'''simple docstring'''






import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
 from ... import PreTrainedTokenizerBase, TensorType

a_					:    List[str]          =    logging.get_logger(__name__)




class  a     (     __SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""vision-encoder-decoder"""
 _lowerCAmelCase 					=	True
 def __init__( self      ,				**__magic_name__						)					->							Dict:
      super().__init__(**_a						)
      if "encoder" not in kwargs or "decoder" not in kwargs:
           raise ValueError(
               f'A configuraton of type {self.model_type} cannot be instantiated because '
               f'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'						)

      _a			      =							kwargs.pop('encoder'						)
      _a			      =							encoder_config.pop('model_type'						)
      _a			      =							kwargs.pop('decoder'						)
      _a			      =							decoder_config.pop('model_type'						)

      _a			      =							AutoConfig.for_model(_a      ,				**_a						)
      _a			      =							AutoConfig.for_model(_a      ,				**_a						)
      _a			      =							True
 @classmethod
 def 							__UpperCAmelCase		( cls      ,				__magic_name__      ,				__magic_name__      ,				**__magic_name__						)					->							str:
      logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'						)
      _a			      =							True
      _a			      =							True

      return cls(encoder=encoder_config.to_dict()      ,				decoder=decoder_config.to_dict()      ,				**_a						)




 def 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							copy.deepcopy(self.__dict__						)
      _a			      =							self.encoder.to_dict()
      _a			      =							self.decoder.to_dict()
      _a			      =							self.__class__.model_type
      return output





class  a     (     __SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	version.parse("""1.11"""       )
 @property
 def 							__UpperCAmelCase		( self						)					->							int:
      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ]						)
 @property
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      return 1e-4




 @property
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}						)





class  a     (     __SCREAMING_SNAKE_CASE       ):
 @property
 def 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							OrderedDict()
      _a			      =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
      _a			      =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
      _a			      =							{0: """batch""", 1: """encoder_sequence"""}

      return common_inputs




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = -1      ,				__magic_name__ = -1      ,				__magic_name__ = False      ,				__magic_name__ = None      ,				)					->							Optional[Any]:
      import torch

      _a			      =							OrderedDict()

      _a			      =							super().generate_dummy_inputs(
          _a      ,				batch_size=_a      ,				seq_length=_a      ,				is_pair=_a      ,				framework=_a						)

      _a			      =							dummy_input["""input_ids"""].shape
      _a			      =							(batch, encoder_sequence, self._config.encoder_hidden_size)
      _a			      =							dummy_input.pop('input_ids'						)
      _a			      =							dummy_input.pop('attention_mask'						)
      _a			      =							torch.zeros(_a						)

      return common_inputs





class  a     (     __SCREAMING_SNAKE_CASE       ):
 @property
 def 							__UpperCAmelCase		( self						)					->							Any:
      pass
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							int:
      return VisionEncoderDecoderEncoderOnnxConfig(_a						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = "default"						)					->							Optional[int]:
      _a			      =							encoder_config.hidden_size
      return VisionEncoderDecoderDecoderOnnxConfig(_a      ,				_a						)





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


a_					:    Optional[Any]          =    logging.get_logger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							None:
      warnings.warn(
          'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DonutImageProcessor instead.'      ,				__magic_name__      ,				)
      super().__init__(*__magic_name__      ,				**__magic_name__						)

