

'''simple docstring'''
from typing import TYPE_CHECKING

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


_snake_case     						=						{
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _snake_case     						=						[
        'Constraint',
        'ConstraintListState',
        'DisjunctiveConstraint',
        'PhrasalConstraint',
    ]
    _snake_case     						=						[
        'BeamHypotheses',
        'BeamScorer',
        'BeamSearchScorer',
        'ConstrainedBeamSearchScorer',
    ]
    _snake_case     						=						[
        'EpsilonLogitsWarper',
        'EtaLogitsWarper',
        'ForcedBOSTokenLogitsProcessor',
        'ForcedEOSTokenLogitsProcessor',
        'HammingDiversityLogitsProcessor',
        'InfNanRemoveLogitsProcessor',
        'LogitsProcessor',
        'LogitsProcessorList',
        'LogitsWarper',
        'MinLengthLogitsProcessor',
        'MinNewTokensLengthLogitsProcessor',
        'NoBadWordsLogitsProcessor',
        'NoRepeatNGramLogitsProcessor',
        'PrefixConstrainedLogitsProcessor',
        'RepetitionPenaltyLogitsProcessor',
        'SequenceBiasLogitsProcessor',
        'EncoderRepetitionPenaltyLogitsProcessor',
        'TemperatureLogitsWarper',
        'TopKLogitsWarper',
        'TopPLogitsWarper',
        'TypicalLogitsWarper',
        'EncoderNoRepeatNGramLogitsProcessor',
        'ExponentialDecayLengthPenalty',
        'LogitNormalization',
    ]
    _snake_case     						=						[
        'MaxNewTokensCriteria',
        'MaxLengthCriteria',
        'MaxTimeCriteria',
        'StoppingCriteria',
        'StoppingCriteriaList',
        'validate_stopping_criteria',
    ]
    _snake_case     						=						[
        'GenerationMixin',
        'top_k_top_p_filtering',
        'GreedySearchEncoderDecoderOutput',
        'GreedySearchDecoderOnlyOutput',
        'SampleEncoderDecoderOutput',
        'SampleDecoderOnlyOutput',
        'BeamSearchEncoderDecoderOutput',
        'BeamSearchDecoderOnlyOutput',
        'BeamSampleEncoderDecoderOutput',
        'BeamSampleDecoderOnlyOutput',
        'ContrastiveSearchEncoderDecoderOutput',
        'ContrastiveSearchDecoderOnlyOutput',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _snake_case     						=						[
        'TFForcedBOSTokenLogitsProcessor',
        'TFForcedEOSTokenLogitsProcessor',
        'TFLogitsProcessor',
        'TFLogitsProcessorList',
        'TFLogitsWarper',
        'TFMinLengthLogitsProcessor',
        'TFNoBadWordsLogitsProcessor',
        'TFNoRepeatNGramLogitsProcessor',
        'TFRepetitionPenaltyLogitsProcessor',
        'TFTemperatureLogitsWarper',
        'TFTopKLogitsWarper',
        'TFTopPLogitsWarper',
        'TFForceTokensLogitsProcessor',
        'TFSuppressTokensAtBeginLogitsProcessor',
        'TFSuppressTokensLogitsProcessor',
    ]
    _snake_case     						=						[
        'TFGenerationMixin',
        'tf_top_k_top_p_filtering',
        'TFGreedySearchDecoderOnlyOutput',
        'TFGreedySearchEncoderDecoderOutput',
        'TFSampleEncoderDecoderOutput',
        'TFSampleDecoderOnlyOutput',
        'TFBeamSearchEncoderDecoderOutput',
        'TFBeamSearchDecoderOnlyOutput',
        'TFBeamSampleEncoderDecoderOutput',
        'TFBeamSampleDecoderOnlyOutput',
        'TFContrastiveSearchEncoderDecoderOutput',
        'TFContrastiveSearchDecoderOnlyOutput',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _snake_case     						=						[
        'FlaxForcedBOSTokenLogitsProcessor',
        'FlaxForcedEOSTokenLogitsProcessor',
        'FlaxLogitsProcessor',
        'FlaxLogitsProcessorList',
        'FlaxLogitsWarper',
        'FlaxMinLengthLogitsProcessor',
        'FlaxTemperatureLogitsWarper',
        'FlaxTopKLogitsWarper',
        'FlaxTopPLogitsWarper',
    ]
    _snake_case     						=						[
        'FlaxGenerationMixin',
        'FlaxGreedySearchOutput',
        'FlaxSampleOutput',
        'FlaxBeamSearchOutput',
    ]

