code: 
  
  
  
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional, Tuple
  
  from .training_args import TrainingArguments
  from .utils import cached_property, is_tf_available, logging, requires_backends
  
  
  lowerCAmelCase__	:  str 		=logging.get_logger(__name__)
  
  if is_tf_available():
  			import tensorflow as tf
  
  @dataclass
  class      UpperCAmelCase_						(     UpperCamelCase_ ):
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				List[Any]       						=						'''tf'''
  						UpperCamelCase__    :				Optional[str]       						=						field(
  						    default=UpperCamelCase_					,					metadata={'''help''': '''Name of TPU'''}					,					)
  
  						UpperCamelCase__    :				Optional[str]       						=						field(
  						    default=UpperCamelCase_					,					metadata={'''help''': '''Zone of TPU'''}					,					)
  
  						UpperCamelCase__    :				Optional[str]       						=						field(
  						    default=UpperCamelCase_					,					metadata={'''help''': '''Name of Cloud TPU-enabled project'''}					,					)
  
  						UpperCamelCase__    :				float       						=						field(
  						    default=1.0					,					metadata={'''help''': '''Power for the Polynomial decay LR scheduler.'''}					,					)
  
  						UpperCamelCase__    :				bool       						=						field(default=UpperCamelCase_					,					metadata={'''help''': '''Whether to activate the XLA compilation or not'''} )
  
  
  
  
  
  
  						@cached_property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													requires_backends(self   ,							['tf']	)
  													logger.info('Tensorflow: setting up strategy'	)
  
  													__SCREAMING_SNAKE_CASE								=      tf.config.list_physical_devices('GPU'	)
  
  													# Set to float16 at first
  													if self.fpaa:
  																				tf.keras.mixed_precision.set_global_policy('mixed_float16'	)
  
  													if self.no_cuda:
  																				__SCREAMING_SNAKE_CASE								=      tf.distribute.OneDeviceStrategy(device='/cpu:0'	)
  													else:
  																				try:
  																											if self.tpu_name:
  																																		__SCREAMING_SNAKE_CASE								=      tf.distribute.cluster_resolver.TPUClusterResolver(
  																																		    self.tpu_name   ,							zone=self.tpu_zone   ,							project=self.gcp_project	)
  																											else:
  																																		__SCREAMING_SNAKE_CASE								=      tf.distribute.cluster_resolver.TPUClusterResolver()
  																				except ValueError:
  																											if self.tpu_name:
  																																		raise RuntimeError(f"""Couldn't connect to TPU {self.tpu_name}!"""	)
  																											else:
  																																		__SCREAMING_SNAKE_CASE								=      None
  
  																				if tpu:
  																											# Set to bfloat16 in case of TPU
  																											if self.fpaa:
  																																		tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'	)
  
  																											tf.config.experimental_connect_to_cluster(_A	)
  																											tf.tpu.experimental.initialize_tpu_system(_A	)
  
  																											__SCREAMING_SNAKE_CASE								=      tf.distribute.TPUStrategy(_A	)
  
  																				elif len(_A	) == 0:
  																											__SCREAMING_SNAKE_CASE								=      tf.distribute.OneDeviceStrategy(device='/cpu:0'	)
  																				elif len(_A	) == 1:
  																											__SCREAMING_SNAKE_CASE								=      tf.distribute.OneDeviceStrategy(device='/gpu:0'	)
  																				elif len(_A	) > 1:
  																											# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
  																											__SCREAMING_SNAKE_CASE								=      tf.distribute.MirroredStrategy()
  																				else:
  																											raise ValueError('Cannot find the proper strategy, please check your environment properties.'	)
  
  													return strategy
  
  
  
  
  
  
  						@property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													requires_backends(self   ,							['tf']	)
  													return self._setup_strategy
  
  
  
  
  
  
  						@property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													requires_backends(self   ,							['tf']	)
  													return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
  
  						@property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													return False  # TF Logging is handled by Keras not the Trainer
  
  
  
  
  
  
  						@property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if self.per_gpu_train_batch_size:
  																				logger.warning(
  																				    'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
  																				    'version. Using `--per_device_train_batch_size` is preferred.'	)
  													__SCREAMING_SNAKE_CASE								=      self.per_gpu_train_batch_size or self.per_device_train_batch_size
  													return per_device_batch_size * self.n_replicas
  
  
  
  
  
  
  						@property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if self.per_gpu_eval_batch_size:
  																				logger.warning(
  																				    'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
  																				    'version. Using `--per_device_eval_batch_size` is preferred.'	)
  													__SCREAMING_SNAKE_CASE								=      self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
  													return per_device_batch_size * self.n_replicas
  
  
  
  
  
  
  						@property
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													requires_backends(self   ,							['tf']	)
  													warnings.warn(
  													    'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'   ,							_A   ,							)
  													return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
  
  
style_context: 
  
  
  
  def 			__lowercase     (				a__     )       ->				list:
  							__SCREAMING_SNAKE_CASE								=      1.3
  							__SCREAMING_SNAKE_CASE								=      len(a__     )
  							__SCREAMING_SNAKE_CASE								=      False
  
  							while not completed:
  														# Update the gap value for a next comb
  														__SCREAMING_SNAKE_CASE								=      int(gap / shrink_factor     )
  														if gap <= 1:
  																					__SCREAMING_SNAKE_CASE								=      True
  
  														__SCREAMING_SNAKE_CASE								=      0
  														while index + gap < len(a__     ):
  																					if data[index] > data[index + gap]:
  																												# Swap values
  																												__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      data[index + gap], data[index]
  																												__SCREAMING_SNAKE_CASE								=      False
  																					index += 1
  
  							return data
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  			lowerCAmelCase__	:  int 		=input('''Enter numbers separated by a comma:\n''').strip()
  			lowerCAmelCase__	:  Dict 		=[int(item) for item in user_input.split(''',''')]
  			print(comb_sort(unsorted))
  
  
  
  
  
  
  