# 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,
)


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

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


def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[int]:

  '''simple docstring'''




  sequence.append(lowerCamelCase__					)

  a__     		=     0
  while sequence[index] != target:
    index += 1

  sequence.pop()

  if index == len(lowerCamelCase__					):
    return None

  return index


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE   						=    input("""Enter numbers separated by comma:\n""").strip()
     _SCREAMING_SNAKE_CASE   						=    [int(item) for item in user_input.split(""",""")]

     _SCREAMING_SNAKE_CASE   						=    input("""Enter a single number to be found in the list:\n""")
     _SCREAMING_SNAKE_CASE   						=    int(target_input)
     _SCREAMING_SNAKE_CASE   						=    sentinel_linear_search(sequence, target)
     if result is not None:
          print(F"""{target} found at positions: {result}""")
     else:
          print("""Not found""")

