code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  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
  
  
  lowercase_								=    logging.get_logger(__name__)
  
  if is_tf_available():
  		import tensorflow as tf
  
  
  
  
  @dataclass
  class 					_UpperCamelCase (      a_	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  'tf'
  							_A  				=  field(
  							    default=a_			,							metadata={"help": "Name of TPU"}			,							)
  
  							_A  				=  field(
  							    default=a_			,							metadata={"help": "Zone of TPU"}			,							)
  
  							_A  				=  field(
  							    default=a_			,							metadata={"help": "Name of Cloud TPU-enabled project"}			,							)
  
  							_A  				=  field(
  							    default=1.0			,							metadata={"help": "Power for the Polynomial decay LR scheduler."}			,							)
  
  							_A  				=  field(default=a_			,							metadata={"help": "Whether to activate the XLA compilation or not"}	)
  
  
  
  
  
  
  
  							@cached_property
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
  													requires_backends(self		,				['tf']		)
  													logger.info('Tensorflow: setting up strategy'		)
  
  													_a     =			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:
  																			_a     =			tf.distribute.OneDeviceStrategy(device='/cpu:0'		)
  													else:
  																			try:
  																									if self.tpu_name:
  																															_a     =			tf.distribute.cluster_resolver.TPUClusterResolver(
  																															    self.tpu_name		,				zone=self.tpu_zone		,				project=self.gcp_project		)
  																									else:
  																															_a     =			tf.distribute.cluster_resolver.TPUClusterResolver()
  																			except ValueError:
  																									if self.tpu_name:
  																															raise RuntimeError(f"""Couldn\'t connect to TPU {self.tpu_name}!"""		)
  																									else:
  																															_a     =			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(_snake_case		)
  																									tf.tpu.experimental.initialize_tpu_system(_snake_case		)
  
  																									_a     =			tf.distribute.TPUStrategy(_snake_case		)
  
  																			elif len(_snake_case		) == 0:
  																									_a     =			tf.distribute.OneDeviceStrategy(device='/cpu:0'		)
  																			elif len(_snake_case		) == 1:
  																									_a     =			tf.distribute.OneDeviceStrategy(device='/gpu:0'		)
  																			elif len(_snake_case		) > 1:
  																									# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
  																									_a     =			tf.distribute.MirroredStrategy()
  																			else:
  																									raise ValueError('Cannot find the proper strategy, please check your environment properties.'		)
  
  													return strategy
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				int		):
  													requires_backends(self		,				['tf']		)
  													return self._setup_strategy
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													requires_backends(self		,				['tf']		)
  													return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				List[Any]		):
  													return False  # TF Logging is handled by Keras not the Trainer
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Optional[Any]		):
  													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.'		)
  													_a     =			self.per_gpu_train_batch_size or self.per_device_train_batch_size
  													return per_device_batch_size * self.n_replicas
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				str		):
  													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.'		)
  													_a     =			self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
  													return per_device_batch_size * self.n_replicas
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				str		):
  													requires_backends(self		,				['tf']		)
  													warnings.warn(
  													    'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'		,				_snake_case		,				)
  													return self._setup_strategy.num_replicas_in_sync
  
  
  
style_context: 
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=256				)    ->  Dict:
  						_a     =			min(img.size				)
  
  						if s < target_image_size:
  												raise ValueError(f"""min dim for image {s} < {target_image_size}"""				)
  
  						_a     =			target_image_size / s
  						_a     =			(round(r * img.size[1]				), round(r * img.size[0]				))
  						_a     =			TF.resize(_UpperCAmelCase							,     _UpperCAmelCase							,     interpolation=PIL.Image.LANCZOS				)
  						_a     =			TF.center_crop(_UpperCAmelCase							,     output_size=2 * [target_image_size]				)
  						_a     =			torch.unsqueeze(T.ToTensor()(_UpperCAmelCase				)							,     0				)
  						return img
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  List[Any]:
  						_a     =			2.0 * x - 1.0
  						return x
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=True							,     _UpperCAmelCase="RGB"				)    ->  Optional[Any]:
  						_a     =			x.detach().cpu()
  						if process:
  												_a     =			post_process_tensor(_UpperCAmelCase				)
  						_a     =			x.numpy()
  						if process:
  												_a     =			(255 * x).astype(np.uinta				)
  						_a     =			Image.fromarray(_UpperCAmelCase				)
  						if not x.mode == mode:
  												_a     =			x.convert(_UpperCAmelCase				)
  						return x
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  Optional[Any]:
  						_a     =			torch.clamp(_UpperCAmelCase							,     -1.0							,     1.0				)
  						_a     =			(x + 1.0) / 2.0
  						_a     =			x.permute(1							,     2							,     0				)
  						return x
  
  
  
  
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  str:
  						_a     =			post_process_tensor(x.squeeze()				)
  						return x.permute(2							,     0							,     1				).unsqueeze(0				)
  
  
  