






def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int | float | str       ):
	try:
		snake_case__			:				Union[str, Any]       =  float(snake_case_       )
	except ValueError:
		raise ValueError("Please enter a valid number"       )
	snake_case__			:				List[str]       =  decimal - int(snake_case_       )
	if fractional_part == 0:
		return int(snake_case_       ), 1
	else:
		snake_case__			:				Union[str, Any]       =  len(str(snake_case_       ).split("."       )[1]       )
		snake_case__			:				str       =  int(decimal * (10**number_of_frac_digits)       )
		snake_case__			:				Union[str, Any]       =  10**number_of_frac_digits
		snake_case__,						snake_case__			:				int       =  denominator, numerator
		while True:
			snake_case__			:				Any       =  dividend % divisor
			if remainder == 0:
				break
			snake_case__,						snake_case__			:				Tuple       =  divisor, remainder
		snake_case__,						snake_case__			:				Union[str, Any]       =  numerator / divisor, denominator / divisor
		return int(snake_case_       ), int(snake_case_       )


if __name__ == "__main__":
	print(f"{decimal_to_fraction(2) = }")
	print(f"{decimal_to_fraction(89.0) = }")
	print(f"{decimal_to_fraction('67') = }")
	print(f"{decimal_to_fraction('45.0') = }")
	print(f"{decimal_to_fraction(1.5) = }")
	print(f"{decimal_to_fraction('6.25') = }")
	print(f"{decimal_to_fraction('78td') = }")











from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch



@require_torch
@require_tf
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				Optional[Any]       =  SMALL_MODEL_IDENTIFIER
						snake_case__			:				Any       =  "pt"
						snake_case__			:				Any       =  "tf"



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  List[Any]	):
						snake_case__			:				int       =  AutoModel.from_pretrained(self.test_model	)
						model_pt.save_pretrained(__A	)



					def 	_lowercase					( self					:  Optional[int]							,		__A					:  Tuple	):
						snake_case__			:				List[Any]       =  TFAutoModel.from_pretrained(self.test_model							,		from_pt=__A	)
						model_tf.save_pretrained(__A	)



					def 	_lowercase					( self					:  str	):
						snake_case__			:				Optional[Any]       =  "mock_framework"

						# Framework provided - return whatever the user provides
						snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model							,		__A	)
						self.assertEqual(__A							,		__A	)

						# Local checkpoint and framework provided - return provided framework
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
							self._setup_pt_ckpt(__A	)
							snake_case__			:				Optional[int]       =  FeaturesManager.determine_framework(__A							,		__A	)
							self.assertEqual(__A							,		__A	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
							self._setup_tf_ckpt(__A	)
							snake_case__			:				int       =  FeaturesManager.determine_framework(__A							,		__A	)
							self.assertEqual(__A							,		__A	)



					def 	_lowercase					( self					:  Dict	):
						# PyTorch checkpoint
						with TemporaryDirectory() as local_pt_ckpt:
							self._setup_pt_ckpt(__A	)
							snake_case__			:				List[str]       =  FeaturesManager.determine_framework(__A	)
							self.assertEqual(__A							,		self.framework_pt	)

						# TensorFlow checkpoint
						with TemporaryDirectory() as local_tf_ckpt:
							self._setup_tf_ckpt(__A	)
							snake_case__			:				Tuple       =  FeaturesManager.determine_framework(__A	)
							self.assertEqual(__A							,		self.framework_tf	)

						# Invalid local checkpoint
						with TemporaryDirectory() as local_invalid_ckpt:
							with self.assertRaises(__A	):
								snake_case__			:				int       =  FeaturesManager.determine_framework(__A	)






					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				Dict       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	):
							snake_case__			:				List[Any]       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_pt	)

						# PyTorch not in environment -> use TensorFlow
						snake_case__			:				Tuple       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_torch_available"							,		__A	):
							snake_case__			:				int       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_tf	)

						# Both in environment -> use PyTorch
						snake_case__			:				Dict       =  MagicMock(return_value=__A	)
						snake_case__			:				Optional[int]       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	), patch(
						    "transformers.onnx.features.is_torch_available"							,		__A	):
							snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model	)
							self.assertEqual(__A							,		self.framework_pt	)

						# Both not in environment -> raise error
						snake_case__			:				List[str]       =  MagicMock(return_value=__A	)
						snake_case__			:				Optional[Any]       =  MagicMock(return_value=__A	)
						with patch("transformers.onnx.features.is_tf_available"							,		__A	), patch(
						    "transformers.onnx.features.is_torch_available"							,		__A	):
							with self.assertRaises(__A	):
								snake_case__			:				Optional[Any]       =  FeaturesManager.determine_framework(self.test_model	)



