





"""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_	:		Union[str, Any]         =   logging.get_logger(__name__)

if is_tf_available():
   import tensorflow as tf

@dataclass
class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					List[Any]             =  """tf"""
      lowercase_ :					Optional[str]             =  field(
          default=UpperCAmelCase__     ,  metadata={"""help""": """Name of TPU"""}     ,  )

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

      lowercase_ :					Optional[str]             =  field(
          default=UpperCAmelCase__     ,  metadata={"""help""": """Name of Cloud TPU-enabled project"""}     ,  )

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

      lowercase_ :					bool             =  field(default=UpperCAmelCase__     ,  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'    )

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

            A_    :			int									=    tf.distribute.TPUStrategy(snake_case_    )

          elif len(snake_case_    ) == 0:
            A_    :			List[str]									=    tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
          elif len(snake_case_    ) == 1:
            A_    :			List[str]									=    tf.distribute.OneDeviceStrategy(device='/gpu:0'    )
          elif len(snake_case_    ) > 1:
            # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
            A_    :			List[str]									=    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.'    )
        A_    :			Any									=    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.'    )
        A_    :			List[str]									=    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.'   , snake_case_   , )
        return self._setup_strategy.num_replicas_in_sync


'''simple docstring'''







from __future__ import annotations

from collections.abc import Generator






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	if not isinstance(snake_case_							,       snake_case_	) or n < 1:
		raise Exception("""Sequence only defined for positive integers"""	)

	yield n
	while n != 1:
		if n % 2 == 0:
			n //= 2
		else:
			_lowercase							=     3 * n + 1
		yield n






def 					_SCREAMING_SNAKE_CASE		(						):
	_lowercase							=     43
	_lowercase							=     tuple(collatz_sequence(snake_case_	)	)
	print(snake_case_	)
	print(F"""Collatz sequence from {n} took {len(snake_case_	)} steps."""	)


if __name__ == "__main__":
							main()




