



"""simple docstring"""







import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


UpperCAmelCase:  Tuple			     =				2 << 20  # 2mb
UpperCAmelCase:  Tuple			     =				get_logger(__name__)





class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   self._options.get_all_options().get("""runner"""   )
			return runner in [None, "DirectRunner", "PortableRunner"]







def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase=False      ):
		_lowercase				: Dict     =   FileSystems
		if fs.exists(__UpperCAmelCase      ):
				if force_upload:
						logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""      )
				else:
						logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""      )
						return
		with fs.create(__UpperCAmelCase      ) as remote_file:
				with open(__UpperCAmelCase   ,				"""rb"""      ) as local_file:
						_lowercase				: Optional[Any]     =   local_file.read(__UpperCAmelCase      )
						while chunk:
								remote_file.write(__UpperCAmelCase      )
								_lowercase				: Tuple     =   local_file.read(__UpperCAmelCase      )


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase=False      ):
		_lowercase				: Optional[int]     =   FileSystems
		if os.path.exists(__UpperCAmelCase      ):
				if force_download:
						logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""      )
				else:
						logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""      )
						return
		with fs.open(__UpperCAmelCase      ) as remote_file:
				with open(__UpperCAmelCase   ,				"""wb"""      ) as local_file:
						_lowercase				: List[str]     =   remote_file.read(__UpperCAmelCase      )
						while chunk:
								local_file.write(__UpperCAmelCase      )
								_lowercase				: List[Any]     =   remote_file.read(__UpperCAmelCase      )




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Optional[Any]          =	["image_processor", "tokenizer"]
       A							:	Dict          =	"VideoMAEImageProcessor"
       A							:	Tuple          =	("CLIPTokenizer", "CLIPTokenizerFast")

       def __init__(							self					:     str			,			_lowerCAmelCase					:     Tuple=None			,			_lowerCAmelCase					:     Any=None			,			**_lowerCAmelCase					:     Union[str, Any]			):
           __snake_case       :				int 			=    None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                   """ instead."""			,			_lowerCAmelCase			,			)
               __snake_case       :				Optional[Any] 			=    kwargs.pop("""feature_extractor"""			)

           __snake_case       :				int 			=    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__(_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    self.image_processor

       def __call__(							self					:     str			,			_lowerCAmelCase					:     int=None			,			_lowerCAmelCase					:     Dict=None			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     List[str]			):

           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:
               __snake_case       :				Optional[Any] 			=    self.tokenizer(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if videos is not None:
               __snake_case       :				Optional[int] 			=    self.image_processor(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if text is not None and videos is not None:
               __snake_case       :				Union[str, Any] 			=    image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)			,			tensor_type=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     str			,			**_lowerCAmelCase					:     List[Any]			):
           return self.tokenizer.batch_decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     List[Any]			,			**_lowerCAmelCase					:     Dict			):
           return self.tokenizer.decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       @property
       def 			snake_case__    (							self					:     str			):
           return ["input_ids", "attention_mask", "position_ids", "pixel_values"]

       @property
       def 			snake_case__    (							self					:     str			):
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor_class





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



