


from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCAmelCase__  :Optional[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.
lowerCAmelCase__  :str             =							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.
lowerCAmelCase__  :List[str]             =							0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))

def 	lowerCAmelCase__			(      a__:		str   ,  a__:		str	)  ->		tuple[str, float]:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		str   ,  a__:		str	)  ->		tuple[str, str]:


							'''simple docstring'''





							_UpperCAmelCase					=      random.randint(0   ,  len(_lowercase	) - 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 	lowerCAmelCase__			(      a__:		str   ,  a__:		list[str]	)  ->		str:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		tuple[str, float]   ,  a__:		list[tuple[str, float]]   ,  a__:		list[str]   ,  )  ->		list[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							# Generate more children proportionally to the fitness score.
							_UpperCAmelCase					=      int(parent_a[1] * 1_0_0	) + 1
							_UpperCAmelCase					=      1_0 if child_n >= 1_0 else child_n
							for _ in range(_lowercase	):
														_UpperCAmelCase					=      population_score[random.randint(0   ,  _lowercase	)][0]

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

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


							'''simple docstring'''





							if N_POPULATION < N_SELECTED:
														_UpperCAmelCase					=      F'''{N_POPULATION} must be bigger than {N_SELECTED}'''
														raise ValueError(_lowercase	)
							# 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(_lowercase	)

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

							# 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(_lowercase	)

														# 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(_lowercase   ,  _lowercase	) for item in population]

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

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


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




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 		__a				(    pl.LightningModule      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      model
									_UpperCAmelCase					=      2
									_UpperCAmelCase					=      nn.Linear(self.model.config.hidden_size      ,   self.num_labels						)






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									pass



def 	lowerCAmelCase__			(      a__:		str      ,  a__:		str      ,  a__:		str	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      LongformerModel.from_pretrained(a__	)
							_UpperCAmelCase					=      LightningModel(a__	)

							_UpperCAmelCase					=      torch.load(a__      ,  map_location=torch.device('cpu'	)	)
							lightning_model.load_state_dict(ckpt['state_dict']	)

							# init longformer question answering model
							_UpperCAmelCase					=      LongformerForQuestionAnswering.from_pretrained(a__	)

							# transfer weights
							longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()	)
							longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()	)
							longformer_for_qa.eval()

							# save model
							longformer_for_qa.save_pretrained(a__	)

							print(F'''Conversion successful. Model saved under {pytorch_dump_folder_path}'''	)


if __name__ == "__main__":
		lowerCAmelCase__  :Optional[int]             =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--longformer_model''',
		    default=None,
		    type=str,
		    required=True,
		    help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
		)
		parser.add_argument(
		    '''--longformer_question_answering_ckpt_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch Lightning Checkpoint.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowerCAmelCase__  :Dict             =							parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)
