




'''simple docstring'''







import functools
from math import sqrt
from time import time







def 	_A			(   UpperCAmelCase			,*UpperCAmelCase			,**UpperCAmelCase	):






   '''simple docstring'''







   A__         =    time()
   A__         =    func(*UpperCAmelCase			,**UpperCAmelCase	)
   A__         =    time()
   if int(end - start	) > 0:
      print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""	)
   else:
      print(F"""{func.__name__} runtime: {(end - start) * 1000:0.4f} ms"""	)
   return output







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   if n < 0:
      raise Exception('n is negative'	)
   if n == 0:
      return [0]
   A__         =    [0, 1]
   for _ in range(n - 1	):
      fib.append(fib[-1] + fib[-2]	)
   return fib







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   def fib_recursive_term(UpperCAmelCase	) -> int:
      if i < 0:
         raise Exception('n is negative'	)
      if i < 2:
         return i
      return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

   if n < 0:
      raise Exception('n is negative'	)
   return [fib_recursive_term(UpperCAmelCase	) for i in range(n + 1	)]







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   @functools.cache
   def fib_recursive_term(UpperCAmelCase	) -> int:
      if i < 0:
         raise Exception('n is negative'	)
      if i < 2:
         return i
      return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

   if n < 0:
      raise Exception('n is negative'	)
   return [fib_recursive_term(UpperCAmelCase	) for i in range(n + 1	)]







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   if n < 0:
      raise Exception('n is negative'	)
   # Cache must be outside recursuive function
   # other it will reset every time it calls itself.
   A__         =    {0: 0, 1: 1, 2: 1}  # Prefilled cache

   def rec_fn_memoized(UpperCAmelCase	) -> int:
      if num in cache:
         return cache[num]

      A__         =    rec_fn_memoized(num - 1	) + rec_fn_memoized(num - 2	)
      A__         =    value
      return value

   return [rec_fn_memoized(UpperCAmelCase	) for i in range(n + 1	)]







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   if n < 0:
      raise Exception('n is negative'	)
   if n >= 1475:
      raise Exception('n is too large'	)
   A__         =    sqrt(5	)
   A__         =    (1 + sqrt_a) / 2
   return [round(phi**i / sqrt_a	) for i in range(n + 1	)]


if __name__ == "__main__":
    lowerCAmelCase_    	=							3_0
    time_func(fib_iterative, num)
    time_func(fib_recursive, num)  # Around 3s runtime
    time_func(fib_recursive_cached, num)  # Around 0ms runtime
    time_func(fib_memoization, num)
    time_func(fib_binet, num)








'''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


lowerCAmelCase_    	=							logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf

@dataclass
class   _snake_case( UpperCAmelCase			):
 __snake_case:       Optional[Any]									=				'''tf'''
 __snake_case:       Optional[str]									=				field(
     default=UpperCAmelCase					,   metadata={'''help''': '''Name of TPU'''}					,   )

 __snake_case:       Optional[str]									=				field(
     default=UpperCAmelCase					,   metadata={'''help''': '''Zone of TPU'''}					,   )

 __snake_case:       Optional[str]									=				field(
     default=UpperCAmelCase					,   metadata={'''help''': '''Name of Cloud TPU-enabled project'''}					,   )

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

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






 @cached_property
 def 	_UpperCamelCase   (self					:     Tuple						)					->      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 	_UpperCamelCase   (self					:     List[Any]						)					->      "tf.distribute.Strategy":






    """simple docstring"""


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






 @property
 def 	_UpperCamelCase   (self					:     Tuple						)					->      int:






    """simple docstring"""


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






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






    """simple docstring"""


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






 @property
 def 	_UpperCamelCase   (self					:     Union[str, 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 	_UpperCamelCase   (self					:     Optional[int]						)					->      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 	_UpperCamelCase   (self					:     Optional[Any]						)					->      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


