


"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_											=     logging.get_logger(__name__)

a_											=     {
    "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json",
}
class snake_case   (	lowercase_):
		__UpperCamelCase							     =      'mpnet'







		def __init__(       self   :							str     , a__   :							int=3_05_27     , a__   :							Tuple=7_68     , a__   :							Any=12     , a__   :							List[str]=12     , a__   :							Optional[int]=30_72     , a__   :							Any="gelu"     , a__   :							Union[str, Any]=0.1     , a__   :							Any=0.1     , a__   :							List[Any]=5_12     , a__   :							int=0.0_2     , a__   :							List[Any]=1E-1_2     , a__   :							Dict=32     , a__   :							Any=1     , a__   :							Union[str, Any]=0     , a__   :							List[Any]=2     , **a__   :							Optional[int]     , )							->    List[str]:





						'''simple docstring'''







						super().__init__(pad_token_id=a__     , bos_token_id=a__     , eos_token_id=a__     , **a__		)

						_A       	=      vocab_size
						_A       	=      hidden_size
						_A       	=      num_hidden_layers
						_A       	=      num_attention_heads
						_A       	=      hidden_act
						_A       	=      intermediate_size
						_A       	=      hidden_dropout_prob
						_A       	=      attention_probs_dropout_prob
						_A       	=      max_position_embeddings
						_A       	=      initializer_range
						_A       	=      layer_norm_eps
						_A       	=      relative_attention_num_buckets

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = ["image_processor", "tokenizer"]
  _UpperCamelCase						 = "AutoImageProcessor"
  _UpperCamelCase						 = "AutoTokenizer"


  def __init__(					self	,		a__=None	,		a__=None	,		**a__		):
    _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.'''	,		a__	,		)
      _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__(a__	,		a__		)
    _lowerCamelCase      =						self.image_processor
    _lowerCamelCase      =						False


  def __call__(					self	,		*a__	,		**a__		):
    # For backward compatibility
    if self._in_target_context_manager:
      return self.current_processor(*a__	,		**a__		)

    _lowerCamelCase      =						kwargs.pop('''images'''	,		a__		)
    _lowerCamelCase      =						kwargs.pop('''text'''	,		a__		)
    if len(a__		) > 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(a__	,		*a__	,		**a__		)
    if text is not None:
      _lowerCamelCase      =						self.tokenizer(a__	,		**a__		)

    if text is None:
      return inputs
    elif images is None:
      return encodings
    else:
      _lowerCamelCase      =						encodings['''input_ids''']
      return inputs


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    return self.tokenizer.batch_decode(*a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    return self.tokenizer.decode(*a__	,		**a__		)


  @contextmanager
  def     _UpperCAmelCase  (					self		):
    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     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''	,		a__	,		)
    return self.image_processor_class


  @property
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,		a__	,		)
    return self.image_processor




