




from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_						:					List[Any]			   =       TypeVar('''T''')







class 		__UpperCAmelCase(		Generic[T] ):





     """simple docstring"""



     def __init__(      self :       Union[str, Any]						,				A :       T    )			->			int:






         """simple docstring"""
         a__      =       data  # Assign data
         a__      =       None  # Initialize next as null
         a__      =       None  # Initialize prev as null







class 		__UpperCAmelCase(		Generic[T] ):





     """simple docstring"""



     def __init__(      self :       Optional[Any]    )			->			None:






         """simple docstring"""
         a__      =       None

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]						,				A :       T    )			->			None:






         """simple docstring"""
         if self.head is None:
             a__      =       Node(A    )
         else:
             a__      =       Node(A    )
             a__      =       new_node
             a__      =       self.head
             a__      =       None
             a__      =       new_node

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			T | None:






         """simple docstring"""
         if self.head is None:
             return None
         else:
             assert self.head is not None
             a__      =       self.head.data
             a__      =       self.head.next
             if self.head is not None:
                 a__      =       None
             return temp

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			T | None:






         """simple docstring"""
         return self.head.data if self.head is not None else None

     def __len__(      self :       int    )			->			int:






         """simple docstring"""
         a__      =       self.head
         a__      =       0
         while temp is not None:
             count += 1
             a__      =       temp.next
         return count

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			bool:






         """simple docstring"""
         return self.head is None






     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			None:






         """simple docstring"""
         print("""stack elements are:"""    )
         a__      =       self.head
         while temp is not None:
             print(temp.data						,				end="""->"""    )
             a__      =       temp.next


# Code execution starts here
if __name__ == "__main__":
 # Start with the empty stack
 SCREAMING_SNAKE_CASE_						:					Stack[int]			   =       Stack()

 # Insert 4 at the beginning. So stack becomes 4->None
 print('''Stack operations using Doubly LinkedList''')
 stack.push(4)

 # Insert 5 at the beginning. So stack becomes 4->5->None
 stack.push(5)

 # Insert 6 at the beginning. So stack becomes 4->5->6->None
 stack.push(6)

 # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
 stack.push(7)

 # Print the stack
 stack.print_stack()

 # Print the top element
 print('''\nTop element is ''', stack.top())

 # Print the stack size
 print('''Size of the stack is ''', len(stack))

 # pop the top element
 stack.pop()

 # pop the top element
 stack.pop()

 # two elements have now been popped off
 stack.print_stack()

 # Print True if the stack is empty else False
 print('''\nstack is empty:''', stack.is_empty())












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


SCREAMING_SNAKE_CASE_						:					Optional[Any]			   =       logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf







@dataclass
class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     __UpperCAmelCase	: int						=    """tf"""
     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Name of TPU"""}       ,	)

     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Zone of TPU"""}       ,	)

     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	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=lowercase__       ,	metadata={"""help""": """Whether to activate the XLA compilation or not"""} )

     @cached_property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Tuple["tf.distribute.Strategy", int]:






         """simple docstring"""
         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(A    )
                 tf.tpu.experimental.initialize_tpu_system(A    )

                 a__      =       tf.distribute.TPUStrategy(A    )

             elif len(A    ) == 0:
                 a__      =       tf.distribute.OneDeviceStrategy(device="""/cpu:0"""    )
             elif len(A    ) == 1:
                 a__      =       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`
                 a__      =       tf.distribute.MirroredStrategy()
             else:
                 raise ValueError("""Cannot find the proper strategy, please check your environment properties."""    )

         return strategy

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			"tf.distribute.Strategy":






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

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			int:






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

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			List[Any]:






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

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			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."""    )
         a__      =       self.per_gpu_train_batch_size or self.per_device_train_batch_size
         return per_device_batch_size * self.n_replicas

     @property
     def 				__SCREAMING_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."""    )
         a__      =       self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
         return per_device_batch_size * self.n_replicas






     @property
     def 				__SCREAMING_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."""						,				A						,				)
         return self._setup_strategy.num_replicas_in_sync






