





'''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'''


from __future__ import annotations




def       A							(      _UpperCAmelCase   :				list[int]						,_UpperCAmelCase   :				int					)  ->				int:


  '''simple docstring'''




  __lowerCAmelCase     ,					__lowerCAmelCase		:    Dict			     =  0, len(_UpperCAmelCase					) - 1
  while start_ind <= end_ind:
    if array[start_ind] == search_item:
      return start_ind
    elif array[end_ind] == search_item:
      return end_ind
    else:
      start_ind += 1
      end_ind -= 1
    # returns -1 if search_item is not found in array
  return -1


if __name__ == "__main__":
    print(double_linear_search(list(range(1_00)), 40))





