




"""simple docstring"""




import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
			import torch

@require_torch
@require_optimum
@slow
class   UpperCAmelCase_    (			unittest.TestCase		):
		def 	_lowerCamelCase							(						self       )						->       str:
					__lowercase			:  Any								=       '''hf-internal-testing/tiny-random-t5'''
					__lowercase			:  int								=       AutoTokenizer.from_pretrained(UpperCamelCase_       )
					__lowercase			:  Any								=       AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_       )

					__lowercase			:  Tuple								=       tokenizer('''This is me'''				,						return_tensors='''pt'''       )

					__lowercase			:  List[str]								=       model.to_bettertransformer()

					self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()       )       )

					__lowercase			:  Dict								=       model.generate(**UpperCamelCase_       )

					__lowercase			:  int								=       model.reverse_bettertransformer()

					self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()       )       )

					with tempfile.TemporaryDirectory() as tmpdirname:
								model.save_pretrained(UpperCamelCase_       )

								__lowercase			:  str								=       AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_       )

								self.assertFalse(
								    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()       )       )

								__lowercase			:  List[str]								=       model_reloaded.generate(**UpperCamelCase_       )
								self.assertTrue(torch.allclose(UpperCamelCase_				,						UpperCamelCase_       )       )
		def 	_lowerCamelCase							(						self       )						->       Optional[Any]:
					__lowercase			:  str								=       '''hf-internal-testing/tiny-random-t5'''
					__lowercase			:  List[Any]								=       AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_       )

					__lowercase			:  Any								=       model.to_bettertransformer()

					with tempfile.TemporaryDirectory() as tmpdirname:
								with self.assertRaises(UpperCamelCase_       ):
											model.save_pretrained(UpperCamelCase_       )

								__lowercase			:  Tuple								=       model.reverse_bettertransformer()
								model.save_pretrained(UpperCamelCase_       )




'''simple docstring'''


from __future__ import annotations

__lowerCAmelCase						:							List[str]    							=						8.988e9  # units = N * m^s * C^-2





def    lowerCAmelCase       (		UpperCamelCase__					: float     ,       UpperCamelCase__					: float     ,       UpperCamelCase__					: float     ,       UpperCamelCase__					: float							):


	"""simple docstring"""
	__UpperCAmelCase      					=      abs(chargea * chargea							)

	if (force, chargea, chargea, distance).count(0							) != 1:
		raise ValueError('''One and only one argument must be 0'''							)
	if distance < 0:
		raise ValueError('''Distance cannot be negative'''							)
	if force == 0:
		__UpperCAmelCase      					=      COULOMBS_CONSTANT * charge_product / (distance**2)
		return {"force": force}
	elif chargea == 0:
		__UpperCAmelCase      					=      abs(UpperCamelCase__							) * (distance**2) / (COULOMBS_CONSTANT * chargea)
		return {"charge1": chargea}
	elif chargea == 0:
		__UpperCAmelCase      					=      abs(UpperCamelCase__							) * (distance**2) / (COULOMBS_CONSTANT * chargea)
		return {"charge2": chargea}
	elif distance == 0:
		__UpperCAmelCase      					=      (COULOMBS_CONSTANT * charge_product / abs(UpperCamelCase__							)) ** 0.5
		return {"distance": distance}
	raise ValueError('''Exactly one argument must be 0'''							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
