


'''simple docstring'''



import math
import time

from transformers import Trainer, 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 			a       (	__UpperCAmelCase ):


     """simple docstring"""






     def __init__(       self  :	List[str]							,				*snake_case  :	Any							,				snake_case  :	Any=None							,				snake_case  :	int=None							,				**snake_case  :	Any    )		->							Optional[int]:
          super().__init__(*snake_case							,				**snake_case    )
          __UpperCAmelCase			:				Dict													=    eval_examples
          __UpperCAmelCase			:				str													=    post_process_function

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	Any=None							,				snake_case  :	Tuple=None							,				snake_case  :	int=None							,				snake_case  :	List[Any] = "eval"    )		->							Optional[int]:
          __UpperCAmelCase			:				Any													=    self.eval_dataset if eval_dataset is None else eval_dataset
          __UpperCAmelCase			:				str													=    self.get_eval_dataloader(snake_case    )
          __UpperCAmelCase			:				Optional[int]													=    self.eval_examples if eval_examples is None else eval_examples

          # Temporarily disable metric computation, we will do it in the loop here.
          __UpperCAmelCase			:				Optional[int]													=    self.compute_metrics
          __UpperCAmelCase			:				List[str]													=    None
          __UpperCAmelCase			:				List[str]													=    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          __UpperCAmelCase			:				Dict													=    time.time()
          try:
               __UpperCAmelCase			:				Dict													=    eval_loop(
                   snake_case							,				description='''Evaluation'''							,				prediction_loss_only=True if compute_metrics is None else None							,				ignore_keys=snake_case							,				metric_key_prefix=snake_case							,				)
          finally:
               __UpperCAmelCase			:				Dict													=    compute_metrics
          __UpperCAmelCase			:				Tuple													=    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(
                  snake_case							,				snake_case							,				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
               __UpperCAmelCase			:				Optional[int]													=    self.post_process_function(snake_case							,				snake_case							,				output.predictions    )
               __UpperCAmelCase			:				List[str]													=    self.compute_metrics(snake_case    )

               # Prefix all keys with metric_key_prefix + '_'
               for key in list(metrics.keys()    ):
                    if not key.startswith(f'{metric_key_prefix}_'    ):
                         __UpperCAmelCase			:				Tuple													=    metrics.pop(snake_case    )
               metrics.update(output.metrics    )
          else:
               __UpperCAmelCase			:				Union[str, Any]													=    output.metrics

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

          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()    )

          __UpperCAmelCase			:				Dict													=    self.callback_handler.on_evaluate(self.args							,				self.state							,				self.control							,				snake_case    )
          return metrics






     def 				lowerCamelCase__				(       self  :	Tuple							,				snake_case  :	int							,				snake_case  :	Any							,				snake_case  :	Optional[int]=None							,				snake_case  :	List[str] = "test"    )		->							Any:
          __UpperCAmelCase			:				int													=    self.get_test_dataloader(snake_case    )

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

          __UpperCAmelCase			:				Dict													=    self.post_process_function(snake_case							,				snake_case							,				output.predictions							,				'''predict'''    )
          __UpperCAmelCase			:				Optional[Any]													=    self.compute_metrics(snake_case    )

          # Prefix all keys with metric_key_prefix + '_'
          for key in list(metrics.keys()    ):
               if not key.startswith(f'{metric_key_prefix}_'    ):
                    __UpperCAmelCase			:				Any													=    metrics.pop(snake_case    )
          metrics.update(output.metrics    )
          return PredictionOutput(predictions=predictions.predictions							,				label_ids=predictions.label_ids							,				metrics=snake_case    )



'''simple docstring'''



def 			_a			(		_lowercase      :			List[str]						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    len(_lowercase						)
     while cur > 1:
          # Find the maximum number in arr
          __UpperCAmelCase			:				Union[str, Any]													=    arr.index(max(arr[0:cur]						)						)
          # Reverse from 0 to mi
          __UpperCAmelCase			:				Any													=    arr[mi::-1] + arr[mi + 1 : len(_lowercase						)]
          # Reverse whole list
          __UpperCAmelCase			:				List[str]													=    arr[cur - 1 :: -1] + arr[cur : len(_lowercase						)]
          cur -= 1
     return arr


if __name__ == "__main__":
      __UpperCAmelCase    :Any				       =     input("Enter numbers separated by a comma:\n").strip()
      __UpperCAmelCase    :Optional[int]				       =     [int(item) for item in user_input.split(",")]
      print(pancake_sort(unsorted))