from collections import namedtuple

SCREAMING_SNAKE_CASE		:     int         =	namedtuple("from_to", "from_ to")

SCREAMING_SNAKE_CASE		:     str         =	{
    "cubicmeter": from_to(1, 1),
    "litre": from_to(0.001, 1000),
    "kilolitre": from_to(1, 1),
    "gallon": from_to(0.00454, 264.172),
    "cubicyard": from_to(0.76455, 1.30795),
    "cubicfoot": from_to(0.028, 35.3147),
    "cup": from_to(0.000236588, 4226.75),
}






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				str			):
						if from_type not in METRIC_CONVERSION:
												raise ValueError(
												    f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
												    + """, """.join(_SCREAMING_SNAKE_CASE			)			)
						if to_type not in METRIC_CONVERSION:
												raise ValueError(
												    f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
												    + """, """.join(_SCREAMING_SNAKE_CASE			)			)
						return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
	import doctest

	doctest.testmod()







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        UpperCamelCase						(				unittest.TestCase	,	__a				):





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Any  =		load_tool("""text-to-speech"""						)
												self.tool.setup()





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Optional[Any]  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Tuple  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)






