
lowerCamelCase_				:							str       =    [0.33, 0.44, 0.55, 0.44, 0.33]
lowerCamelCase_				:							List[str]       =    3
lowerCamelCase_				:							List[str]       =    5

# Buffer Space (with Parameters Space)
lowerCamelCase_				:							list[float]       =    []
lowerCamelCase_				:							list[float]       =    []

# Machine Time
lowerCamelCase_				:							int       =    0
def        __lowercase(							__snake_case				:		List[str]					)       ->   int:
       global buffer_space, params_space, machine_time, K, m, t

       # Choosing Dynamical Systems (All)
       for key, value in enumerate(__snake_case					):
              # Evolution Parameter
              __snake_case						       =   float(seed / value					)

              # Control Theory: Orbit Change
              __snake_case						       =   (buffer_space[(key + 1) % m] + e) % 1

              # Control Theory: Trajectory Change
              __snake_case						       =   (params_space[key] + e) % 1 + 3

              # Modification (Transition Function) - Jumps
              __snake_case						       =   round(float(r * value * (1 - value)					)    ,10					)
              __snake_case						       =   r  # Saving to Parameters Space

       # Logistic Map
       assert max(__snake_case					) < 1
       assert max(__snake_case					) < 4

       # Machine Time
       machine_time += 1
def        __lowercase(							)       ->   str:
       global buffer_space, params_space, machine_time, K, m, t

       # PRNG (Xorshift by George Marsaglia)
       def xorshift(__snake_case				:		Any    ,__snake_case				:		Tuple					):
              x ^= y >> 13
              y ^= x << 17
              x ^= y >> 5
              return x

       # Choosing Dynamical Systems (Increment)
       __snake_case						       =   machine_time % m

       # Evolution (Time Length)
       for _ in range(0    ,__snake_case					):
              # Variables (Position + Parameters)
              __snake_case						       =   params_space[key]
              __snake_case						       =   buffer_space[key]

              # Modification (Transition Function) - Flow
              __snake_case						       =   round(float(r * value * (1 - value)					)    ,10					)
              __snake_case						       =   (machine_time * 0.01 + r * 1.01) % 1 + 3

       # Choosing Chaotic Data
       __snake_case						       =   int(buffer_space[(key + 2) % m] * (10**10)					)
       __snake_case						       =   int(buffer_space[(key - 2) % m] * (10**10)					)

       # Machine Time
       machine_time += 1

       return xorshift(__snake_case    ,__snake_case					) % 0XFF_FFF_FFF
def        __lowercase(							)       ->   List[str]:
       global buffer_space, params_space, machine_time, K, m, t

       __snake_case						       =   K
       __snake_case						       =   [0] * m
       __snake_case						       =   0


if __name__ == "__main__":
 # Initialization
 reset()

 # Pushing Data (Input)
 import random

 lowerCamelCase_				:							List[Any]       =    random.sample(range(0Xffffffff), 100)
 for chunk in message:
  push(chunk)

 # for controlling
 lowerCamelCase_				:							List[str]       =    ""

 # Pulling Data (Output)
 while inp in ("e", "E"):
  print(f"""{format(pull(), '#04x')}""")
  print(buffer_space)
  print(params_space)
  lowerCamelCase_				:							List[str]       =    input("(e)exit? ").strip()


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

if is_tf_available():
 import tensorflow as tf







@dataclass
class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""tf"""
     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Name of TPU"""}							,					)

     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Zone of TPU"""}							,					)

     lowercase__				  =			field(
         default=lowerCamelCase							,					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=lowerCamelCase							,					metadata={"""help""": """Whether to activate the XLA compilation or not"""}					)

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

            __snake_case						       =   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:
                   __snake_case						       =   tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
            else:
                   try:
                          if self.tpu_name:
                                 __snake_case						       =   tf.distribute.cluster_resolver.TPUClusterResolver(
                                     self.tpu_name							,       zone=self.tpu_zone							,       project=self.gcp_project    )
                          else:
                                 __snake_case						       =   tf.distribute.cluster_resolver.TPUClusterResolver()
                   except ValueError:
                          if self.tpu_name:
                                 raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''    )
                          else:
                                 __snake_case						       =   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(SCREAMING_SNAKE_CASE_    )
                          tf.tpu.experimental.initialize_tpu_system(SCREAMING_SNAKE_CASE_    )

                          __snake_case						       =   tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE_    )

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

            return strategy

     @property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            return self._setup_strategy

     @property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            return self._setup_strategy.num_replicas_in_sync

     @property
     def 						__lowerCamelCase				(  self    ):
            return False  # TF Logging is handled by Keras not the Trainer

     @property
     def 						__lowerCamelCase				(  self    ):
            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.'    )
            __snake_case						       =   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    ):
            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.'    )
            __snake_case						       =   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    ):
            requires_backends(self							,       ['tf']    )
            warnings.warn(
                'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'							,       SCREAMING_SNAKE_CASE_							,       )
            return self._setup_strategy.num_replicas_in_sync
