



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


a_								=   logging.get_logger(__name__)

if is_tf_available():
		import tensorflow as tf



@dataclass
class 	__lowerCAmelCase       (		lowercase_				):
	lowerCAmelCase__       =					"""tf"""
	lowerCAmelCase__       =					field(
	    default=lowercase_					,		metadata={"""help""": """Name of TPU"""}					,		)

	lowerCAmelCase__       =					field(
	    default=lowercase_					,		metadata={"""help""": """Zone of TPU"""}					,		)

	lowerCAmelCase__       =					field(
	    default=lowercase_					,		metadata={"""help""": """Name of Cloud TPU-enabled project"""}					,		)

	lowerCAmelCase__       =					field(
	    default=1.0					,		metadata={"""help""": """Power for the Polynomial decay LR scheduler."""}					,		)

	lowerCAmelCase__       =					field(default=lowercase_					,		metadata={"""help""": """Whether to activate the XLA compilation or not"""}				)




	@cached_property
	def     lowerCamelCase (  self			):



				'''simple docstring'''
				requires_backends(self		,			['''tf''']			)
				logger.info('''Tensorflow: setting up strategy'''			)

				__lowerCamelCase  							=      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:
							__lowerCamelCase  							=      tf.distribute.OneDeviceStrategy(device='''/cpu:0'''			)
				else:
							try:
										if self.tpu_name:
													__lowerCamelCase  							=      tf.distribute.cluster_resolver.TPUClusterResolver(
													    self.tpu_name		,			zone=self.tpu_zone		,			project=self.gcp_project			)
										else:
													__lowerCamelCase  							=      tf.distribute.cluster_resolver.TPUClusterResolver()
							except ValueError:
										if self.tpu_name:
													raise RuntimeError(F"""Couldn't connect to TPU {self.tpu_name}!"""			)
										else:
													__lowerCamelCase  							=      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(__UpperCamelCase			)
										tf.tpu.experimental.initialize_tpu_system(__UpperCamelCase			)

										__lowerCamelCase  							=      tf.distribute.TPUStrategy(__UpperCamelCase			)

							elif len(__UpperCamelCase			) == 0:
										__lowerCamelCase  							=      tf.distribute.OneDeviceStrategy(device='''/cpu:0'''			)
							elif len(__UpperCamelCase			) == 1:
										__lowerCamelCase  							=      tf.distribute.OneDeviceStrategy(device='''/gpu:0'''			)
							elif len(__UpperCamelCase			) > 1:
										# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
										__lowerCamelCase  							=      tf.distribute.MirroredStrategy()
							else:
										raise ValueError('''Cannot find the proper strategy, please check your environment properties.'''			)

				return strategy




	@property
	def     lowerCamelCase (  self			):



				'''simple docstring'''
				requires_backends(self		,			['''tf''']			)
				return self._setup_strategy




	@property
	def     lowerCamelCase (  self			):



				'''simple docstring'''
				requires_backends(self		,			['''tf''']			)
				return self._setup_strategy.num_replicas_in_sync




	@property
	def     lowerCamelCase (  self			):



				'''simple docstring'''
				return False  # TF Logging is handled by Keras not the Trainer




	@property
	def     lowerCamelCase (  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.'''			)
				__lowerCamelCase  							=      self.per_gpu_train_batch_size or self.per_device_train_batch_size
				return per_device_batch_size * self.n_replicas




	@property
	def     lowerCamelCase (  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.'''			)
				__lowerCamelCase  							=      self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
				return per_device_batch_size * self.n_replicas




	@property
	def     lowerCamelCase (  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.'''		,			__UpperCamelCase		,			)
				return self._setup_strategy.num_replicas_in_sync











def 						a__     (						_UpperCamelCase     :	int							):
   if not isinstance(_UpperCamelCase	,_UpperCamelCase							):
      __lowerCamelCase  							=      (
          '''operation can not be conducted on a object of type '''
          F"""{type(_UpperCamelCase							).__name__}"""
      )
      raise TypeError(_UpperCamelCase							)
   __lowerCamelCase  							=      ''''''
   for _ in range(0	,32							):
      bit_string += str(number % 2							)
      __lowerCamelCase  							=      number >> 1
   return bit_string





def 						a__     (						_UpperCamelCase     :	int							):
   if number < 0:
      raise ValueError('''the value of input must be positive'''							)
   elif isinstance(_UpperCamelCase	,_UpperCamelCase							):
      raise TypeError('''Input value must be a \'int\' type'''							)
   elif isinstance(_UpperCamelCase	,_UpperCamelCase							):
      raise TypeError('''\'<\' not supported between instances of \'str\' and \'int\''''							)
   __lowerCamelCase  							=      0
   # iterator over [1 to 32],since we are dealing with 32 bit integer
   for _ in range(1	,33							):
      # left shift the bits by unity
      __lowerCamelCase  							=      result << 1
      # get the end bit
      __lowerCamelCase  							=      number % 2
      # right shift the bits by unity
      __lowerCamelCase  							=      number >> 1
      # add that bit to our ans
      __lowerCamelCase  							=      result | end_bit
   return get_reverse_bit_string(_UpperCamelCase							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






