





'''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[Any] =logging.get_logger(__name__)

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					="vision-encoder-decoder"
 SCREAMING_SNAKE_CASE_	:					Tuple					=True






 def __init__(       self       :							List[str]							,	**__A       :							List[str]	):
        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}'''	)

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

        __UpperCamelCase       =  AutoConfig.for_model(__A							,	**__A	)
        __UpperCamelCase       =  AutoConfig.for_model(__A							,	**__A	)
        __UpperCamelCase       =  True






 @classmethod
 def        _lowerCamelCase					(       cls       :							Any							,	__A       :							PretrainedConfig							,	__A       :							PretrainedConfig							,	**__A       :							Union[str, Any]	):
        logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'	)
        __UpperCamelCase       =  True
        __UpperCamelCase       =  True

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






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  copy.deepcopy(self.__dict__	)
        __UpperCamelCase       =  self.encoder.to_dict()
        __UpperCamelCase       =  self.decoder.to_dict()
        __UpperCamelCase       =  self.__class__.model_type
        return output






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Tuple					=version.parse("1.11"       )






 @property
 def        _lowerCamelCase					(       self       :							str	):
        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]	)






 @property
 def        _lowerCamelCase					(       self       :							Dict	):
        return 1e-4






 @property
 def        _lowerCamelCase					(       self       :							List[str]	):
        return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}	)






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 @property
 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  OrderedDict()
        __UpperCamelCase       =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        __UpperCamelCase       =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        __UpperCamelCase       =  {0: 'batch', 1: 'encoder_sequence'}

        return common_inputs






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							"PreTrainedTokenizerBase"							,	__A       :							int = -1							,	__A       :							int = -1							,	__A       :							bool = False							,	__A       :							Optional["TensorType"] = None							,	):
        import torch

        __UpperCamelCase       =  OrderedDict()

        __UpperCamelCase       =  super().generate_dummy_inputs(
            __A							,	batch_size=__A							,	seq_length=__A							,	is_pair=__A							,	framework=__A	)

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

        return common_inputs






class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 @property
 def        _lowerCamelCase					(       self       :							str	):
        pass






 def        _lowerCamelCase					(       self       :							Optional[int]							,	__A       :							PretrainedConfig	):
        return VisionEncoderDecoderEncoderOnnxConfig(__A	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							PretrainedConfig							,	__A       :							PretrainedConfig							,	__A       :							str = "default"	):
        __UpperCamelCase       =  encoder_config.hidden_size
        return VisionEncoderDecoderDecoderOnnxConfig(__A							,	__A	)




'''simple docstring'''

def _SCREAMING_SNAKE_CASE      (A       )       ->       int:






       """simple docstring"""







       if not isinstance(A  , A       ):
              raise TypeError('''only integers accepted as input'''       )
       else:
              lowercase__      			= str(abs(A       )       )
              lowercase__      			= [list(A       ) for char in range(len(A       )       )]
              for index in range(len(A       )       ):
                     num_transpositions[index].pop(A       )
              return max(
                  int(''''''.join(list(A       )       )       ) for transposition in num_transpositions       )


if __name__ == "__main__":
 __import__('doctest').testmod()




