



'''simple docstring'''






import math


def 				snake_case_  (      _lowerCAmelCase						:		Optional[int]						)   ->     int:

							if not isinstance(_lowercase     ,   _lowercase						):
														UpperCAmelCase				: Any											=	f"""Input value of [number={number}] must be an integer"""
														raise TypeError(_lowercase						)

							if number < 1:
														UpperCAmelCase				: Optional[int]											=	f"""Input value of [number={number}] must be > 0"""
														raise ValueError(_lowercase						)
							elif number == 1:
														return 3
							elif number == 2:
														return 5
							else:
														UpperCAmelCase				: Union[str, Any]											=	int(math.log(number // 3     ,   2						)						) + 2

														UpperCAmelCase				: Dict											=	[3, 5]
														UpperCAmelCase				: List[str]											=	2
														UpperCAmelCase				: str											=	3
														for block in range(1     ,   _lowercase						):
																					for _ in range(_lowercase						):
																												proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]						)
																												proth_index += 1
																					increment *= 2

							return proth_list[number - 1]


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							for number in range(11):
														UpperCamelCase__:     int										=    0
														try:
																					UpperCamelCase__:     Union[str, Any]										=    proth(number)
														except ValueError:
																					print(F"ValueError: there is no {number}th Proth number")
																					continue

														print(F"The {number}th Proth number: {value}")










'''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        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: int											=	AutoTokenizer.from_pretrained(__snake_case	)
								UpperCAmelCase				: Tuple											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	tokenizer('''This is me'''	,    return_tensors='''pt'''	)

								UpperCAmelCase				: Union[str, Any]											=	model.to_bettertransformer()

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

								UpperCAmelCase				: Dict											=	model.generate(**__snake_case	)

								UpperCAmelCase				: Union[str, Any]											=	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(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

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

															UpperCAmelCase				: Dict											=	model_reloaded.generate(**__snake_case	)
															self.assertTrue(torch.allclose(__snake_case	,    __snake_case	)	)







	def   A			(		self    :  Optional[int]	)    ->	List[Any]:
								UpperCAmelCase				: Dict											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: List[Any]											=	model.to_bettertransformer()

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

															UpperCAmelCase				: Any											=	model.reverse_bettertransformer()
															model.save_pretrained(__snake_case	)





