


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A		:				List[Any]									=       {
    '''configuration_blenderbot_small''': [
        '''BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotSmallConfig''',
        '''BlenderbotSmallOnnxConfig''',
    ],
    '''tokenization_blenderbot_small''': ['''BlenderbotSmallTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[str]									=       ['''BlenderbotSmallTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				str									=       [
       '''BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BlenderbotSmallForCausalLM''',
       '''BlenderbotSmallForConditionalGeneration''',
       '''BlenderbotSmallModel''',
       '''BlenderbotSmallPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				Dict									=       [
       '''TFBlenderbotSmallForConditionalGeneration''',
       '''TFBlenderbotSmallModel''',
       '''TFBlenderbotSmallPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				Tuple									=       [
       '''FlaxBlenderbotSmallForConditionalGeneration''',
       '''FlaxBlenderbotSmallModel''',
       '''FlaxBlenderbotSmallPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_blenderbot_small import (
       BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BlenderbotSmallConfig,
       BlenderbotSmallOnnxConfig,
   )
   from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blenderbot_small import (
          BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlenderbotSmallForCausalLM,
          BlenderbotSmallForConditionalGeneration,
          BlenderbotSmallModel,
          BlenderbotSmallPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_blenderbot_small import (
          TFBlenderbotSmallForConditionalGeneration,
          TFBlenderbotSmallModel,
          TFBlenderbotSmallPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_blenderbot_small import (
          FlaxBlenderbotSmallForConditionalGeneration,
          FlaxBlenderbotSmallModel,
          FlaxBlenderbotSmallPreTrainedModel,
      )

else:
   import sys

   _A		:				Optional[Any]									=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






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() = }""")


