



'''simple docstring'''


import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def 				__lowerCamelCase				(   A__    ,						A__ = True    ,						A__ = math.inf    ,						A__ = -math.inf    ,						A__ = math.inf    ,						A__ = -math.inf    ,						A__ = False    ,						A__ = 100    ,						A__ = 0.01    ,						A__ = 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(A__    )
      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(A__    ) - 1    )  # picking a random neighbor
         UpperCamelCase					=  neighbors.pop(A__    )
         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(A__    )    ,						A__    )
      plt.xlabel('Iterations'    )
      plt.ylabel('Function values'    )
      plt.show()
   return best_state


if __name__ == "__main__":







   def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Union[str, Any]:






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

   # starting the problem with initial coordinates (12, 47)
   _lowerCamelCase	:       Tuple      	=	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   _lowerCamelCase	:       Optional[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)
   _lowerCamelCase	:       Optional[Any]      	=	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   _lowerCamelCase	:       List[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				(   A__    ,						A__    )       ->	int:






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

   _lowerCamelCase	:       Optional[int]      	=	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   _lowerCamelCase	:       Tuple      	=	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()}'''
   )

   _lowerCamelCase	:       List[Any]      	=	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   _lowerCamelCase	:       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 json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
						from sagemaker import Session, TrainingJobAnalytics
						from sagemaker.huggingface import HuggingFace



@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''						,'''False'''						)						) is not True						,reason='''Skipping test because should only be run when releasing minor transformers version'''						,)
@pytest.mark.usefixtures('''sm_env'''						)
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 650, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]						)
class 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       int):
									if self.framework == "pytorch":
											subprocess.run(
											    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()				,				encoding='''utf-8'''				,				check=A_				,				)
									assert hasattr(self				,				'''env''')




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       str=1):
									# creates estimator
									return HuggingFace(
									    entry_point=self.script				,				source_dir=self.env.test_path				,				role=self.env.role				,				image_uri=self.env.image_uri				,				base_job_name=F"""{self.env.base_job_name}-single"""				,				instance_count=A_				,				instance_type=self.instance_type				,				debugger_hook_config=A_				,				hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}				,				metric_definitions=self.env.metric_definitions				,				py_version='''py36'''				,				)




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Optional[Any]):
									TrainingJobAnalytics(A_).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")







							def    UpperCAmelCase__			(       self     :       int):
									# create estimator
									lowerCAmelCase_			:     List[Any]									= self.create_estimator()

									# run training
									estimator.fit()

									# result dataframe
									lowerCAmelCase_			:     Union[str, Any]									= TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

									# extract kpis
									lowerCAmelCase_			:     Optional[Any]									= list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''])
									lowerCAmelCase_			:     Optional[int]									= list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''])
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									lowerCAmelCase_			:     Dict									= (
									    Session().describe_training_job(estimator.latest_training_job.name).get('''TrainingTimeInSeconds'''				,				9_9_9_9_9_9)
									)

									# assert kpis
									assert train_runtime <= self.results["train_runtime"]
									assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy)
									assert all(t <= self.results['''eval_loss'''] for t in eval_loss)

									# dump tests result into json file to share in PR
									with open(F"""{estimator.latest_training_job.name}.json"""				,				'''w''') as outfile:
											json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}				,				A_)





