'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


A_	   = {
    "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:
						A_	   = ["DocumentQuestionAnsweringTool"]
						A_	   = ["ImageCaptioningTool"]
						A_	   = ["ImageQuestionAnsweringTool"]
						A_	   = ["ImageSegmentationTool"]
						A_	   = ["SpeechToTextTool"]
						A_	   = ["TextClassificationTool"]
						A_	   = ["TextQuestionAnsweringTool"]
						A_	   = ["TextSummarizationTool"]
						A_	   = ["TextToSpeechTool"]
						A_	   = ["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

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






'''simple docstring'''

def        UpperCAmelCase__   (				_lowerCAmelCase    :			int = 400_0000)    ->		int:



						"""simple docstring"""

						snake_case :       List[Any]				=    1
						snake_case :       int				=    2
						snake_case :       List[str]				=    0
						while j <= n:
												if j % 2 == 0:
																		total += j
												snake_case				,					snake_case :       Any				=    j, i + j

						return total


if __name__ == "__main__":
						print(f"{solution() = }")





