



from __future__ import annotations

from collections.abc import Callable

def      A_ (		__a       :			Callable[[int | float], int | float]	,						__a       :			int | float	,						__a       :			int | float	,						__a       :			int = 100	,						):

				"""simple docstring"""
				a__			 =						x_start
				a__			 =						fnc(__a						)
				a__			 =						0.0

				for _ in range(__a						):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								a__			 =						(x_end - x_start) / steps + xa
								a__			 =						fnc(__a						)
								area += abs(fxa + fxa						) * (xa - xa) / 2

								# Increment step
								a__			 =						xa
								a__			 =						fxa
				return area


if __name__ == "__main__":

	def      A_ (		__a       :			str						):

					"""simple docstring"""
					return x**3

	print("""f(x) = x^3""")
	print("""The area between the curve, x = -10, x = 10 and the x axis is:""")
	UpperCAmelCase								=   10
	while i <= 100_000:
		UpperCAmelCase								=   trapezoidal_area(f, -5, 5, i)
		print(f"""with {i} steps: {area}""")
		i *= 10






import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							@cached_property
							def _a (							self						):
											return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""						)


							@cached_property
							def _a (							self						):
											return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""						)


							def _a (							self						):
											a__			 =						self.tokenizer_ab
											a__			 =						""" I am a small frog."""
											a__			 =						tok([src_text]					,					padding=a_					,					truncation=a_						)["""input_ids"""]
											a__			 =						tok.batch_decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)[0]
											assert src_text == decoded


							def _a (							self						):
											a__			 =						self.rust_tokenizer_ab
											a__			 =						""" I am a small frog."""
											a__			 =						tok([src_text]					,					padding=a_					,					truncation=a_						)["""input_ids"""]
											a__			 =						tok.batch_decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)[0]
											assert src_text == decoded


							def _a (							self						):
											assert self.tokenizer_ab.add_prefix_space
											assert self.tokenizer_ab([""" Sam""", """Sam"""]						).input_ids == [[5_502, 2], [5_502, 2]]







							def _a (							self						):
											assert self.rust_tokenizer_ab.add_prefix_space
											assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]						).input_ids == [[5_502, 2], [5_502, 2]]

