from typing import TYPE_CHECKING

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


lowercase_  : str								=					{
    '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:
      lowercase_  : Any								=					['BlenderbotSmallTokenizerFast']

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

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

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_  : List[str]								=					[
          '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

      lowercase_  : List[str]								=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




def   __UpperCAmelCase					(      __a :     str      )     ->	list:

					"""simple docstring"""

					if n_term == "":
										return []
					_a      :       list   =						[]
					for temp in range(int(__a      )      ):
										series.append(F"""1/{temp + 1}""" if series else '''1'''      )
					return series


if __name__ == "__main__":
			a__												=						input('''Enter the last number (nth term) of the Harmonic Series''')
			print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
			print(harmonic_series(nth_term))



