"""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


_SCREAMING_SNAKE_CASE	:		Any   =   logging.get_logger(__name__)

if is_tf_available():
     import tensorflow as tf






@dataclass
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Tuple    =    "tf"
   __UpperCAmelCase	:							Tuple    =    field(
       default=UpperCAmelCase    ,				metadata={"help": "Name of TPU"}    ,				)

   __UpperCAmelCase	:							List[Any]    =    field(
       default=UpperCAmelCase    ,				metadata={"help": "Zone of TPU"}    ,				)

   __UpperCAmelCase	:							Union[str, Any]    =    field(
       default=UpperCAmelCase    ,				metadata={"help": "Name of Cloud TPU-enabled project"}    ,				)

   __UpperCAmelCase	:							List[str]    =    field(
       default=1.0    ,				metadata={"help": "Power for the Polynomial decay LR scheduler."}    ,				)

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







   @cached_property
   def    _A				(	self	):


      '''simple docstring'''

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

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

            __lowerCamelCase    		=					tf.distribute.TPUStrategy(_SCREAMING_SNAKE_CASE	)

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

      return strategy







   @property
   def    _A				(	self	):


      '''simple docstring'''

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







   @property
   def    _A				(	self	):


      '''simple docstring'''

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







   @property
   def    _A				(	self	):


      '''simple docstring'''

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







   @property
   def    _A				(	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.'	)
      __lowerCamelCase    		=					self.per_gpu_train_batch_size or self.per_device_train_batch_size
      return per_device_batch_size * self.n_replicas







   @property
   def    _A				(	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.'	)
      __lowerCamelCase    		=					self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
      return per_device_batch_size * self.n_replicas







   @property
   def    _A				(	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.' ,				_SCREAMING_SNAKE_CASE ,				)
      return self._setup_strategy.num_replicas_in_sync














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase						= {
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= ['BartTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BartForCausalLM',
						    'BartForConditionalGeneration',
						    'BartForQuestionAnswering',
						    'BartForSequenceClassification',
						    'BartModel',
						    'BartPretrainedModel',
						    'PretrainedBartModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'TFBartForConditionalGeneration',
						    'TFBartForSequenceClassification',
						    'TFBartModel',
						    'TFBartPretrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'FlaxBartDecoderPreTrainedModel',
						    'FlaxBartForCausalLM',
						    'FlaxBartForConditionalGeneration',
						    'FlaxBartForQuestionAnswering',
						    'FlaxBartForSequenceClassification',
						    'FlaxBartModel',
						    'FlaxBartPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						__lowerCamelCase						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)