

'''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 lowerCamelCase (  lowerCAmelCase__						):



			'''simple docstring'''






			def __init__(      self       :						int		,			*lowerCAmelCase_       :						int		,			lowerCAmelCase_       :						List[Any]=None		,			lowerCAmelCase_       :						List[str]=None		,			**lowerCAmelCase_       :						List[Any] )			->		Tuple:





									'''simple docstring'''
									super().__init__(*UpperCamelCase__		,			**UpperCamelCase__ )
									A__     :   Optional[int]      =eval_examples
									A__     :   Union[str, Any]      =post_process_function


			def lowercase__						(      self       :						Tuple		,			lowerCAmelCase_       :						Optional[Any]=None		,			lowerCAmelCase_       :						Dict=None		,			lowerCAmelCase_       :						Tuple=None		,			lowerCAmelCase_       :						Any = "eval" )			->		Optional[int]:





									'''simple docstring'''
									A__     :   Optional[int]      =self.eval_dataset if eval_dataset is None else eval_dataset
									A__     :   str      =self.get_eval_dataloader(UpperCamelCase__ )
									A__     :   int      =self.eval_examples if eval_examples is None else eval_examples

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

															# Prefix all keys with metric_key_prefix + '_'
															for key in list(metrics.keys() ):
																					if not key.startswith(f"{metric_key_prefix}_" ):
																											A__     :   List[str]      =metrics.pop(UpperCamelCase__ )
															metrics.update(output.metrics )
									else:
															A__     :   Optional[Any]      =output.metrics

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

									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__     :   Tuple      =self.callback_handler.on_evaluate(self.args		,			self.state		,			self.control		,			UpperCamelCase__ )
									return metrics



			def lowercase__						(      self       :						Optional[Any]		,			lowerCAmelCase_       :						Union[str, Any]		,			lowerCAmelCase_       :						int		,			lowerCAmelCase_       :						Any=None		,			lowerCAmelCase_       :						List[Any] = "test" )			->		Union[str, Any]:





									'''simple docstring'''
									A__     :   Any      =self.get_test_dataloader(UpperCamelCase__ )

									# Temporarily disable metric computation, we will do it in the loop here.
									A__     :   Tuple      =self.compute_metrics
									A__     :   Dict      =None
									A__     :   int      =self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
									A__     :   Optional[int]      =time.time()
									try:
															A__     :   Optional[int]      =eval_loop(
															    UpperCamelCase__		,			description="""Prediction"""		,			prediction_loss_only=True if compute_metrics is None else None		,			ignore_keys=UpperCamelCase__		,			metric_key_prefix=UpperCamelCase__		,			)
									finally:
															A__     :   Dict      =compute_metrics
									A__     :   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(
									        UpperCamelCase__		,			UpperCamelCase__		,			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__     :   Any      =self.post_process_function(UpperCamelCase__		,			UpperCamelCase__		,			output.predictions		,			"""predict""" )
									A__     :   Union[str, Any]      =self.compute_metrics(UpperCamelCase__ )

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







import random

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> tuple:
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Any			 =					[], [], []
       for element in data:
              if element < pivot:
                     less.append(_SCREAMING_SNAKE_CASE    )
              elif element > pivot:
                     greater.append(_SCREAMING_SNAKE_CASE    )
              else:
                     equal.append(_SCREAMING_SNAKE_CASE    )
       return less, equal, greater


def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(_SCREAMING_SNAKE_CASE    ) or index < 0:
              return None

       lowerCamelCase   :      List[Any]			 =					items[random.randint(0 ,len(_SCREAMING_SNAKE_CASE    ) - 1    )]
       lowerCamelCase   :      Dict			 =					0
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					_partition(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					len(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      str			 =					len(_SCREAMING_SNAKE_CASE    )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # must be in larger
       else:
              return quick_select(_SCREAMING_SNAKE_CASE ,index - (m + count)    )






