



from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

A__	      =       {"""UserAgent""": UserAgent().random}


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					_lowerCAmelCase						=    script.contents[0]
					_lowerCAmelCase						=    json.loads(data[data.find("""{\"config\""""			) : -1]			)
					return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class 					__lowerCAmelCase :




				def __init__(							self						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    F'https://www.instagram.com/{username}/'
									_lowerCAmelCase						=    self.get_json()




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    requests.get(self.url						,				headers=_snake_case		).text
									_lowerCAmelCase						=    BeautifulSoup(_snake_case						,				"""html.parser"""		).find_all("""script"""		)
									try:
														return extract_user_profile(scripts[4]		)
									except (json.decoder.JSONDecodeError, KeyError):
														return extract_user_profile(scripts[3]		)




				def __repr__(							self		):



									"""simple docstring"""





									return F'{self.__class__.__name__}(\'{self.username}\')'




				def __str__(							self		):



									"""simple docstring"""





									return F'{self.fullname} ({self.username}) is {self.biography}'




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.user_data["username"]




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.user_data["full_name"]




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.user_data["biography"]




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.user_data["business_email"]




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.user_data["external_url"]




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.user_data["edge_followed_by"]["count"]




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.user_data["edge_follow"]["count"]




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.user_data["edge_owner_to_timeline_media"]["count"]




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.user_data["profile_pic_url_hd"]




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.user_data["is_verified"]




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									return self.user_data["is_private"]


def 		_UpperCAmelCase				(      snake_case = "github"			):





					"""simple docstring"""

					import os

					if os.environ.get("""CI"""			):
										return  # test failing on GitHub Actions
					_lowerCAmelCase						=    InstagramUser(snake_case			)
					assert instagram_user.user_data
					assert isinstance(instagram_user.user_data	,						snake_case			)
					assert instagram_user.username == username
					if username != "github":
										return
					assert instagram_user.fullname == "GitHub"
					assert instagram_user.biography == "Built for developers."
					assert instagram_user.number_of_posts > 1_50
					assert instagram_user.number_of_followers > 12_00_00
					assert instagram_user.number_of_followings > 15
					assert instagram_user.email == "support@github.com"
					assert instagram_user.website == "https://github.com/readme"
					assert instagram_user.profile_picture_url.startswith("""https://instagram."""			)
					assert instagram_user.is_verified is True
					assert instagram_user.is_private is False


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	A__	      =       InstagramUser("""github""")
	print(instagram_user)
	print(f"{instagram_user.number_of_posts = }")
	print(f"{instagram_user.number_of_followers = }")
	print(f"{instagram_user.number_of_followings = }")
	print(f"{instagram_user.email = }")
	print(f"{instagram_user.website = }")
	print(f"{instagram_user.profile_picture_url = }")
	print(f"{instagram_user.is_verified = }")
	print(f"{instagram_user.is_private = }")





'''simple docstring'''







from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								return 0.0







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[int | float, int | float]:
	_snake_case   						=		min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
	_snake_case   						=		max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
	return lowest, highest







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	None:
	_snake_case   						=		512
	_snake_case   						=		[1] + [0] * (size - 1)
	_snake_case   						=		[filter_type.process(__A	) for item in inputs]

	_snake_case   						=		[0] * (samplerate - size)  # zero-padding
	outputs += filler
	_snake_case   						=		np.abs(np.fft.fft(__A	)	)
	_snake_case   						=		20 * np.logaa(__A	)

	# Frequencies on log scale from 24 to nyquist frequency
	plt.xlim(24			,				samplerate / 2 - 1	)
	plt.xlabel('Frequency (Hz)'	)
	plt.xscale('log'	)

	# Display within reasonable bounds
	_snake_case   						=		get_bounds(__A			,				__A	)
	plt.ylim(max([-80, bounds[0]]	)			,				min([80, bounds[1]]	)	)
	plt.ylabel('Gain (dB)'	)

	plt.plot(__A	)
	plt.show()







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	None:
	_snake_case   						=		512
	_snake_case   						=		[1] + [0] * (size - 1)
	_snake_case   						=		[filter_type.process(__A	) for item in inputs]

	_snake_case   						=		[0] * (samplerate - size)  # zero-padding
	outputs += filler
	_snake_case   						=		np.angle(np.fft.fft(__A	)	)

	# Frequencies on log scale from 24 to nyquist frequency
	plt.xlim(24			,				samplerate / 2 - 1	)
	plt.xlabel('Frequency (Hz)'	)
	plt.xscale('log'	)

	plt.ylim(-2 * pi			,				2 * pi	)
	plt.ylabel('Phase shift (Radians)'	)
	plt.plot(np.unwrap(__A			,				-2 * pi	)	)
	plt.show()

