
"""simple docstring"""






import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






def 						_snake_case       (	lowercase__		:  Tuple		)  ->				Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_    :Union[str, Any]			     =  args.pruning_method
			lowerCAmelCase_    :Tuple			     =  args.threshold

			lowerCAmelCase_    :int			     =  args.model_name_or_path.rstrip("""/"""		)
			lowerCAmelCase_    :Dict			     =  args.target_model_path

			print(f"""Load fine-pruned model from {model_name_or_path}"""		)
			lowerCAmelCase_    :Optional[int]			     =  torch.load(os.path.join(lowercase__      ,     """pytorch_model.bin"""		)		)
			lowerCAmelCase_    :Union[str, Any]			     =  {}

			for name, tensor in model.items():
						if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
									lowerCAmelCase_    :Tuple			     =  tensor
									print(f"""Copied layer {name}"""		)
						elif "classifier" in name or "qa_output" in name:
									lowerCAmelCase_    :Any			     =  tensor
									print(f"""Copied layer {name}"""		)
						elif "bias" in name:
									lowerCAmelCase_    :List[str]			     =  tensor
									print(f"""Copied layer {name}"""		)
						else:
									if pruning_method == "magnitude":
												lowerCAmelCase_    :Any			     =  MagnitudeBinarizer.apply(inputs=lowercase__      ,     threshold=lowercase__		)
												lowerCAmelCase_    :str			     =  tensor * mask
												print(f"""Pruned layer {name}"""		)
									elif pruning_method == "topK":
												if "mask_scores" in name:
															continue
												lowerCAmelCase_    :str			     =  name[:-6]
												lowerCAmelCase_    :Any			     =  model[f"""{prefix_}mask_scores"""]
												lowerCAmelCase_    :Optional[Any]			     =  TopKBinarizer.apply(lowercase__      ,     lowercase__		)
												lowerCAmelCase_    :int			     =  tensor * mask
												print(f"""Pruned layer {name}"""		)
									elif pruning_method == "sigmoied_threshold":
												if "mask_scores" in name:
															continue
												lowerCAmelCase_    :int			     =  name[:-6]
												lowerCAmelCase_    :Optional[Any]			     =  model[f"""{prefix_}mask_scores"""]
												lowerCAmelCase_    :Union[str, Any]			     =  ThresholdBinarizer.apply(lowercase__      ,     lowercase__      ,     lowercase__		)
												lowerCAmelCase_    :Optional[Any]			     =  tensor * mask
												print(f"""Pruned layer {name}"""		)
									elif pruning_method == "l0":
												if "mask_scores" in name:
															continue
												lowerCAmelCase_    :Dict			     =  name[:-6]
												lowerCAmelCase_    :Any			     =  model[f"""{prefix_}mask_scores"""]
												lowerCAmelCase_     ,       lowerCAmelCase_    :str			     =  -0.1, 1.1
												lowerCAmelCase_    :Optional[int]			     =  torch.sigmoid(lowercase__		)
												lowerCAmelCase_    :Any			     =  s * (r - l) + l
												lowerCAmelCase_    :Dict			     =  s_bar.clamp(min=0.0      ,     max=1.0		)
												lowerCAmelCase_    :Any			     =  tensor * mask
												print(f"""Pruned layer {name}"""		)
									else:
												raise ValueError("""Unknown pruning method"""		)

			if target_model_path is None:
						lowerCAmelCase_    :int			     =  os.path.join(
						    os.path.dirname(lowercase__		)      ,     f"""bertarized_{os.path.basename(lowercase__		)}"""		)

			if not os.path.isdir(lowercase__		):
						shutil.copytree(lowercase__      ,     lowercase__		)
						print(f"""\nCreated folder {target_model_path}"""		)

			torch.save(lowercase__      ,     os.path.join(lowercase__      ,     """pytorch_model.bin"""		)		)
			print("""\nPruned model saved! See you later!"""		)


if __name__ == "__main__":
				__UpperCAmelCase       =  argparse.ArgumentParser()

				parser.add_argument(
				    '--pruning_method',
				    choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
				    type=str,
				    required=True,
				    help=(
				        'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
				        ' sigmoied_threshold = Soft movement pruning)'
				    ),
				)
				parser.add_argument(
				    '--threshold',
				    type=float,
				    required=False,
				    help=(
				        'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
				        'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
				        'Not needed for `l0`'
				    ),
				)
				parser.add_argument(
				    '--model_name_or_path',
				    type=str,
				    required=True,
				    help='Folder containing the model that was previously fine-pruned',
				)
				parser.add_argument(
				    '--target_model_path',
				    default=None,
				    type=str,
				    required=False,
				    help='Folder containing the model that was previously fine-pruned',
				)

				__UpperCAmelCase       =  parser.parse_args()

				main(args)








'''simple docstring'''







from __future__ import annotations

import random

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[str, float]:
	_snake_case   						=		len([g for position, g in enumerate(__A	) if g == main_target[position]]	)
	return (item, float(__A	))







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[str, str]:
	_snake_case   						=		random.randint(0			,				len(__A	) - 1	)
	_snake_case   						=		parent_a[:random_slice] + parent_a[random_slice:]
	_snake_case   						=		parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str:
	_snake_case   						=		list(__A	)
	if random.uniform(0			,				1	) < MUTATION_PROBABILITY:
		_snake_case   						=		random.choice(__A	)
	return "".join(__A	)







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

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A = True	)       ->	tuple[int, int, str]:

	# Verify if N_POPULATION is bigger than N_SELECTED
	if N_POPULATION < N_SELECTED:
		_snake_case   						=		F'{N_POPULATION} must be bigger than {N_SELECTED}'
		raise ValueError(__A	)
	# Verify that the target contains no genes besides the ones inside genes variable.
	_snake_case   						=		sorted({c for c in target if c not in genes}	)
	if not_in_genes_list:
		_snake_case   						=		F'{not_in_genes_list} is not in genes list, evolution cannot converge'
		raise ValueError(__A	)

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

	# Just some logs to know what the algorithms is doing.
	_snake_case						, _snake_case   						=		0, 0

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

		# 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.
		_snake_case   						=		[evaluate(__A			,				__A	) for item in population]

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

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


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

