




"""simple docstring"""


from typing import TYPE_CHECKING

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


A_      			=       {
    '''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_      			=       ['''BlenderbotSmallTokenizerFast''']

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

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

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      			=       [
         '''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_      			=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








"""simple docstring"""


from typing import Any


def       UpperCAmelCase__ (snake_case__							:      list					):
  """simple docstring"""

  if not input_list:
    return []
  _snake_case							:	List[Any]			=						[input_list.count(snake_case__					) for value in input_list]
  _snake_case							:	Optional[int]			=						max(snake_case__					)  # Gets the maximum count in the input list.
  # Gets values of modes
  return sorted({input_list[i] for i, value in enumerate(snake_case__					) if value == y}					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()


