
import random

class     _UpperCamelCase			:






			'''simple docstring'''

			@staticmethod
			def  lowerCAmelCase__		(    snake_case_	:    str ):
							UpperCamelCase_:							Any		=  [ord(snake_case_ ) for i in text]
							UpperCamelCase_:							Union[str, Any]		=  []
							UpperCamelCase_:							Any		=  []
							for i in plain:
											UpperCamelCase_:							Any		=  random.randint(1  ,		300 )
											UpperCamelCase_:							Union[str, Any]		=  (i + k) * k
											cipher.append(snake_case_ )
											key.append(snake_case_ )
							return cipher, key




			@staticmethod
			def  lowerCAmelCase__		(    snake_case_	:    list[int]  ,		snake_case_	:    list[int] ):
							UpperCamelCase_:							List[Any]		=  []
							for i in range(len(snake_case_ ) ):
											UpperCamelCase_:							int		=  int((cipher[i] - (key[i]) ** 2) / key[i] )
											plain.append(chr(snake_case_ ) )
							return "".join(snake_case_ )


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








import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow

@is_pipeline_test
@require_torch
class     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

			@require_torch
			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Any		=  pipeline(
							    task="""zero-shot-audio-classification"""  ,		model="""hf-internal-testing/tiny-clap-htsat-unfused""" )
							UpperCamelCase_:							str		=  load_dataset("""ashraq/esc50""" )
							UpperCamelCase_:							Optional[int]		=  dataset["""train"""]["""audio"""][-1]["""array"""]
							UpperCamelCase_:							Optional[int]		=  audio_classifier(snake_case_  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[{"""score""": 0.501, """label""": """Sound of a dog"""}, {"""score""": 0.499, """label""": """Sound of vaccum cleaner"""}]  ,		)

			@unittest.skip("""No models are available in TF""" )
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							pass

			@slow
			@require_torch
			def  lowerCAmelCase__		(    self	:    int ):
							UpperCamelCase_:							List[str]		=  pipeline(
							    task="""zero-shot-audio-classification"""  ,		model="""laion/clap-htsat-unfused"""  ,		)
							# This is an audio of a dog
							UpperCamelCase_:							Tuple		=  load_dataset("""ashraq/esc50""" )
							UpperCamelCase_:							Optional[int]		=  dataset["""train"""]["""audio"""][-1]["""array"""]
							UpperCamelCase_:							int		=  audio_classifier(snake_case_  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )

							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[
							        {"""score""": 0.999, """label""": """Sound of a dog"""},
							        {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
							    ]  ,		)

							UpperCamelCase_:							Any		=  audio_classifier([audio] * 5  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""] )
							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[
							        [
							            {"""score""": 0.999, """label""": """Sound of a dog"""},
							            {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
							        ],
							    ]
							    * 5  ,		)
							UpperCamelCase_:							Any		=  audio_classifier(
							    [audio] * 5  ,		candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]  ,		batch_size=5 )
							self.assertEqual(
							    nested_simplify(snake_case_ )  ,		[
							        [
							            {"""score""": 0.999, """label""": """Sound of a dog"""},
							            {"""score""": 0.001, """label""": """Sound of vaccum cleaner"""},
							        ],
							    ]
							    * 5  ,		)




			@unittest.skip("""No models are available in TF""" )
			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							pass






