






import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def   __lowercase       (			snake_case,	snake_case = True,	snake_case = math.inf,	snake_case = -math.inf,	snake_case = math.inf,	snake_case = -math.inf,	snake_case = False,	snake_case = 1_0_0,	snake_case = 0.01,	snake_case = 1,	):







				"""simple docstring"""






				__magic_name__		:Optional[int]   =   False
				__magic_name__		:Optional[Any]   =   search_prob
				__magic_name__		:Dict   =   start_temperate
				__magic_name__		:Tuple   =   []
				__magic_name__		:List[str]   =   0
				__magic_name__		:Any   =   None

				while not search_end:
								__magic_name__		:Any   =   current_state.score()
								if best_state is None or current_score > best_state.score():
												__magic_name__		:List[str]   =   current_state
								scores.append(snake_case			)
								iterations += 1
								__magic_name__		:Optional[Any]   =   None
								__magic_name__		:Tuple   =   current_state.get_neighbors()
								while (
								    next_state is None and neighbors
								):  # till we do not find a neighbor that we can move to
												__magic_name__		:str   =   random.randint(0,	len(snake_case			) - 1			)  # picking a random neighbor
												__magic_name__		:int   =   neighbors.pop(snake_case			)
												__magic_name__		:List[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:
																__magic_name__		:str   =   change * -1  # in case we are finding minimum
												if change > 0:  # improves the solution
																__magic_name__		:List[str]   =   picked_neighbor
												else:
																__magic_name__		:Tuple   =   (math.e) ** (
																    change / current_temp
																)  # probability generation function
																if random.random() < probability:  # random number within probability
																				__magic_name__		:List[str]   =   picked_neighbor
								__magic_name__		:Tuple   =   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
												__magic_name__		:str   =   True
								else:
												__magic_name__		:Any   =   next_state

				if visualization:
								from matplotlib import pyplot as plt

								plt.plot(range(snake_case			),	snake_case			)
								plt.xlabel('''Iterations'''			)
								plt.ylabel('''Function values'''			)
								plt.show()
				return best_state


if __name__ == "__main__":

					def   __lowercase       (			snake_case,	snake_case			):







									"""simple docstring"""






									return (x**2) + (y**2)

					# starting the problem with initial coordinates (12, 47)
					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		simulated_annealing(
					    prob, find_max=False, max_x=1_00, 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)
					SCREAMING_SNAKE_CASE__   :      Tuple				=		SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE__   :      List[Any]				=		simulated_annealing(
					    prob, find_max=True, max_x=1_00, 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   __lowercase       (			snake_case,	snake_case			):







									"""simple docstring"""






									return (3 * x**2) - (6 * y)

					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE__   :      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()}"
					)

					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE__   :      Tuple				=		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 argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Tuple						)		->				Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      BigBirdConfig.from_json_file(_UpperCamelCase						)
 print(f"""Building PyTorch model from configuration: {config}"""						)

 if is_trivia_qa:
  SCREAMING_SNAKE_CASE		    =      BigBirdForQuestionAnswering(_UpperCamelCase						)
 else:
  SCREAMING_SNAKE_CASE		    =      BigBirdForPreTraining(_UpperCamelCase						)

 # Load weights from tf checkpoint
 load_tf_weights_in_big_bird(_UpperCamelCase		,				_UpperCamelCase		,				is_trivia_qa=_UpperCamelCase						)

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
 model.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    int     		=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--big_bird_config_file",
          default=None,
          type=str,
          required=True,
          help=(
              "The config json file corresponding to the pre-trained BERT model. \n"
              "This specifies the model architecture."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
      )
      a_     :    Any     		=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
      )

