




import math






class 			A			:
     """simple docstring"""

     def __init__(    self  :   Tuple	,  A__  :   int	,  A__  :   int	,  A__  :   int	,  A__  :   Optional[int]			)				->       Optional[Any]:
           lowerCamelCase__        =				x
           lowerCamelCase__        =				y
           lowerCamelCase__        =				step_size
           lowerCamelCase__        =				function_to_optimize




     def  __snake_case   (    self  :   Tuple			)				->       int:
           return self.function(self.x	,  self.y			)




     def  __snake_case   (    self  :   Optional[Any]			)				->       str:
           lowerCamelCase__        =				self.step_size
           return [
               SearchProblem(A__	,  A__	,  A__	,  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  :   Optional[int]			)				->       Optional[int]:
           return hash(str(self			)			)




     def __eq__(    self  :   Any	,  A__  :   List[Any]			)				->       List[Any]:
           if isinstance(A__	,  A__			):
                 return hash(str(self			)			) == hash(str(A__			)			)
           return False




     def __str__(    self  :   str			)				->       Tuple:
           return F"""x: {self.x} y: {self.y}"""

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[Any]     ,				lowerCamelCase:       bool = True     ,				lowerCamelCase:       float = math.inf     ,				lowerCamelCase:       float = -math.inf     ,				lowerCamelCase:       float = math.inf     ,				lowerCamelCase:       float = -math.inf     ,				lowerCamelCase:       bool = False     ,				lowerCamelCase:       int = 1_00_00     ,				)					->			SearchProblem:


      '''simple docstring'''




      lowerCamelCase__        =				search_prob
      lowerCamelCase__        =				[]  # list to store the current score at each iteration
      lowerCamelCase__        =				0
      lowerCamelCase__        =				False
      lowerCamelCase__        =				set()
      while not solution_found and iterations < max_iter:
            visited.add(lowerCamelCase						)
            iterations += 1
            lowerCamelCase__        =				current_state.score()
            scores.append(lowerCamelCase						)
            lowerCamelCase__        =				current_state.get_neighbors()
            lowerCamelCase__        =				-math.inf
            lowerCamelCase__        =				math.inf
            lowerCamelCase__        =				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__        =				neighbor.score() - current_score
                  if find_max:  # finding max
                        # going to direction with greatest ascent
                        if change > max_change and change > 0:
                              lowerCamelCase__        =				change
                              lowerCamelCase__        =				neighbor
                  else:  # finding min
                        # to direction with greatest descent
                        if change < min_change and change < 0:
                              lowerCamelCase__        =				change
                              lowerCamelCase__        =				neighbor
            if next_state is not None:
                  # we found at least one neighbor which improved the current state
                  lowerCamelCase__        =				next_state
            else:
                  # since we have no neighbor that improves the solution we stop the search
                  lowerCamelCase__        =				True

      if visualization:
            from matplotlib import pyplot as plt

            plt.plot(range(lowerCamelCase						)     ,				lowerCamelCase						)
            plt.xlabel('Iterations'						)
            plt.ylabel('Function values'						)
            plt.show()

      return current_state


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[Any]     ,				lowerCamelCase:       Tuple						)					->			List[str]:


            '''simple docstring'''




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

      # starting the problem with initial coordinates (3, 4)
      __SCREAMING_SNAKE_CASE    :   Optional[int]       =SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      __SCREAMING_SNAKE_CASE    :   int       =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)
      __SCREAMING_SNAKE_CASE    :   Any       =SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      __SCREAMING_SNAKE_CASE    :   Dict       =hill_climbing(
          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()}"""
      )

      def 			lowerCAmelCase_			(  lowerCamelCase:       List[Any]     ,				lowerCamelCase:       Any						)					->			Optional[int]:


            '''simple docstring'''




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

      __SCREAMING_SNAKE_CASE    :   Dict       =SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      __SCREAMING_SNAKE_CASE    :   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 logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE    :   List[str]       =logging.getLogger()

def 			lowerCAmelCase_			(  )					->			Any:


      '''simple docstring'''




      lowerCamelCase__        =				argparse.ArgumentParser()
      parser.add_argument('-f'						)
      lowerCamelCase__        =				parser.parse_args()
      return args.f






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def  __snake_case   (    self  :   Optional[Any]			)				->       Optional[Any]:
           lowerCamelCase__        =				logging.StreamHandler(sys.stdout			)
           logger.addHandler(A__			)

           lowerCamelCase__        =				self.get_auto_remove_tmp_dir()
           lowerCamelCase__        =				F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

           with patch.object(A__	,  'argv'	,  A__			):
                 lowerCamelCase__        =				run_glue_with_pabee.main()
                 for value in result.values():
                       self.assertGreaterEqual(A__	,  0.75			)