

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


__A     						=      logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf



@dataclass
class     lowercase_       (    __lowercase	):
      UpperCamelCase_     :						Optional[int]		 =							"tf"
      UpperCamelCase_     :						Optional[str]		 =							field(
          default=__lowercase  ,				metadata={"help": "Name of TPU"}  ,				)

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

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

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

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



      @cached_property
      def 		UpperCamelCase_						(     self :							Dict						)       ->	Tuple["tf.distribute.Strategy", int]:
        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(A__						)
            tf.tpu.experimental.initialize_tpu_system(A__						)

            _snake_case		=				tf.distribute.TPUStrategy(A__						)

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

        return strategy



      @property
      def 		UpperCamelCase_						(     self :							str						)       ->	"tf.distribute.Strategy":
        requires_backends(self     ,   ['''tf''']						)
        return self._setup_strategy



      @property
      def 		UpperCamelCase_						(     self :							str						)       ->	int:
        requires_backends(self     ,   ['''tf''']						)
        return self._setup_strategy.num_replicas_in_sync



      @property
      def 		UpperCamelCase_						(     self :							Union[str, Any]						)       ->	Union[str, Any]:
        return False  # TF Logging is handled by Keras not the Trainer



      @property
      def 		UpperCamelCase_						(     self :							Any						)       ->	int:
        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 		UpperCamelCase_						(     self :							int						)       ->	int:
        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 		UpperCamelCase_						(     self :							Tuple						)       ->	int:
        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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A     						=      {'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''HubertForCTC''',
     '''HubertForSequenceClassification''',
     '''HubertModel''',
     '''HubertPreTrainedModel''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A     						=      [
     '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFHubertForCTC''',
     '''TFHubertModel''',
     '''TFHubertPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_hubert import (
      HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      HubertForCTC,
      HubertForSequenceClassification,
      HubertModel,
      HubertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_hubert import (
      TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFHubertForCTC,
      TFHubertModel,
      TFHubertPreTrainedModel,
  )


else:
 import sys

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

