





'''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   	=    logging.get_logger(__name__)







class __a			( _snake_case      ):
   __UpperCamelCase :			Optional[int]						   =						'vision-encoder-decoder'
   __UpperCamelCase :			Optional[int]						   =						True





   def __init__(       self					:      List[Any]					,**lowerCamelCase					:      List[Any]     ):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )
      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}"""     )

      __SCREAMING_SNAKE_CASE   				=							kwargs.pop("""encoder"""     )
      __SCREAMING_SNAKE_CASE   				=							encoder_config.pop("""model_type"""     )
      __SCREAMING_SNAKE_CASE   				=							kwargs.pop("""decoder"""     )
      __SCREAMING_SNAKE_CASE   				=							decoder_config.pop("""model_type"""     )

      __SCREAMING_SNAKE_CASE   				=							AutoConfig.for_model(lowerCamelCase					,**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							AutoConfig.for_model(lowerCamelCase					,**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							True





   @classmethod
   def   UpperCAmelCase__  (       cls					:      Tuple					,lowerCamelCase					:      PretrainedConfig					,lowerCamelCase					:      PretrainedConfig					,**lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""     )
      __SCREAMING_SNAKE_CASE   				=							True
      __SCREAMING_SNAKE_CASE   				=							True

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





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							copy.deepcopy(self.__dict__     )
      __SCREAMING_SNAKE_CASE   				=							self.encoder.to_dict()
      __SCREAMING_SNAKE_CASE   				=							self.decoder.to_dict()
      __SCREAMING_SNAKE_CASE   				=							self.__class__.model_type
      return output







class __a			( _snake_case      ):
   __UpperCamelCase :			Dict						   =						version.parse('1.11'      )





   @property
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
          ]     )





   @property
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return 1E-4





   @property
   def   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}}     )







class __a			( _snake_case      ):





   @property
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							OrderedDict()
      __SCREAMING_SNAKE_CASE   				=							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
      __SCREAMING_SNAKE_CASE   				=							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
      __SCREAMING_SNAKE_CASE   				=							{0: """batch""", 1: """encoder_sequence"""}

      return common_inputs





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      "PreTrainedTokenizerBase"					,lowerCamelCase					:      int = -1					,lowerCamelCase					:      int = -1					,lowerCamelCase					:      bool = False					,lowerCamelCase					:      Optional["TensorType"] = None					,):

      '''simple docstring'''

      import torch

      __SCREAMING_SNAKE_CASE   				=							OrderedDict()

      __SCREAMING_SNAKE_CASE   				=							super().generate_dummy_inputs(
          lowerCamelCase					,batch_size=lowerCamelCase					,seq_length=lowerCamelCase					,is_pair=lowerCamelCase					,framework=lowerCamelCase     )

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

      return common_inputs







class __a			( _snake_case      ):





   @property
   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      pass





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      PretrainedConfig     ):

      '''simple docstring'''

      return VisionEncoderDecoderEncoderOnnxConfig(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Any					,lowerCamelCase					:      PretrainedConfig					,lowerCamelCase					:      PretrainedConfig					,lowerCamelCase					:      str = "default"     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							encoder_config.hidden_size
      return VisionEncoderDecoderDecoderOnnxConfig(lowerCamelCase					,lowerCamelCase     )












'''simple docstring'''

import os
from math import logaa

def     __magic_name__							(				__UpperCAmelCase = "base_exp.txt"   )    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							0
   __SCREAMING_SNAKE_CASE   				=							0
   for i, line in enumerate(open(os.path.join(os.path.dirname(__UpperCAmelCase   )				,						__UpperCAmelCase   )   )   ):
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							list(map(__UpperCAmelCase				,						line.split(""","""   )   )   )
      if x * logaa(__UpperCAmelCase   ) > largest:
         __SCREAMING_SNAKE_CASE   				=							x * logaa(__UpperCAmelCase   )
         __SCREAMING_SNAKE_CASE   				=							i + 1
   return result


if __name__ == "__main__":
    print(solution())





