

import random





class 	snake_case_   :







			'''simple docstring'''

			@staticmethod
			def 				snake_case__(     _UpperCamelCase :				str							)      ->tuple[list[int], list[int]]:
									snake_case_   =	[ord(_UpperCamelCase							) for i in text]
									snake_case_   =	[]
									snake_case_   =	[]
									for i in plain:
															snake_case_   =	random.randint(1							,   3_0_0							)
															snake_case_   =	(i + k) * k
															cipher.append(_UpperCamelCase							)
															key.append(_UpperCamelCase							)
									return cipher, key







			@staticmethod
			def 				snake_case__(     _UpperCamelCase :				list[int]							,   _UpperCamelCase :				list[int]							)      ->str:
									snake_case_   =	[]
									for i in range(len(_UpperCamelCase							)							):
															snake_case_   =	int((cipher[i] - (key[i]) ** 2) / key[i]							)
															plain.append(chr(_UpperCamelCase							)							)
									return "".join(_UpperCamelCase							)


if __name__ == "__main__":
					lowerCAmelCase_	,      lowerCAmelCase_       	=	Onepad().encrypt('''Hello''')
					print(c, k)
					print(Onepad().decrypt(c, k))


# 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Any:
									return self.pre_processor.decode(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)