



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 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       *lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(*lowerCamelCase_		,       **lowerCamelCase_		)
						a					     =   eval_examples
						a					     =   post_process_function




	def      UpperCamelCase_       (self		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       lowerCamelCase_ = "eval"		):
						"""simple docstring"""

						a					     =   self.eval_dataset if eval_dataset is None else eval_dataset
						a					     =   self.get_eval_dataloader(lowerCamelCase_		)
						a					     =   self.eval_examples if eval_examples is None else eval_examples

						# Temporarily disable metric computation, we will do it in the loop here.
						a					     =   self.compute_metrics
						a					     =   None
						a					     =   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
						a					     =   time.time()
						try:
											a					     =   eval_loop(
											    lowerCamelCase_		,       description="Evaluation"		,       prediction_loss_only=True if compute_metrics is None else None		,       ignore_keys=lowerCamelCase_		,       metric_key_prefix=lowerCamelCase_		,       )
						finally:
											a					     =   compute_metrics
						a					     =   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(
						        lowerCamelCase_		,       lowerCamelCase_		,       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
											a					     =   self.post_process_function(lowerCamelCase_		,       lowerCamelCase_		,       output.predictions		)
											a					     =   self.compute_metrics(lowerCamelCase_		)

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()		):
																if not key.startswith(F'''{metric_key_prefix}_'''		):
																					a					     =   metrics.pop(lowerCamelCase_		)
											metrics.update(output.metrics		)
						else:
											a					     =   output.metrics

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

						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()		)

						a					     =   self.callback_handler.on_evaluate(self.args		,       self.state		,       self.control		,       lowerCamelCase_		)
						return metrics




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_ = "test"		):
						"""simple docstring"""

						a					     =   self.get_test_dataloader(lowerCamelCase_		)

						# Temporarily disable metric computation, we will do it in the loop here.
						a					     =   self.compute_metrics
						a					     =   None
						a					     =   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
						a					     =   time.time()
						try:
											a					     =   eval_loop(
											    lowerCamelCase_		,       description="Prediction"		,       prediction_loss_only=True if compute_metrics is None else None		,       ignore_keys=lowerCamelCase_		,       metric_key_prefix=lowerCamelCase_		,       )
						finally:
											a					     =   compute_metrics
						a					     =   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(
						        lowerCamelCase_		,       lowerCamelCase_		,       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

						a					     =   self.post_process_function(lowerCamelCase_		,       lowerCamelCase_		,       output.predictions		,       "predict"		)
						a					     =   self.compute_metrics(lowerCamelCase_		)

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





import random






def     a(      A       :    Optional[Any] ,     A       :    Optional[Any] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





					a					     =   a[left_index]
					a					     =   left_index + 1
					for j in range(left_index + 1 ,     A       ):
										if a[j] < pivot:
															a     ,				a					     =   a[i], a[j]
															i += 1
					a     ,				a					     =   a[i - 1], a[left_index]
					return i - 1






def     a(      A       :    List[Any] ,     A       :    List[Any] ,     A       :    Union[str, Any]       )		->       List[Any]:
					"""simple docstring"""





					if left < right:
										a					     =   random.randint(A ,     right - 1       )
										a     ,				a					     =   (
										    a[left],
										    a[pivot],
										)  # switches the pivot with the left most bound
										a					     =   partition(A ,     A ,     A       )
										quick_sort_random(
										    A ,     A ,     A       )  # recursive quicksort to the left of the pivot point
										quick_sort_random(
										    A ,     pivot_index + 1 ,     A       )  # recursive quicksort to the right of the pivot point






def     a(      )		->       Any:
					"""simple docstring"""





					a					     =   input("Enter numbers separated by a comma:\n"       ).strip()
					a					     =   [int(A       ) for item in user_input.split(","       )]

					quick_sort_random(A ,     0 ,     len(A       )       )

					print(A       )


if __name__ == "__main__":
				main()
