
"""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


_A            = logging.get_logger(__name__)

if is_tf_available():
					import tensorflow as tf






@dataclass
class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	'tf'
	lowercase_        =	field(
	    default=__UpperCAmelCase				,       metadata={'help': 'Name of TPU'}				,       )

	lowercase_        =	field(
	    default=__UpperCAmelCase				,       metadata={'help': 'Zone of TPU'}				,       )

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

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

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




	@cached_property
	def 			_UpperCamelCase    ( self					)		->							Tuple["tf.distribute.Strategy", int]:
			requires_backends(self		, ['tf']					)
			logger.info('Tensorflow: setting up strategy'					)

			lowerCamelCase     :			Union[str, 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:
					lowerCamelCase     :			Optional[int]		=    tf.distribute.OneDeviceStrategy(device='/cpu:0'					)
			else:
					try:
							if self.tpu_name:
									lowerCamelCase     :			int		=    tf.distribute.cluster_resolver.TPUClusterResolver(
									    self.tpu_name		, zone=self.tpu_zone		, project=self.gcp_project					)
							else:
									lowerCamelCase     :			Optional[int]		=    tf.distribute.cluster_resolver.TPUClusterResolver()
					except ValueError:
							if self.tpu_name:
									raise RuntimeError(F"""Couldn't connect to TPU {self.tpu_name}!"""					)
							else:
									lowerCamelCase     :			Optional[int]		=    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     :			List[str]		=    tf.distribute.TPUStrategy(UpperCAmelCase_					)

					elif len(UpperCAmelCase_					) == 0:
							lowerCamelCase     :			str		=    tf.distribute.OneDeviceStrategy(device='/cpu:0'					)
					elif len(UpperCAmelCase_					) == 1:
							lowerCamelCase     :			Dict		=    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     :			Any		=    tf.distribute.MirroredStrategy()
					else:
							raise ValueError('Cannot find the proper strategy, please check your environment properties.'					)

			return strategy




	@property
	def 			_UpperCamelCase    ( self					)		->							"tf.distribute.Strategy":
			requires_backends(self		, ['tf']					)
			return self._setup_strategy




	@property
	def 			_UpperCamelCase    ( self					)		->							int:
			requires_backends(self		, ['tf']					)
			return self._setup_strategy.num_replicas_in_sync




	@property
	def 			_UpperCamelCase    ( self					)		->							str:
			return False  # TF Logging is handled by Keras not the Trainer




	@property
	def 			_UpperCamelCase    ( self					)		->							int:
			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     :			str		=    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					)		->							int:
			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     :			List[Any]		=    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					)		->							int:
			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














"""simple docstring"""







from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap


@total_ordering
class     __UpperCAmelCase:




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       word
												lowercase__				:      Any=       count




						def __eq__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count == other.count




						def __lt__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count < other.count





def 						lowercase__(A					,		A						)				->list[str]:




						"""simple docstring"""



						lowercase__				:      Heap[WordCount]=       Heap()
						lowercase__				:      str=       Counter(A						)
						heap.build_max_heap(
						    [WordCount(A					,		A						) for word, count in count_by_word.items()]						)
						return [heap.extract_max().word for _ in range(min(A					,		len(A						)						)						)]


if __name__ == "__main__":
			import doctest

			doctest.testmod()






