






"""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


A_			  =    logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf
@dataclass
class 						__lowerCAmelCase   (			UpperCAmelCase       ):







  '''simple docstring'''


  __lowerCamelCase       :    Any     	=			"tf"
  __lowerCamelCase       :    Optional[str]     	=			field(
      default=UpperCAmelCase       ,   metadata={"help": "Name of TPU"}       ,   )

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

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

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

  __lowerCamelCase       :    bool     	=			field(default=UpperCAmelCase       ,   metadata={"help": "Whether to activate the XLA compilation or not"}       )
  @cached_property
  def 					UpperCamelCase__   (      self:		Tuple					):
     requires_backends(self				,			["tf"]					)
     logger.info("Tensorflow: setting up strategy"					)

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

           UpperCamelCase_     =tf.distribute.TPUStrategy(UpperCamelCase_					)

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

     return strategy
  @property
  def 					UpperCamelCase__   (      self:		Optional[Any]					):
     requires_backends(self				,			["tf"]					)
     return self._setup_strategy
  @property
  def 					UpperCamelCase__   (      self:		Dict					):
     requires_backends(self				,			["tf"]					)
     return self._setup_strategy.num_replicas_in_sync
  @property
  def 					UpperCamelCase__   (      self:		str					):
     return False  # TF Logging is handled by Keras not the Trainer
  @property
  def 					UpperCamelCase__   (      self:		Dict					):
     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."					)
     UpperCamelCase_     =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:		Union[str, Any]					):
     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."					)
     UpperCamelCase_     =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:		List[Any]					):
     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








"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 						__lowerCAmelCase   (			UpperCAmelCase       ):







  '''simple docstring'''


  __lowerCamelCase       :    Optional[Any]     	=			["image_processor", "tokenizer"]
  __lowerCamelCase       :    List[str]     	=			"VideoMAEImageProcessor"
  __lowerCamelCase       :    Dict     	=			("CLIPTokenizer", "CLIPTokenizerFast")
  def __init__(      self:		str				,			UpperCamelCase_:		Dict=None				,			UpperCamelCase_:		str=None				,			**UpperCamelCase_:		str					):
     UpperCamelCase_     =None
     if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."				,			UpperCamelCase_				,			)
        UpperCamelCase_     =kwargs.pop("feature_extractor"					)

     UpperCamelCase_     =image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
        raise ValueError("You need to specify an `image_processor`."					)
     if tokenizer is None:
        raise ValueError("You need to specify a `tokenizer`."					)

     super().__init__(UpperCamelCase_				,			UpperCamelCase_					)
     UpperCamelCase_     =self.image_processor
  def __call__(      self:		Tuple				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		Optional[Any]=None				,			**UpperCamelCase_:		int					):

     if text is None and videos is None:
        raise ValueError("You have to specify either text or videos. Both cannot be none."					)

     if text is not None:
        UpperCamelCase_     =self.tokenizer(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)

     if videos is not None:
        UpperCamelCase_     =self.image_processor(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)

     if text is not None and videos is not None:
        UpperCamelCase_     =image_features.pixel_values
        return encoding
     elif text is not None:
        return encoding
     else:
        return BatchEncoding(data=dict(**UpperCamelCase_					)				,			tensor_type=UpperCamelCase_					)
  def 					UpperCamelCase__   (      self:		Any				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Optional[int]					):
     return self.tokenizer.batch_decode(*UpperCamelCase_				,			**UpperCamelCase_					)
  def 					UpperCamelCase__   (      self:		Optional[Any]				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Tuple					):
     return self.tokenizer.decode(*UpperCamelCase_				,			**UpperCamelCase_					)
  @property
  def 					UpperCamelCase__   (      self:		List[Any]					):
     return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
  @property
  def 					UpperCamelCase__   (      self:		str					):
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			UpperCamelCase_				,			)
     return self.image_processor_class





  @property
  def 					UpperCamelCase__   (      self:		Optional[Any]					):
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			UpperCamelCase_				,			)
     return self.image_processor
