"""simple docstring"""





from __future__ import annotations

import math
from collections.abc import Callable





def 				a_     (      _lowerCAmelCase  : Callable[[int | float], int | float]   , _lowerCAmelCase  : int | float   , _lowerCAmelCase  : int | float   , _lowerCAmelCase  : int = 100   , ):





       '''simple docstring'''

       lowercase__			:		Dict       =	x_start
       lowercase__			:		Union[str, Any]       =	fnc(_lowerCAmelCase     )
       lowercase__			:		Optional[Any]       =	0.0

       for _ in range(_lowerCAmelCase     ):
              # Approximates curve as a sequence of linear lines and sums their length
              lowercase__			:		Union[str, Any]       =	(x_end - x_start) / steps + xa
              lowercase__			:		Union[str, Any]       =	fnc(_lowerCAmelCase     )
              length += math.hypot(xa - xa   , fxa - fxa     )

              # Increment step
              lowercase__			:		Union[str, Any]       =	xa
              lowercase__			:		int       =	fxa

       return length


if __name__ == "__main__":





       def 				a_     (      _lowerCAmelCase  : List[Any]     ):





              '''simple docstring'''

              return math.sin(10 * x     )

       print("f(x) = sin(10 * x)")
       print("The length of the curve from x = -10 to x = 10 is:")
       _UpperCamelCase   :						str      =    10
       while i <= 10_00_00:
              print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
              i *= 10



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	:    Optional[int]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoTokenizer.from_pretrained(lowercase	)
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			tokenizer('This is me'			,  return_tensors='pt'	)

				_snake_case		=			model.to_bettertransformer()

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

				_snake_case		=			model.generate(**lowercase	)

				_snake_case		=			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(lowercase	)

							_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

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

							_snake_case		=			model_reloaded.generate(**lowercase	)
							self.assertTrue(torch.allclose(lowercase			,  lowercase	)	)





	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			model.to_bettertransformer()

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

							_snake_case		=			model.reverse_bettertransformer()
							model.save_pretrained(lowercase	)