

'''simple docstring'''
import timeit


class      a__						:


   def __init__(				self    ,       _UpperCamelCase						):





      """simple docstring"""





      if isinstance(_UpperCamelCase    ,       _UpperCamelCase						):  # Initializes a matrix identical to the one provided.
         _lowercase    :   str      =     arg
         _lowercase    :   Union[str, Any]      =     len(_UpperCamelCase						)
      else:  # Initializes a square matrix of the given size and set values to zero.
         _lowercase    :   Optional[int]      =     arg
         _lowercase    :   List[str]      =     [[0 for _ in range(self.n						)] for _ in range(self.n						)]






   def __mul__(				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   int      =     Matrix(self.n						)
      for i in range(self.n						):
         for j in range(self.n						):
            for k in range(self.n						):
               matrix.t[i][j] += self.t[i][k] * b.t[k][j]
      return matrix







def       _A	(			snake_case	,							snake_case  )    ->			Tuple:
   _lowercase    :   Union[str, Any]      =     Matrix([[1, 0], [0, 1]]  )
   while b > 0:
      if b & 1:
         matrix *= a
      a *= a
      b >>= 1
   return matrix







def       _A	(			snake_case	,							snake_case	,							snake_case  )    ->			Optional[int]:
   # Trivial Cases
   if n == 1:
      return fa
   elif n == 2:
      return fa
   _lowercase    :   Tuple      =     Matrix([[1, 1], [1, 0]]  )
   _lowercase    :   str      =     modular_exponentiation(snake_case	,							n - 2  )
   return fa * matrix.t[0][0] + fa * matrix.t[0][1]







def       _A	(			snake_case	,							snake_case	,							snake_case  )    ->			List[Any]:
   # Trivial Cases
   if n == 1:
      return fa
   elif n == 2:
      return fa

   _lowercase    :   Dict      =     fa
   _lowercase    :   str      =     fa
   n -= 2

   while n > 0:
      _lowercase  ,   _lowercase    :   Optional[int]      =     fn_a + fn_a, fn_a
      n -= 1

   return fn_a







def       _A	(			)    ->			Any:
   _lowercase    :   Optional[Any]      =     "\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n"
   _lowercase    :   Union[str, Any]      =     "fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"
   _lowercase    :   Tuple      =     timeit.timeit(setup=snake_case	,							stmt=snake_case	,							number=1_00  )
   print("With matrix exponentiation the average execution time is "	,							exec_time / 1_00  )
   return exec_time







def       _A	(			)    ->			List[str]:
   _lowercase    :   Optional[int]      =     "\nfrom random import randint\nfrom __main__ import simple_fibonacci\n"
   _lowercase    :   Dict      =     "simple_fibonacci(randint(1,70000), 1, 1)"
   _lowercase    :   List[Any]      =     timeit.timeit(setup=snake_case	,							stmt=snake_case	,							number=1_00  )
   print(
       "Without matrix exponentiation the average execution time is "	,							exec_time / 1_00  )
   return exec_time







def       _A	(			)    ->			Union[str, Any]:
   matrix_exponentiation_time()
   simple_fibonacci_time()


if __name__ == "__main__":
    main()









'''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


_snake_case     						=						logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf


@dataclass
class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  int			 =							'tf'
   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of TPU'}						,   )

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

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

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

   _SCREAMING_SNAKE_CASE					:  bool			 =							field(default=lowerCamelCase_						,   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"						)

      _lowercase    :   Optional[int]      =     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:
         _lowercase    :   Optional[int]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
      else:
         try:
            if self.tpu_name:
               _lowercase    :   int      =     tf.distribute.cluster_resolver.TPUClusterResolver(
                   self.tpu_name    ,       zone=self.tpu_zone    ,       project=self.gcp_project						)
            else:
               _lowercase    :   Any      =     tf.distribute.cluster_resolver.TPUClusterResolver()
         except ValueError:
            if self.tpu_name:
               raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''						)
            else:
               _lowercase    :   Tuple      =     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						)

            _lowercase    :   List[str]      =     tf.distribute.TPUStrategy(_UpperCamelCase						)

         elif len(_UpperCamelCase						) == 0:
            _lowercase    :   Optional[Any]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
         elif len(_UpperCamelCase						) == 1:
            _lowercase    :   Optional[int]      =     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`
            _lowercase    :   Any      =     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."						)
      _lowercase    :   str      =     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."						)
      _lowercase    :   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 		_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






