
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        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def __init__(  self     :   List[Any]      ,  *lowercase     :   Optional[int]      ,  lowercase     :   List[Any]=None      ,  lowercase     :   List[Any]=None      ,  **lowercase     :   Optional[int]     ):


				'''simple docstring'''


				super().__init__(*__UpperCAmelCase      ,  **__UpperCAmelCase     )
				_snake_case          =      eval_examples
				_snake_case          =      post_process_function



	def 			A							(  self     :   Dict      ,  lowercase     :   Optional[Dataset] = None      ,  lowercase     :   Dict=None      ,  lowercase     :   Optional[List[str]] = None      ,  lowercase     :   str = "eval"      ,  **lowercase     :   Any      ,  ):


				'''simple docstring'''


				_snake_case          =      gen_kwargs.copy()
				_snake_case          =      (
				    gen_kwargs['max_length'] if gen_kwargs.get('max_length'     ) is not None else self.args.generation_max_length
				)
				_snake_case          =      (
				    gen_kwargs['num_beams'] if gen_kwargs.get('num_beams'     ) is not None else self.args.generation_num_beams
				)
				_snake_case          =      gen_kwargs

				_snake_case          =      self.eval_dataset if eval_dataset is None else eval_dataset
				_snake_case          =      self.get_eval_dataloader(__UpperCAmelCase     )
				_snake_case          =      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          =      self.compute_metrics
				_snake_case          =      None
				_snake_case          =      time.time()
				_snake_case          =      self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
				try:
							_snake_case          =      eval_loop(
							    __UpperCAmelCase      ,  description='Evaluation'      ,  prediction_loss_only=True if compute_metrics is None else None      ,  ignore_keys=__UpperCAmelCase      ,  metric_key_prefix=__UpperCAmelCase      ,  )
				finally:
							_snake_case          =      compute_metrics
				_snake_case          =      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
							_snake_case          =      self.post_process_function(__UpperCAmelCase      ,  __UpperCAmelCase      ,  __UpperCAmelCase     )
							_snake_case          =      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}_'''     ):
													_snake_case          =      metrics.pop(__UpperCAmelCase     )

							metrics.update(output.metrics     )
				else:
							_snake_case          =      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()     )

				_snake_case          =      self.callback_handler.on_evaluate(self.args      ,  self.state      ,  self.control      ,  __UpperCAmelCase     )
				return metrics





	def 			A							(  self     :   Tuple      ,  lowercase     :   int      ,  lowercase     :   int      ,  lowercase     :   int=None      ,  lowercase     :   str = "test"      ,  **lowercase     :   Any     ):


				'''simple docstring'''


				_snake_case          =      gen_kwargs.copy()

				_snake_case          =      self.get_test_dataloader(__UpperCAmelCase     )

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

				_snake_case          =      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

				_snake_case          =      self.post_process_function(__UpperCAmelCase      ,  __UpperCAmelCase      ,  __UpperCAmelCase      ,  'predict'     )
				_snake_case          =      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}_'''     ):
										_snake_case          =      metrics.pop(__UpperCAmelCase     )
				metrics.update(output.metrics     )
				return PredictionOutput(predictions=predictions.predictions      ,  label_ids=predictions.label_ids      ,  metrics=__UpperCAmelCase     )

def 	a_		(	__lowercase       :     List[Any]			)			->  Tuple:
			_snake_case          =      len(__lowercase			)
			for i in range(length - 1			):
						_snake_case          =      i
						for k in range(i + 1	, __lowercase			):
									if collection[k] < collection[least]:
												_snake_case          =      k
						if least != i:
									_snake_case     , _snake_case          =      (collection[i], collection[least])
			return collection


if __name__ == "__main__":
			_lowerCamelCase	:    str          =      input('''Enter numbers separated by a comma:\n''').strip()
			_lowerCamelCase	:    List[Any]          =      [int(item) for item in user_input.split(''',''')]
			print(selection_sort(unsorted))