




from __future__ import annotations
def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	):





							"""simple docstring"""





							if not all((nth_term, start_term_a, common_ratio_r) ):
														return []
							SCREAMING_SNAKE_CASE			: list[float | int]	     =[]
							SCREAMING_SNAKE_CASE			: Any	     =1
							SCREAMING_SNAKE_CASE			: Tuple	     =common_ratio_r
							for _ in range(int(__a ) ):
														if not series:
																					series.append(__a )
														else:
																					power += 1
																					series.append(float(start_term_a * multiple ) )
																					SCREAMING_SNAKE_CASE			: List[str]	     =pow(float(__a )	,	__a )
							return series


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							_A							    =	float(input("""Enter the last number (n term) of the Geometric Series"""))
							_A							    =	float(input("""Enter the starting term (a) of the Geometric Series"""))
							_A							    =	float(
							    input("""Enter the common ratio between two terms (r) of the Geometric Series""")
							)
							print("""Formula of Geometric Series => a + ar + ar^2 ... +ar^n""")
							print(geometric_series(nth_term, start_term_a, common_ratio_r))












from typing import TYPE_CHECKING

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


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

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

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

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """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

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






