






"""simple docstring"""







from typing import TYPE_CHECKING

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


a :				Optional[Any]      					=		{"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a :				str      					=		["""T5Tokenizer"""]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a :				List[str]      					=		["""T5TokenizerFast"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a :				Any      					=		[
			    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """T5EncoderModel""",
			    """T5ForConditionalGeneration""",
			    """T5Model""",
			    """T5PreTrainedModel""",
			    """load_tf_weights_in_t5""",
			    """T5ForQuestionAnswering""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a :				Dict      					=		[
			    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFT5EncoderModel""",
			    """TFT5ForConditionalGeneration""",
			    """TFT5Model""",
			    """TFT5PreTrainedModel""",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a :				List[Any]      					=		[
			    """FlaxT5EncoderModel""",
			    """FlaxT5ForConditionalGeneration""",
			    """FlaxT5Model""",
			    """FlaxT5PreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_ta import TaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_ta_fast import TaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ta import (
						    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TaEncoderModel,
						    TaForConditionalGeneration,
						    TaForQuestionAnswering,
						    TaModel,
						    TaPreTrainedModel,
						    load_tf_weights_in_ta,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_ta import (
						    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFTaEncoderModel,
						    TFTaForConditionalGeneration,
						    TFTaModel,
						    TFTaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_ta import (
						    FlaxTaEncoderModel,
						    FlaxTaForConditionalGeneration,
						    FlaxTaModel,
						    FlaxTaPreTrainedModel,
						)


else:
			import sys

			a :				Optional[int]      					=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













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


UpperCamelCase__          =logging.get_logger(__name__)

if is_tf_available():
       import tensorflow as tf





@dataclass
class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       __snake_case       	=			'tf'
       __snake_case       	=			field(
           default=__lowercase     ,   metadata={'help': 'Name of TPU'}     ,   )

       __snake_case       	=			field(
           default=__lowercase     ,   metadata={'help': 'Zone of TPU'}     ,   )

       __snake_case       	=			field(
           default=__lowercase     ,   metadata={'help': 'Name of Cloud TPU-enabled project'}     ,   )

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

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




       @cached_property
       def 			UpperCamelCase_	(					self							)	->   Tuple["tf.distribute.Strategy", int]:
            requires_backends(self			,  ["tf"]							)
            logger.info("Tensorflow: setting up strategy"							)

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

                      _SCREAMING_SNAKE_CASE				:		List[str]  			=							tf.distribute.TPUStrategy(__lowerCamelCase							)

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

            return strategy




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




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




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




       @property
       def 			UpperCamelCase_	(					self							)	->   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."							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							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:
            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."							)
            _SCREAMING_SNAKE_CASE				:		int  			=							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							)	->   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."			,  __lowerCamelCase			,  )
            return self._setup_strategy.num_replicas_in_sync