



'''simple docstring'''




from __future__ import annotations

from typing import Generic, TypeVar

a  :     Dict       =       TypeVar("""T""")

class      UpperCamelCase_					(				Generic[T]     ):





	def __init__(    self						,						A       )     ->     List[Any]:
								UpperCAmelCase	:						Dict					=						data  # Assign data
								UpperCAmelCase	:						Node[T] | None					=						None  # Initialize next as null
								UpperCAmelCase	:						Node[T] | None					=						None  # Initialize prev as null




class      UpperCamelCase_					(				Generic[T]     ):





	def __init__(    self       )     ->     None:
								UpperCAmelCase	:						Node[T] | None					=						None





	def 					_lowercase(    self						,						A       )     ->     None:
								if self.head is None:
															UpperCAmelCase	:						Optional[Any]					=						Node(A       )
								else:
															UpperCAmelCase	:						List[str]					=						Node(A       )
															UpperCAmelCase	:						Dict					=						new_node
															UpperCAmelCase	:						Union[str, Any]					=						self.head
															UpperCAmelCase	:						List[Any]					=						None
															UpperCAmelCase	:						Union[str, Any]					=						new_node





	def 					_lowercase(    self       )     ->     T | None:
								if self.head is None:
															return None
								else:
															assert self.head is not None
															UpperCAmelCase	:						str					=						self.head.data
															UpperCAmelCase	:						Optional[Any]					=						self.head.next
															if self.head is not None:
																						UpperCAmelCase	:						List[Any]					=						None
															return temp





	def 					_lowercase(    self       )     ->     T | None:
								return self.head.data if self.head is not None else None





	def __len__(    self       )     ->     int:
								UpperCAmelCase	:						Tuple					=						self.head
								UpperCAmelCase	:						int					=						0
								while temp is not None:
															count += 1
															UpperCAmelCase	:						List[Any]					=						temp.next
								return count





	def 					_lowercase(    self       )     ->     bool:
								return self.head is None






	def 					_lowercase(    self       )     ->     None:
								print("""stack elements are:"""       )
								UpperCAmelCase	:						Optional[Any]					=						self.head
								while temp is not None:
															print(temp.data						,						end="""->"""       )
															UpperCAmelCase	:						int					=						temp.next


# Code execution starts here
if __name__ == "__main__":
							# Start with the empty stack
							a  :     Stack[int]       =       Stack()

							# Insert 4 at the beginning. So stack becomes 4->None
							print("""Stack operations using Doubly LinkedList""")
							stack.push(4)

							# Insert 5 at the beginning. So stack becomes 4->5->None
							stack.push(5)

							# Insert 6 at the beginning. So stack becomes 4->5->6->None
							stack.push(6)

							# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
							stack.push(7)

							# Print the stack
							stack.print_stack()

							# Print the top element
							print("""\nTop element is """, stack.top())

							# Print the stack size
							print("""Size of the stack is """, len(stack))

							# pop the top element
							stack.pop()

							# pop the top element
							stack.pop()

							# two elements have now been popped off
							stack.print_stack()

							# Print True if the stack is empty else False
							print("""\nstack is empty:""", stack.is_empty())






'''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  :     Optional[Any]       =       {
    """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  :     Dict       =       ["""DocumentQuestionAnsweringTool"""]
							a  :     List[str]       =       ["""ImageCaptioningTool"""]
							a  :     Tuple       =       ["""ImageQuestionAnsweringTool"""]
							a  :     Tuple       =       ["""ImageSegmentationTool"""]
							a  :     List[str]       =       ["""SpeechToTextTool"""]
							a  :     Dict       =       ["""TextClassificationTool"""]
							a  :     Union[str, Any]       =       ["""TextQuestionAnsweringTool"""]
							a  :     List[str]       =       ["""TextSummarizationTool"""]
							a  :     Optional[Any]       =       ["""TextToSpeechTool"""]
							a  :     str       =       ["""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  :     Dict       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

