



import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def      lowerCAmelCase_					(  _lowercase    :       Dict						,       _lowercase    :       bool = True						,       _lowercase    :       float = math.inf						,       _lowercase    :       float = -math.inf						,       _lowercase    :       float = math.inf						,       _lowercase    :       float = -math.inf						,       _lowercase    :       bool = False						,       _lowercase    :       float = 100						,       _lowercase    :       float = 0.01						,       _lowercase    :       float = 1						,       )							->	Any:







			"""simple docstring"""
			a__       :						Any							=  False
			a__       :						Union[str, Any]							=  search_prob
			a__       :						Tuple							=  start_temperate
			a__       :						Any							=  []
			a__       :						Optional[Any]							=  0
			a__       :						Dict							=  None

			while not search_end:
						a__       :						Union[str, Any]							=  current_state.score()
						if best_state is None or current_score > best_state.score():
									a__       :						Tuple							=  current_state
						scores.append(_lowercase)
						iterations += 1
						a__       :						List[Any]							=  None
						a__       :						str							=  current_state.get_neighbors()
						while (
						    next_state is None and neighbors
						):  # till we do not find a neighbor that we can move to
									a__       :						Optional[int]							=  random.randint(0						,       len(_lowercase) - 1)  # picking a random neighbor
									a__       :						Dict							=  neighbors.pop(_lowercase)
									a__       :						Any							=  picked_neighbor.score() - current_score

									if (
									    picked_neighbor.x > max_x
									    or picked_neighbor.x < min_x
									    or picked_neighbor.y > max_y
									    or picked_neighbor.y < min_y
									):
												continue  # neighbor outside our bounds

									if not find_max:
												a__       :						List[Any]							=  change * -1  # in case we are finding minimum
									if change > 0:  # improves the solution
												a__       :						Any							=  picked_neighbor
									else:
												a__       :						str							=  (math.e) ** (
												    change / current_temp
												)  # probability generation function
												if random.random() < probability:  # random number within probability
															a__       :						Optional[Any]							=  picked_neighbor
						a__       :						Optional[int]							=  current_temp - (current_temp * rate_of_decrease)

						if current_temp < threshold_temp or next_state is None:
									# temperature below threshold, or could not find a suitable neighbor
									a__       :						Optional[Any]							=  True
						else:
									a__       :						int							=  next_state

			if visualization:
						from matplotlib import pyplot as plt

						plt.plot(range(_lowercase)						,       _lowercase)
						plt.xlabel("""Iterations""")
						plt.ylabel("""Function values""")
						plt.show()
			return best_state


if __name__ == "__main__":

		def      lowerCAmelCase_					(  _lowercase    :       Any						,       _lowercase    :       Optional[int])							->	Union[str, Any]:







					"""simple docstring"""
					return (x**2) + (y**2)

		# starting the problem with initial coordinates (12, 47)
		_lowercase		:				List[Any]      		=SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		_lowercase		:				Union[str, Any]      		=simulated_annealing(
		    prob, find_max=False, max_x=100, min_x=5, max_y=50, 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()}'
		)

		# starting the problem with initial coordinates (12, 47)
		_lowercase		:				Optional[Any]      		=SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		_lowercase		:				Union[str, Any]      		=simulated_annealing(
		    prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
		)
		print(
		    "The maximum 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      lowerCAmelCase_					(  _lowercase    :       Union[str, Any]						,       _lowercase    :       List[str])							->	str:







					"""simple docstring"""
					return (3 * x**2) - (6 * y)

		_lowercase		:				Any      		=SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		_lowercase		:				Union[str, Any]      		=simulated_annealing(prob, find_max=False, visualization=True)
		print(
		    "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
		    f'{local_min.score()}'
		)

		_lowercase		:				Optional[int]      		=SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		_lowercase		:				str      		=simulated_annealing(prob, find_max=True, visualization=True)





		print(
		    "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
		    f'{local_min.score()}'
		)









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase		:				str      		=logging.getLogger(__name__)

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[float]      =					field(
						    default=0.0 ,     metadata={"help": "The label smoothing epsilon to apply (if not zero)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "Whether to SortishSamler or not."}		)
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "whether to use adafactor"}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Encoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Decoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(default=A__ ,     metadata={"help": "Dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Attention dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default="linear" ,     metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}"} ,     )




