



'''simple docstring'''






import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def  snake_case_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						bool = True   ,      _lowerCAmelCase  :						float = math.inf   ,      _lowerCAmelCase  :						float = -math.inf   ,      _lowerCAmelCase  :						float = math.inf   ,      _lowerCAmelCase  :						float = -math.inf   ,      _lowerCAmelCase  :						bool = False   ,      _lowerCAmelCase  :						float = 100   ,      _lowerCAmelCase  :						float = 0.0_1   ,      _lowerCAmelCase  :						float = 1   ,      )      ->   Any:
							UpperCAmelCase :       Dict 							=		False
							UpperCAmelCase :       List[Any] 							=		search_prob
							UpperCAmelCase :       Any 							=		start_temperate
							UpperCAmelCase :       Any 							=		[]
							UpperCAmelCase :       int 							=		0
							UpperCAmelCase :       Optional[int] 							=		None

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

							if visualization:
														from matplotlib import pyplot as plt

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


if __name__ == "__main__":


						def  snake_case_     (					_lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						List[Any]  )      ->   List[str]:
													return (x**2) + (y**2)

						# starting the problem with initial coordinates (12, 47)
						UpperCamelCase__:   Dict								=			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
						UpperCamelCase__:   List[str]								=			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)
						UpperCamelCase__:   List[Any]								=			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
						UpperCamelCase__:   Dict								=			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  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						int  )      ->   List[str]:
													return (3 * x**2) - (6 * y)

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

						UpperCamelCase__:   str								=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						UpperCamelCase__:   Union[str, Any]								=			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()}"
						)










'''simple docstring'''






# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCamelCase__:   Optional[int]								=			"3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
						import torch

						print("Torch version:", torch.__version__)
						print("Cuda available:", torch.cuda.is_available())
						print("Cuda version:", torch.version.cuda)
						print("CuDNN version:", torch.backends.cudnn.version())
						print("Number of GPUs available:", torch.cuda.device_count())
						print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
						print("Torch version:", None)

try:
						import deepspeed

						print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
						print("DeepSpeed version:", None)

try:
						import tensorflow as tf

						print("TensorFlow version:", tf.__version__)
						print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
						print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
						print("TensorFlow version:", None)





