




"""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_       )






from collections.abc import Sequence
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	return sum(c * (x**i) for i, c in enumerate(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	__A :			Any		    =			0.0
	for coeff in reversed(a__       ):
		__A :			List[str]		    =			result * x + coeff
	return result


if __name__ == "__main__":
			UpperCAmelCase_							:					List[str]		 =   (0.0, 0.0, 5.0, 9.3, 7.0)
			UpperCAmelCase_							:					str		 =   10.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))





