


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        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  int						      =	"vision-encoder-decoder"
     UpperCAmelCase__ :  List[Any]						      =	True





     def __init__(    self					:				List[Any]    ,	**SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        super().__init__(**SCREAMING_SNAKE_CASE_							)
        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}'							)

        __lowerCamelCase:				List[Any]     =	kwargs.pop("""encoder"""							)
        __lowerCamelCase:				Union[str, Any]     =	encoder_config.pop("""model_type"""							)
        __lowerCamelCase:				Optional[Any]     =	kwargs.pop("""decoder"""							)
        __lowerCamelCase:				int     =	decoder_config.pop("""model_type"""							)

        __lowerCamelCase:				Tuple     =	AutoConfig.for_model(SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	AutoConfig.for_model(SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	True





     @classmethod
     def  SCREAMING_SNAKE_CASE__       (    cls					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				PretrainedConfig    ,	SCREAMING_SNAKE_CASE_					:				PretrainedConfig    ,	**SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""							)
        __lowerCamelCase:				Optional[Any]     =	True
        __lowerCamelCase:				Any     =	True

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







     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				str     =	copy.deepcopy(self.__dict__							)
        __lowerCamelCase:				Dict     =	self.encoder.to_dict()
        __lowerCamelCase:				Tuple     =	self.decoder.to_dict()
        __lowerCamelCase:				Optional[int]     =	self.__class__.model_type
        return output



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Optional[Any]						      =	version.parse("1.11"   )





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ]							)





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        return 1E-4







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



class        a					(			_UpperCAmelCase   ):





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Any     =	OrderedDict()
        __lowerCamelCase:				Optional[int]     =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
        __lowerCamelCase:				List[str]     =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
        __lowerCamelCase:				int     =	{0: """batch""", 1: """encoder_sequence"""}

        return common_inputs







     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				"PreTrainedTokenizerBase"    ,	SCREAMING_SNAKE_CASE_					:				int = -1    ,	SCREAMING_SNAKE_CASE_					:				int = -1    ,	SCREAMING_SNAKE_CASE_					:				bool = False    ,	SCREAMING_SNAKE_CASE_					:				Optional["TensorType"] = None    ,	):
        import torch

        __lowerCamelCase:				str     =	OrderedDict()

        __lowerCamelCase:				List[Any]     =	super().generate_dummy_inputs(
            SCREAMING_SNAKE_CASE_    ,	batch_size=SCREAMING_SNAKE_CASE_    ,	seq_length=SCREAMING_SNAKE_CASE_    ,	is_pair=SCREAMING_SNAKE_CASE_    ,	framework=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Tuple     =	dummy_input["""input_ids"""].shape
        __lowerCamelCase:				Optional[int]     =	(batch, encoder_sequence, self._config.encoder_hidden_size)
        __lowerCamelCase:				Union[str, Any]     =	dummy_input.pop("""input_ids"""							)
        __lowerCamelCase:				Tuple     =	dummy_input.pop("""attention_mask"""							)
        __lowerCamelCase:				List[Any]     =	torch.zeros(SCREAMING_SNAKE_CASE_							)

        return common_inputs



class        a					(			_UpperCAmelCase   ):





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        pass





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				PretrainedConfig							):
        return VisionEncoderDecoderEncoderOnnxConfig(SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				PretrainedConfig    ,	SCREAMING_SNAKE_CASE_					:				PretrainedConfig    ,	SCREAMING_SNAKE_CASE_					:				str = "default"							):
        __lowerCamelCase:				Dict     =	encoder_config.hidden_size
        return VisionEncoderDecoderDecoderOnnxConfig(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)






import math



def     __lowerCAmelCase						(							snake_case	:    int )       ->							bool:
   __lowerCamelCase:				Dict     =	math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )

   return exponent == int(snake_case )



def     __lowerCAmelCase						(							snake_case	:    float = 1 / 12345 )       ->							int:
   __lowerCamelCase:				str     =	0
   __lowerCamelCase:				Optional[Any]     =	0

   __lowerCamelCase:				str     =	3
   while True:
      __lowerCamelCase:				int     =	(integer**2 - 1) / 4
      # if candidate is an integer, then there is a partition for k
      if partition_candidate == int(snake_case ):
         __lowerCamelCase:				Union[str, Any]     =	int(snake_case )
         total_partitions += 1
         if check_partition_perfect(snake_case ):
            perfect_partitions += 1
      if perfect_partitions > 0:
         if perfect_partitions / total_partitions < max_proportion:
            return int(snake_case )
      integer += 1


if __name__ == "__main__":
   print(F"""{solution() = }""")