if TYPE_CHECKING:
    from .configuration_utils import GenerationConfig
    from .streamers import TextIteratorStreamer, TextStreamer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
        from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
        from .logits_process import (
            EncoderNoRepeatNGramLogitsProcessor,
            EncoderRepetitionPenaltyLogitsProcessor,
            EpsilonLogitsWarper,
            EtaLogitsWarper,
            ExponentialDecayLengthPenalty,
            ForcedBOSTokenLogitsProcessor,
            ForcedEOSTokenLogitsProcessor,
            HammingDiversityLogitsProcessor,
            InfNanRemoveLogitsProcessor,
            LogitNormalization,
            LogitsProcessor,
            LogitsProcessorList,
            LogitsWarper,
            MinLengthLogitsProcessor,
            MinNewTokensLengthLogitsProcessor,
            NoBadWordsLogitsProcessor,
            NoRepeatNGramLogitsProcessor,
            PrefixConstrainedLogitsProcessor,
            RepetitionPenaltyLogitsProcessor,
            SequenceBiasLogitsProcessor,
            TemperatureLogitsWarper,
            TopKLogitsWarper,
            TopPLogitsWarper,
            TypicalLogitsWarper,
        )
        from .stopping_criteria import (
            MaxLengthCriteria,
            MaxNewTokensCriteria,
            MaxTimeCriteria,
            StoppingCriteria,
            StoppingCriteriaList,
            validate_stopping_criteria,
        )
        from .utils import (
            BeamSampleDecoderOnlyOutput,
            BeamSampleEncoderDecoderOutput,
            BeamSearchDecoderOnlyOutput,
            BeamSearchEncoderDecoderOutput,
            ContrastiveSearchDecoderOnlyOutput,
            ContrastiveSearchEncoderDecoderOutput,
            GenerationMixin,
            GreedySearchDecoderOnlyOutput,
            GreedySearchEncoderDecoderOutput,
            SampleDecoderOnlyOutput,
            SampleEncoderDecoderOutput,
            top_k_top_p_filtering,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tf_logits_process import (
            TFForcedBOSTokenLogitsProcessor,
            TFForcedEOSTokenLogitsProcessor,
            TFForceTokensLogitsProcessor,
            TFLogitsProcessor,
            TFLogitsProcessorList,
            TFLogitsWarper,
            TFMinLengthLogitsProcessor,
            TFNoBadWordsLogitsProcessor,
            TFNoRepeatNGramLogitsProcessor,
            TFRepetitionPenaltyLogitsProcessor,
            TFSuppressTokensAtBeginLogitsProcessor,
            TFSuppressTokensLogitsProcessor,
            TFTemperatureLogitsWarper,
            TFTopKLogitsWarper,
            TFTopPLogitsWarper,
        )
        from .tf_utils import (
            TFBeamSampleDecoderOnlyOutput,
            TFBeamSampleEncoderDecoderOutput,
            TFBeamSearchDecoderOnlyOutput,
            TFBeamSearchEncoderDecoderOutput,
            TFContrastiveSearchDecoderOnlyOutput,
            TFContrastiveSearchEncoderDecoderOutput,
            TFGenerationMixin,
            TFGreedySearchDecoderOnlyOutput,
            TFGreedySearchEncoderDecoderOutput,
            TFSampleDecoderOnlyOutput,
            TFSampleEncoderDecoderOutput,
            tf_top_k_top_p_filtering,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .flax_logits_process import (
            FlaxForcedBOSTokenLogitsProcessor,
            FlaxForcedEOSTokenLogitsProcessor,
            FlaxLogitsProcessor,
            FlaxLogitsProcessorList,
            FlaxLogitsWarper,
            FlaxMinLengthLogitsProcessor,
            FlaxTemperatureLogitsWarper,
            FlaxTopKLogitsWarper,
            FlaxTopPLogitsWarper,
        )
        from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
    import sys

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









'''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






