




"""simple docstring"""





from typing import TYPE_CHECKING

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


_lowercase	      =    {
    """agents""": ["""Agent""", """AzureOpenAiAgent""", """HfAgent""", """LocalAgent""", """OpenAiAgent"""],
    """base""": ["""PipelineTool""", """RemoteTool""", """Tool""", """launch_gradio_demo""", """load_tool"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase	      =    ["""DocumentQuestionAnsweringTool"""]
							_lowercase	      =    ["""ImageCaptioningTool"""]
							_lowercase	      =    ["""ImageQuestionAnsweringTool"""]
							_lowercase	      =    ["""ImageSegmentationTool"""]
							_lowercase	      =    ["""SpeechToTextTool"""]
							_lowercase	      =    ["""TextClassificationTool"""]
							_lowercase	      =    ["""TextQuestionAnsweringTool"""]
							_lowercase	      =    ["""TextSummarizationTool"""]
							_lowercase	      =    ["""TextToSpeechTool"""]
							_lowercase	      =    ["""TranslationTool"""]

if TYPE_CHECKING:
							from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
							from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .document_question_answering import DocumentQuestionAnsweringTool
														from .image_captioning import ImageCaptioningTool
														from .image_question_answering import ImageQuestionAnsweringTool
														from .image_segmentation import ImageSegmentationTool
														from .speech_to_text import SpeechToTextTool
														from .text_classification import TextClassificationTool
														from .text_question_answering import TextQuestionAnsweringTool
														from .text_summarization import TextSummarizationTool
														from .text_to_speech import TextToSpeechTool
														from .translation import TranslationTool
else:
							import sys

							_lowercase	      =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








"""simple docstring"""





from __future__ import annotations

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			):
						'''simple docstring'''


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


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							_lowercase	      =    float(input("""Enter the last number (n term) of the Geometric Series"""))
							_lowercase	      =    float(input("""Enter the starting term (a) of the Geometric Series"""))
							_lowercase	      =    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))


