


"""simple docstring"""


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean






def      a__ (				__lowercase   )	->	Tuple:

				# A local function to see if a dot lands in the circle.
				def is_in_circle(__lowercase     ,			__lowercase   ) -> bool:
								_A					  =			sqrt((x**2) + (y**2)   )
								# Our circle has a radius of 1, so a distance
								# greater than 1 would land outside the circle.
								return distance_from_centre <= 1

				# The proportion of guesses that landed in the circle
				_A					  =			mean(
				    int(is_in_circle(uniform(-1.0     ,			1.0   )     ,			uniform(-1.0     ,			1.0   )   )   )
				    for _ in range(__lowercase   )   )
				# The ratio of the area for circle to square is pi/4.
				_A					  =			proportion * 4
				print(f"""The estimated value of pi is {pi_estimate}"""   )
				print(f"""The numpy value of pi is {pi}"""   )
				print(f"""The total error is {abs(pi - pi_estimate   )}"""   )






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase = 0.0     ,			__lowercase = 1.0     ,			)	->	float:
				return mean(
				    function_to_integrate(uniform(__lowercase     ,			__lowercase   )   ) for _ in range(__lowercase   )   ) * (max_value - min_value)






def      a__ (				__lowercase     ,			__lowercase = 0.0     ,			__lowercase = 1.0   )	->	None:

				def identity_function(__lowercase   ) -> float:
								return x

				_A					  =			area_under_curve_estimator(
				    __lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase   )
				_A					  =			(max_value * max_value - min_value * min_value) / 2

				print("******************"   )
				print(f"""Estimating area under y=x where x varies from {min_value} to {max_value}"""   )
				print(f"""Estimated value is {estimated_value}"""   )
				print(f"""Expected value is {expected_value}"""   )
				print(f"""Total error is {abs(estimated_value - expected_value   )}"""   )
				print("******************"   )






def      a__ (				__lowercase   )	->	None:

				def function_to_integrate(__lowercase   ) -> float:
								return sqrt(4.0 - x * x   )

				_A					  =			area_under_curve_estimator(
				    __lowercase     ,			__lowercase     ,			0.0     ,			2.0   )

				print("******************"   )
				print("Estimating pi using area_under_curve_estimator"   )
				print(f"""Estimated value is {estimated_value}"""   )
				print(f"""Expected value is {pi}"""   )
				print(f"""Total error is {abs(estimated_value - pi   )}"""   )
				print("******************"   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()



"""simple docstring"""


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin
class snake_case      (  unittest.TestCase     ,				_UpperCamelCase):







		def  a_ (					self      :       Optional[Any]		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			load_tool("text-classification"		)
						self.tool.setup()
						_A					  =			load_tool("text-classification"    ,   remote=a__		)







		def  a_ (					self      :       Optional[int]		)       ->						Dict:





						'''simple docstring'''







						_A					  =			self.tool("That's quite cool"    ,   ["positive", "negative"]		)
						self.assertEqual(a__    ,   "positive"		)







		def  a_ (					self      :       Optional[Any]		)       ->						Dict:





						'''simple docstring'''







						_A					  =			self.remote_tool("That's quite cool"    ,   ["positive", "negative"]		)
						self.assertEqual(a__    ,   "positive"		)







		def  a_ (					self      :       Dict		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			self.tool(text="That's quite cool"    ,   labels=["positive", "negative"]		)
						self.assertEqual(a__    ,   "positive"		)







		def  a_ (					self      :       Dict		)       ->						Any:





						'''simple docstring'''







						_A					  =			self.remote_tool(text="That's quite cool"    ,   labels=["positive", "negative"]		)
						self.assertEqual(a__    ,   "positive"		)