import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def        lowercase(     UpperCamelCase_				, UpperCamelCase_ = True				, UpperCamelCase_ = math.inf				, UpperCamelCase_ = -math.inf				, UpperCamelCase_ = math.inf				, UpperCamelCase_ = -math.inf				, UpperCamelCase_ = False				, UpperCamelCase_ = 100				, UpperCamelCase_ = 0.0_1				, UpperCamelCase_ = 1				, )						->     Any:







 '''simple docstring'''
 UpperCamelCase          =	False
 UpperCamelCase          =	search_prob
 UpperCamelCase          =	start_temperate
 UpperCamelCase          =	[]
 UpperCamelCase          =	0
 UpperCamelCase          =	None

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







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

     # starting the problem with initial coordinates (12, 47)
     _SCREAMING_SNAKE_CASE      						=	SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     _SCREAMING_SNAKE_CASE      						=	simulated_annealing(
         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()}'''
     )

     # starting the problem with initial coordinates (12, 47)
     _SCREAMING_SNAKE_CASE      						=	SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     _SCREAMING_SNAKE_CASE      						=	simulated_annealing(
         prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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(     UpperCamelCase_				, UpperCamelCase_    )						->     List[str]:







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

     _SCREAMING_SNAKE_CASE      						=	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     _SCREAMING_SNAKE_CASE      						=	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      						=	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     _SCREAMING_SNAKE_CASE      						=	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)





