

'''simple docstring'''





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





def a(lowercase__	):





	'''simple docstring'''







	def is_in_circle(lowercase__			, lowercase__	) -> bool:
		snake_case_      		= 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
	snake_case_      		= 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.
	snake_case_      		= 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			, ):





	'''simple docstring'''
	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	):





	'''simple docstring'''







	def identity_function(lowercase__	) -> float:
		return x

	snake_case_      		= area_under_curve_estimator(
	    lowercase__			, lowercase__			, lowercase__			, lowercase__	)
	snake_case_      		= (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__	):





	'''simple docstring'''







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

	snake_case_      		= 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()



# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


A											=     '3'

print('Python version:', sys.version)

print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())

try:
		import torch

		print('Torch version:', torch.__version__)
		print('Cuda available:', torch.cuda.is_available())
		print('Cuda version:', torch.version.cuda)
		print('CuDNN version:', torch.backends.cudnn.version())
		print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
		print('Torch version:', None)

try:
		import transformers

		print('transformers version:', transformers.__version__)
except ImportError:
		print('transformers version:', None)
