
from typing import TYPE_CHECKING

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


_lowerCamelCase        ={"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =[
          """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """RegNetForImageClassification""",
          """RegNetModel""",
          """RegNetPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =[
          """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFRegNetForImageClassification""",
          """TFRegNetModel""",
          """TFRegNetPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =[
          """FlaxRegNetForImageClassification""",
          """FlaxRegNetModel""",
          """FlaxRegNetPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_regnet import (
                REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                RegNetForImageClassification,
                RegNetModel,
                RegNetPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_regnet import (
                TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRegNetForImageClassification,
                TFRegNetModel,
                TFRegNetPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_regnet import (
                FlaxRegNetForImageClassification,
                FlaxRegNetModel,
                FlaxRegNetPreTrainedModel,
            )


else:
      import sys

      _lowerCamelCase        =_LazyModule(__name__, globals()["""__file__"""], _import_structure)






def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Optional[int]          =				[1 / first_term]
 lowerCamelCase		:     Any          =				1 / first_term
 for _ in range(number_of_terms - 1							):
  first_term += common_difference
  arithmetic_progression.append(lowerCamelCase							)
 lowerCamelCase		:     Optional[Any]          =				[1 / step for step in arithmetic_progression]
 return sum(lowerCamelCase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(sum_of_harmonic_progression(1 / 2, 2, 2))




