




import math
class 				__snake_case     :



				def __init__(					self					:						Optional[Any]						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						Optional[Any]					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					x
								_lowerCAmelCase			:			int        =					y
								_lowerCAmelCase			:			Any        =					step_size
								_lowerCAmelCase			:			Optional[int]        =					function_to_optimize



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			int:





								'''simple docstring'''




								return self.function(self.x						,		self.y					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			Dict:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					self.step_size
								return [
								    SearchProblem(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase						,		self.function					)
								    for x, y in (
								        (self.x - step_size, self.y - step_size),
								        (self.x - step_size, self.y),
								        (self.x - step_size, self.y + step_size),
								        (self.x, self.y - step_size),
								        (self.x, self.y + step_size),
								        (self.x + step_size, self.y - step_size),
								        (self.x + step_size, self.y),
								        (self.x + step_size, self.y + step_size),
								    )
								]



				def __hash__(					self					:						str					)     ->			Optional[int]:





								'''simple docstring'''




								return hash(str(self					)					)



				def __eq__(					self					:						Tuple						,		_UpperCAmelCase					:						int					)     ->			Union[str, Any]:





								'''simple docstring'''




								if isinstance(_UpperCAmelCase						,		_UpperCAmelCase					):
												return hash(str(self					)					) == hash(str(_UpperCAmelCase					)					)
								return False



				def __str__(					self					:						Optional[Any]					)     ->			int:





								'''simple docstring'''




								return f"x: {self.x} y: {self.y}"






def 							_UpperCAmelCase     (UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       bool = True					,			UpperCamelCase_       :       float = math.inf					,			UpperCamelCase_       :       float = -math.inf					,			UpperCamelCase_       :       float = math.inf					,			UpperCamelCase_       :       float = -math.inf					,			UpperCamelCase_       :       bool = False					,			UpperCamelCase_       :       int = 10000					,			):
				'''simple docstring'''
				_lowerCAmelCase			:			int        =					search_prob
				_lowerCAmelCase			:			int        =					[]  # list to store the current score at each iteration
				_lowerCAmelCase			:			Optional[Any]        =					0
				_lowerCAmelCase			:			Dict        =					False
				_lowerCAmelCase			:			Optional[Any]        =					set()
				while not solution_found and iterations < max_iter:
								visited.add(UpperCamelCase_				)
								iterations += 1
								_lowerCAmelCase			:			Optional[Any]        =					current_state.score()
								scores.append(UpperCamelCase_				)
								_lowerCAmelCase			:			str        =					current_state.get_neighbors()
								_lowerCAmelCase			:			int        =					-math.inf
								_lowerCAmelCase			:			Union[str, Any]        =					math.inf
								_lowerCAmelCase			:			int        =					None  # to hold the next best neighbor
								for neighbor in neighbors:
												if neighbor in visited:
																continue  # do not want to visit the same state again
												if (
												    neighbor.x > max_x
												    or neighbor.x < min_x
												    or neighbor.y > max_y
												    or neighbor.y < min_y
												):
																continue  # neighbor outside our bounds
												_lowerCAmelCase			:			Optional[int]        =					neighbor.score() - current_score
												if find_max:  # finding max
																# going to direction with greatest ascent
																if change > max_change and change > 0:
																				_lowerCAmelCase			:			int        =					change
																				_lowerCAmelCase			:			int        =					neighbor
												else:  # finding min
																# to direction with greatest descent
																if change < min_change and change < 0:
																				_lowerCAmelCase			:			Tuple        =					change
																				_lowerCAmelCase			:			List[str]        =					neighbor
								if next_state is not None:
												# we found at least one neighbor which improved the current state
												_lowerCAmelCase			:			str        =					next_state
								else:
												# since we have no neighbor that improves the solution we stop the search
												_lowerCAmelCase			:			str        =					True

				if visualization:
								from matplotlib import pyplot as plt

								plt.plot(range(UpperCamelCase_				)					,			UpperCamelCase_				)
								plt.xlabel("""Iterations"""				)
								plt.ylabel("""Function values"""				)
								plt.show()

				return current_state


if __name__ == "__main__":
	import doctest

	doctest.testmod()




	def 							_UpperCAmelCase     (UpperCamelCase_       :       str					,			UpperCamelCase_       :       Dict				):
					'''simple docstring'''
					return (x**2) + (y**2)

	# starting the problem with initial coordinates (3, 4)
	_lowerCamelCase					:     List[Any]					  =  SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	_lowerCamelCase					:     Optional[Any]					  =  hill_climbing(prob, find_max=False)
	print(
	    "The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
	    F'''{local_min.score()}'''
	)

	# starting the problem with initial coordinates (12, 47)
	_lowerCamelCase					:     Optional[int]					  =  SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
	_lowerCamelCase					:     Optional[Any]					  =  hill_climbing(
	    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
	)
	print(
	    "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
	    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
	)




	def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[int]					,			UpperCamelCase_       :       str				):
					'''simple docstring'''
					return (3 * x**2) - (6 * y)

	_lowerCamelCase					:     Dict					  =  SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	_lowerCamelCase					:     int					  =  hill_climbing(prob, find_max=True)





	print(
	    "The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
	    F'''{local_min.score()}'''
	)







import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Tuple					  =  ["UtteranceLevel"]




@torch.no_grad()
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			List[str]        =					torch.load(UpperCamelCase_					,			map_location="""cpu"""				)
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"				)

				_lowerCAmelCase			:			List[str]        =					checkpoint["""Downstream"""]

				_lowerCAmelCase			:			Optional[int]        =					HubertConfig.from_pretrained(UpperCamelCase_				)
				_lowerCAmelCase			:			str        =					HubertForSequenceClassification.from_pretrained(UpperCamelCase_					,			config=UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					WavaVecaFeatureExtractor.from_pretrained(
				    UpperCamelCase_					,			return_attention_mask=UpperCamelCase_					,			do_normalize=UpperCamelCase_				)

				if hf_congfig.use_weighted_layer_sum:
								_lowerCAmelCase			:			int        =					checkpoint["""Featurizer"""]["""weights"""]

				_lowerCAmelCase			:			Optional[int]        =					downstream_dict["""projector.weight"""]
				_lowerCAmelCase			:			int        =					downstream_dict["""projector.bias"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.weight"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.bias"""]

				hf_feature_extractor.save_pretrained(UpperCamelCase_				)
				hf_model.save_pretrained(UpperCamelCase_				)


if __name__ == "__main__":
	_lowerCamelCase					:     Any					  =  argparse.ArgumentParser()
	parser.add_argument(
	    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
	)
	parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
	parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
	parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
	_lowerCamelCase					:     Dict					  =  parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

