





"""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


UpperCamelCase_           =logging.get_logger(__name__)

if is_tf_available():
						import tensorflow as tf




@dataclass
class  _a						(      _lowerCAmelCase				):
				UpperCamelCase								=    '''tf'''
				UpperCamelCase								=    field(
				    default=_lowerCAmelCase	,				metadata={'''help''': '''Name of TPU'''}	,				)

				UpperCamelCase								=    field(
				    default=_lowerCAmelCase	,				metadata={'''help''': '''Zone of TPU'''}	,				)

				UpperCamelCase								=    field(
				    default=_lowerCAmelCase	,				metadata={'''help''': '''Name of Cloud TPU-enabled project'''}	,				)

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

				UpperCamelCase								=    field(default=_lowerCAmelCase	,				metadata={'''help''': '''Whether to activate the XLA compilation or not'''}				)






				@cached_property
				def 				snake_case	( self		:    Union[str, Any]							)	->      Tuple["tf.distribute.Strategy", int]:




										'''simple docstring'''



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

										_UpperCamelCase			:					List[Any]     =    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:
																_UpperCamelCase			:					Tuple     =    tf.distribute.OneDeviceStrategy(device='''/cpu:0'''							)
										else:
																try:
																						if self.tpu_name:
																												_UpperCamelCase			:					Dict     =    tf.distribute.cluster_resolver.TPUClusterResolver(
																												    self.tpu_name, zone=self.tpu_zone, project=self.gcp_project							)
																						else:
																												_UpperCamelCase			:					List[str]     =    tf.distribute.cluster_resolver.TPUClusterResolver()
																except ValueError:
																						if self.tpu_name:
																												raise RuntimeError(f"""Couldn't connect to TPU {self.tpu_name}!"""							)
																						else:
																												_UpperCamelCase			:					str     =    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(lowerCAmelCase__							)
																						tf.tpu.experimental.initialize_tpu_system(lowerCAmelCase__							)

																						_UpperCamelCase			:					Optional[Any]     =    tf.distribute.TPUStrategy(lowerCAmelCase__							)

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

										return strategy






				@property
				def 				snake_case	( self		:    Dict							)	->      "tf.distribute.Strategy":




										'''simple docstring'''



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






				@property
				def 				snake_case	( self		:    Optional[Any]							)	->      int:




										'''simple docstring'''



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






				@property
				def 				snake_case	( self		:    Optional[int]							)	->      Tuple:




										'''simple docstring'''



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






				@property
				def 				snake_case	( self		:    List[str]							)	->      int:




										'''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.'''							)
										_UpperCamelCase			:					Union[str, Any]     =    self.per_gpu_train_batch_size or self.per_device_train_batch_size
										return per_device_batch_size * self.n_replicas






				@property
				def 				snake_case	( self		:    List[str]							)	->      int:




										'''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.'''							)
										_UpperCamelCase			:					Dict     =    self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
										return per_device_batch_size * self.n_replicas






				@property
				def 				snake_case	( self		:    str							)	->      int:




										'''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.''', lowerCAmelCase__, )
										return self._setup_strategy.num_replicas_in_sync










"""simple docstring"""




from functools import lru_cache





@lru_cache
def        a_				(							_lowercase   ):
						if not isinstance(_lowercase       ,						_lowercase   ):
												raise TypeError('''k must be an integer.'''   )
						if k < 0:
												raise ValueError('''k integer must be greater or equal to zero.'''   )
						if k == 0:
												return 0
						elif k == 1:
												return 1
						else:
												return fibonacci(k - 1   ) + fibonacci(k - 2   )





def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Any     =    len(_lowercase   )
						# Find m such that F_m >= n where F_i is the i_th fibonacci number.
						_UpperCamelCase			:					Optional[int]     =    0
						while True:
												if fibonacci(_lowercase   ) >= len_list:
																		_UpperCamelCase			:					List[Any]     =    i
																		break
												i += 1
						_UpperCamelCase			:					Tuple     =    0
						while fibb_k > 0:
												_UpperCamelCase			:					Union[str, Any]     =    min(
												    offset + fibonacci(fibb_k - 1   )       ,						len_list - 1   )  # Prevent out of range
												_UpperCamelCase			:					Any     =    arr[index_k]
												if item_k_1 == val:
																		return index_k
												elif val < item_k_1:
																		fibb_k -= 1
												elif val > item_k_1:
																		offset += fibonacci(fibb_k - 1   )
																		fibb_k -= 2
						else:
												return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()



