

import numpy as np

A_			:					str    			=     [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]



class        _a		:


					'''simple docstring'''






					def __init__(					self     ):
						A__				:       str	  =						np.array(A__     )






					def     __A     (					self       ,			A__     ):
						A__  ,  A__				:       Union[str, Any]	  =						np.where(letter == self.SQUARE     )
						A__				:       Any	  =						np.concatenate([indexa + 1, indexa + 1]     )
						return indexes






					def     __A     (					self       ,			A__       ,			A__     ):
						A__				:       str	  =						self.SQUARE[indexa - 1, indexa - 1]
						return letter






					def     __A     (					self       ,			A__     ):
						A__				:       List[str]	  =						message.lower()
						A__				:       Dict	  =						message.replace(""" """       ,			""""""     )
						A__				:       str	  =						message.replace("""j"""       ,			"""i"""     )

						A__				:       Dict	  =						np.empty((2, len(A__     ))     )
						for letter_index in range(len(A__     )     ):
							A__				:       Union[str, Any]	  =						self.letter_to_numbers(message[letter_index]     )

							A__				:       Any	  =						numbers[0]
							A__				:       str	  =						numbers[1]

						A__				:       List[Any]	  =						first_step.reshape(2 * len(A__     )     )
						A__				:       Optional[Any]	  =						""""""
						for numbers_index in range(len(A__     )     ):
							A__				:       List[str]	  =						int(second_step[numbers_index * 2]     )
							A__				:       List[Any]	  =						int(second_step[(numbers_index * 2) + 1]     )
							A__				:       List[str]	  =						self.numbers_to_letter(A__       ,			A__     )
							A__				:       Optional[int]	  =						encoded_message + letter

						return encoded_message






					def     __A     (					self       ,			A__     ):
						A__				:       Optional[int]	  =						message.lower()
						message.replace(""" """       ,			""""""     )
						A__				:       Tuple	  =						np.empty(2 * len(A__     )     )
						for letter_index in range(len(A__     )     ):
							A__				:       List[Any]	  =						self.letter_to_numbers(message[letter_index]     )
							A__				:       Optional[int]	  =						numbers[0]
							A__				:       Optional[int]	  =						numbers[1]

						A__				:       Union[str, Any]	  =						first_step.reshape((2, len(A__     ))     )
						A__				:       str	  =						""""""
						for numbers_index in range(len(A__     )     ):
							A__				:       Dict	  =						int(second_step[0, numbers_index]     )
							A__				:       str	  =						int(second_step[1, numbers_index]     )
							A__				:       Dict	  =						self.numbers_to_letter(A__       ,			A__     )
							A__				:       Optional[Any]	  =						decoded_message + letter

						return decoded_message






import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class        _a		(unittest.TestCase						,  __magic_name__							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__				:       List[str]	  =						load_tool("""text-to-speech"""     )
						self.tool.setup()






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Tuple	  =						self.tool("""hey"""     )
						A__				:       Tuple	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Any	  =						self.tool("""hey"""     )
						A__				:       Optional[Any]	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )



