



'''simple docstring'''







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class      lowerCamelCase__     (     _A				):






	'''simple docstring'''

	A__		=	['''image_processor''', '''tokenizer''']
	A__		=	'''AutoImageProcessor'''
	A__		=	'''AutoTokenizer'''




	def __init__(  self		:  Any   ,		__A		:  List[Any]=None   ,		__A		:  int=None   ,		**__A		:  str					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   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   ,		)
															lowerCAmelCase__  =   kwargs.pop("""feature_extractor"""					)

								lowerCAmelCase__  =   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					)
								lowerCAmelCase__  =   self.image_processor
								lowerCAmelCase__  =   False




	def __call__(  self		:  Optional[int]   ,		*__A		:  Optional[Any]   ,		**__A		:  Optional[int]					)     ->			Optional[Any]:




								'''simple docstring'''





								if self._in_target_context_manager:
															return self.current_processor(*_lowerCAmelCase   ,		**_lowerCAmelCase					)

								lowerCAmelCase__  =   kwargs.pop("""images"""   ,		_lowerCAmelCase					)
								lowerCAmelCase__  =   kwargs.pop("""text"""   ,		_lowerCAmelCase					)
								if len(_lowerCAmelCase					) > 0:
															lowerCAmelCase__  =   args[0]
															lowerCAmelCase__  =   args[1:]

								if images is None and text is None:
															raise ValueError("""You need to specify either an `images` or `text` input to process."""					)

								if images is not None:
															lowerCAmelCase__  =   self.image_processor(_lowerCAmelCase   ,		*_lowerCAmelCase   ,		**_lowerCAmelCase					)
								if text is not None:
															lowerCAmelCase__  =   self.tokenizer(_lowerCAmelCase   ,		**_lowerCAmelCase					)

								if text is None:
															return inputs
								elif images is None:
															return encodings
								else:
															lowerCAmelCase__  =   encodings["input_ids"]
															return inputs




	def      lowercase__							(  self		:  List[str]   ,		*__A		:  Union[str, Any]   ,		**__A		:  str					)     ->			List[str]:




								'''simple docstring'''





								return self.tokenizer.batch_decode(*_lowerCAmelCase   ,		**_lowerCAmelCase					)




	def      lowercase__							(  self		:  Optional[Any]   ,		*__A		:  Dict   ,		**__A		:  Any					)     ->			Optional[int]:




								'''simple docstring'''





								return self.tokenizer.decode(*_lowerCAmelCase   ,		**_lowerCAmelCase					)




	@contextmanager
	def      lowercase__							(  self		:  Optional[Any]					)     ->			Any:




								'''simple docstring'''





								warnings.warn(
								    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
								    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
								    """your images inputs, or in a separate call."""					)
								lowerCAmelCase__  =   True
								lowerCAmelCase__  =   self.tokenizer
								yield
								lowerCAmelCase__  =   self.image_processor
								lowerCAmelCase__  =   False




	@property
	def      lowercase__							(  self		:  List[str]					)     ->			Dict:




								'''simple docstring'''





								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      lowercase__							(  self		:  int					)     ->			List[Any]:




								'''simple docstring'''





								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,		_lowerCAmelCase   ,		)
								return self.image_processor











import os

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

from .logging import get_logger


_UpperCAmelCase						:    List[str]      =  2 << 20  # 2mb
_UpperCAmelCase						:    Optional[int]      =  get_logger(__name__)




class  __lowerCAmelCase							(				lowerCAmelCase):






       def        SCREAMING_SNAKE_CASE     ( self:       str					):
         lowercase     :str   						=    self._options.get_all_options().get("runner"					)
         return runner in [None, "DirectRunner", "PortableRunner"]







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=False ):
  lowercase     :str   						=    FileSystems
  if fs.exists(lowerCamelCase ):
    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(lowerCamelCase ) as remote_file:
    with open(lowerCamelCase,      "rb" ) as local_file:
      lowercase     :int   						=    local_file.read(lowerCamelCase )
      while chunk:
        remote_file.write(lowerCamelCase )
        lowercase     :List[Any]   						=    local_file.read(lowerCamelCase )







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=False ):
  lowercase     :Union[str, Any]   						=    FileSystems
  if os.path.exists(lowerCamelCase ):
    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(lowerCamelCase ) as remote_file:
    with open(lowerCamelCase,      "wb" ) as local_file:
      lowercase     :Optional[Any]   						=    remote_file.read(lowerCamelCase )
      while chunk:
        local_file.write(lowerCamelCase )
        lowercase     :Dict   						=    remote_file.read(lowerCamelCase )



