    Optional[int]   )->int:
							return (
							    sum(self.embedding_dimension   )
							    + self.num_dynamic_real_features
							    + self.num_time_features
							    + self.num_static_real_features
							    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
							)



