



"""simple docstring"""

import math
import random
from typing import Any

from .hill_climbing import SearchProblem




def __lowercase    (							_a	,    _a = True	,    _a = math.inf	,    _a = -math.inf	,    _a = math.inf	,    _a = -math.inf	,    _a = False	,    _a = 100	,    _a = 0.01	,    _a = 1	,    ):
    snake_case_				:  Optional[Any]       =			False
    snake_case_				:  List[Any]       =			search_prob
    snake_case_				:  str       =			start_temperate
    snake_case_				:  Dict       =			[]
    snake_case_				:  int       =			0
    snake_case_				:  Tuple       =			None

    while not search_end:
        snake_case_				:  int       =			current_state.score()
        if best_state is None or current_score > best_state.score():
            snake_case_				:  Any       =			current_state
        scores.append(_a					)
        iterations += 1
        snake_case_				:  Optional[Any]       =			None
        snake_case_				:  Dict       =			current_state.get_neighbors()
        while (
            next_state is None and neighbors
        ):  # till we do not find a neighbor that we can move to
            snake_case_				:  Optional[Any]       =			random.randint(0	,    len(_a					) - 1					)  # picking a random neighbor
            snake_case_				:  Union[str, Any]       =			neighbors.pop(_a					)
            snake_case_				:  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:
                snake_case_				:  str       =			change * -1  # in case we are finding minimum
            if change > 0:  # improves the solution
                snake_case_				:  List[str]       =			picked_neighbor
            else:
                snake_case_				:  Tuple       =			(math.e) ** (
                    change / current_temp
                )  # probability generation function
                if random.random() < probability:  # random number within probability
                    snake_case_				:  Tuple       =			picked_neighbor
        snake_case_				:  List[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
            snake_case_				:  Dict       =			True
        else:
            snake_case_				:  List[Any]       =			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 __lowercase    (							_a	,    _a					):
           return (x**2) + (y**2)

       # starting the problem with initial coordinates (12, 47)
       lowercase__					:						Dict												=			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       lowercase__					:						Union[str, 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)
       lowercase__					:						Any												=			SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       lowercase__					:						int												=			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    (							_a	,    _a					):
           return (3 * x**2) - (6 * y)

       lowercase__					:						List[str]												=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       lowercase__					:						Dict												=			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()}'
       )

       lowercase__					:						Any												=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       lowercase__					:						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()}'
       )










"""simple docstring"""

import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met




class  _UpperCAmelCase (    lowerCAmelCase__):

 def __init__(  self	: List[Any]						,  *lowercase_	: List[Any]						,  lowercase_	: Optional[Any]=None						,  lowercase_	: Tuple=None						,  **lowercase_	: Union[str, Any]       ):
     super().__init__(*lowercase_						,  **lowercase_       )
     snake_case_				:  str       =			eval_examples
     snake_case_				:  Optional[Any]       =			post_process_function

 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: Optional[Dataset] = None						,  lowercase_	: str=None						,  lowercase_	: Optional[List[str]] = None						,  lowercase_	: str = "eval"						,  **lowercase_	: Dict						,  ):
     snake_case_				:  int       =			gen_kwargs.copy()
     snake_case_				:  Dict       =			(
         gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''       ) is not None else self.args.generation_max_length
     )
     snake_case_				:  Tuple       =			(
         gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''       ) is not None else self.args.generation_num_beams
     )
     snake_case_				:  Optional[Any]       =			gen_kwargs

     snake_case_				:  Optional[int]       =			self.eval_dataset if eval_dataset is None else eval_dataset
     snake_case_				:  Optional[Any]       =			self.get_eval_dataloader(lowercase_       )
     snake_case_				:  Dict       =			self.eval_examples if eval_examples is None else eval_examples

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_				:  Optional[int]       =			self.compute_metrics
     snake_case_				:  str       =			None
     snake_case_				:  int       =			time.time()
     snake_case_				:  Any       =			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
         snake_case_				:  str       =			eval_loop(
             lowercase_						,  description='''Evaluation'''						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase_						,  metric_key_prefix=lowercase_						,  )
     finally:
         snake_case_				:  Optional[int]       =			compute_metrics
     snake_case_				:  Dict       =			self.args.eval_batch_size * self.args.world_size
     if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
         start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
     output.metrics.update(
         speed_metrics(
             lowercase_						,  lowercase_						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size       )						,  )       )

     if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
         # Only the main node write the results by default
         snake_case_				:  Tuple       =			self.post_process_function(lowercase_						,  lowercase_						,  lowercase_       )
         snake_case_				:  Optional[int]       =			self.compute_metrics(lowercase_       )

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()       ):
             if not key.startswith(f"{metric_key_prefix}_"       ):
                 snake_case_				:  int       =			metrics.pop(lowercase_       )

         metrics.update(output.metrics       )
     else:
         snake_case_				:  Tuple       =			output.metrics

     if self.args.should_log:
         # Only the main node log the results by default
         self.log(lowercase_       )

     if self.args.tpu_metrics_debug or self.args.debug:
         # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
         xm.master_print(met.metrics_report()       )

     snake_case_				:  str       =			self.callback_handler.on_evaluate(self.args						,  self.state						,  self.control						,  lowercase_       )
     return metrics






 def 		_snake_case      (  self	: Any						,  lowercase_	: int						,  lowercase_	: List[str]						,  lowercase_	: str=None						,  lowercase_	: str = "test"						,  **lowercase_	: Dict       ):
     snake_case_				:  Dict       =			gen_kwargs.copy()

     snake_case_				:  Dict       =			self.get_test_dataloader(lowercase_       )

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_				:  Tuple       =			self.compute_metrics
     snake_case_				:  int       =			None
     snake_case_				:  List[Any]       =			time.time()
     snake_case_				:  int       =			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
         snake_case_				:  List[str]       =			eval_loop(
             lowercase_						,  description='''Prediction'''						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase_						,  metric_key_prefix=lowercase_						,  )
     finally:
         snake_case_				:  str       =			compute_metrics

     snake_case_				:  int       =			self.args.eval_batch_size * self.args.world_size
     if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
         start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
     output.metrics.update(
         speed_metrics(
             lowercase_						,  lowercase_						,  num_samples=output.num_samples						,  num_steps=math.ceil(output.num_samples / total_batch_size       )						,  )       )
     if self.post_process_function is None or self.compute_metrics is None:
         return output

     snake_case_				:  Tuple       =			self.post_process_function(lowercase_						,  lowercase_						,  lowercase_						,  '''predict'''       )
     snake_case_				:  Dict       =			self.compute_metrics(lowercase_       )

     # Prefix all keys with metric_key_prefix + '_'
     for key in list(metrics.keys()       ):
         if not key.startswith(f"{metric_key_prefix}_"       ):
             snake_case_				:  Union[str, Any]       =			metrics.pop(lowercase_       )
     metrics.update(output.metrics       )
     return PredictionOutput(predictions=predictions.predictions						,  label_ids=predictions.label_ids						,  metrics=lowercase_       )





