"""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__)










def 		_UpperCamelCase      (			UpperCamelCase_				:    str						)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  len(UpperCamelCase_						)
						lowerCAmelCase__			  =  [0] * length
						lowerCAmelCase__			  =  [[False for i in range(UpperCamelCase_						)] for j in range(UpperCamelCase_						)]
						for i, c in enumerate(UpperCamelCase_						):
												lowerCAmelCase__			  =  i
												for j in range(i + 1						):
																		if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
																								lowerCAmelCase__			  =  True
																								lowerCAmelCase__			  =  min(UpperCamelCase_			,				0 if j == 0 else (cut[j - 1] + 1)						)
												lowerCAmelCase__			  =  mincut
						return cut[length - 1]


if __name__ == "__main__":
					__snake_case			:			Optional[Any]			   =   input("""Enter the string: """).strip()
					__snake_case			:			List[Any]			   =   find_minimum_partitions(s)
					print(f'Minimum number of partitions required for the \'{s}\' is {ans}')





