



"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_      						=			"scheduler_config.json"




class       snake_case_    (    a_					):
							__lowerCAmelCase            =       1
							__lowerCAmelCase            =       2
							__lowerCAmelCase            =       3
							__lowerCAmelCase            =       4
							__lowerCAmelCase            =       5




@dataclass
class       snake_case_    (    a_					):
							__lowerCAmelCase            =       4_2




class       snake_case_    :
							__lowerCAmelCase            =       SCHEDULER_CONFIG_NAME
							__lowerCAmelCase            =       ["dtype"]
							__lowerCAmelCase            =       []
							__lowerCAmelCase            =       True






							@classmethod
							def snake_case_	( cls      ,  a_ = None      ,  a_ = None      ,  a_=False      ,  **a_      ,  ):
									a_    ,					a_  :   Any			    =  cls.load_config(
									    pretrained_model_name_or_path=a_      ,  subfolder=a_      ,  return_unused_kwargs=a_      ,  **a_      ,  )
									a_    ,					a_  :   List[str]			    =  cls.from_config(a_      ,  return_unused_kwargs=a_      ,  **a_   )

									if hasattr(a_      ,  "create_state"   ) and getattr(a_      ,  "has_state"      ,  a_   ):
											a_  :   Dict			    =  scheduler.create_state()

									if return_unused_kwargs:
											return scheduler, state, unused_kwargs

									return scheduler, state






							def snake_case_	( self      ,  a_      ,  a_ = False      ,  **a_   ):
									self.save_config(save_directory=a_      ,  push_to_hub=a_      ,  **a_   )






							@property
							def snake_case_	( self   ):
									return self._get_compatibles()






							@classmethod
							def snake_case_	( cls   ):
									a_  :   List[str]			    =  list(set([cls.__name__] + cls._compatibles   )   )
									a_  :   Optional[int]			    =  importlib.import_module(__name__.split("."   )[0]   )
									a_  :   Any			    =  [
									    getattr(a_      ,  a_   ) for c in compatible_classes_str if hasattr(a_      ,  a_   )
									]
									return compatible_classes



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__						)      ->   jnp.ndarray:
		assert len(snake_case__						) >= x.ndim
		return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(snake_case__						) - x.ndim)						),			snake_case__						)



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__=0.9_99,			SCREAMING_SNAKE_CASE__=jnp.floataa						)      ->   jnp.ndarray:





		def alpha_bar(SCREAMING_SNAKE_CASE__						):
				return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2						) ** 2

		a_  :   Any			    =  []
		for i in range(snake_case__						):
				a_  :   List[str]			    =  i / num_diffusion_timesteps
				a_  :   Tuple			    =  (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar(snake_case__						) / alpha_bar(snake_case__						),			snake_case__						)						)
		return jnp.array(snake_case__,			dtype=snake_case__						)




@flax.struct.dataclass
class       snake_case_    :
							__lowerCAmelCase            =       4_2
							__lowerCAmelCase            =       4_2
							__lowerCAmelCase            =       4_2






							@classmethod
							def snake_case_	( cls      ,  a_   ):
									a_  :   Optional[Any]			    =  scheduler.config

									if config.trained_betas is not None:
											a_  :   Tuple			    =  jnp.asarray(config.trained_betas      ,  dtype=scheduler.dtype   )
									elif config.beta_schedule == "linear":
											a_  :   List[str]			    =  jnp.linspace(config.beta_start      ,  config.beta_end      ,  config.num_train_timesteps      ,  dtype=scheduler.dtype   )
									elif config.beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											a_  :   Optional[Any]			    =  (
											    jnp.linspace(
											        config.beta_start**0.5      ,  config.beta_end**0.5      ,  config.num_train_timesteps      ,  dtype=scheduler.dtype   )
											    ** 2
											)
									elif config.beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											a_  :   Any			    =  betas_for_alpha_bar(config.num_train_timesteps      ,  dtype=scheduler.dtype   )
									else:
											raise NotImplementedError(
											    F"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""   )

									a_  :   Dict			    =  1.0 - betas

									a_  :   Optional[int]			    =  jnp.cumprod(a_      ,  axis=0   )

									return cls(
									    alphas=a_      ,  betas=a_      ,  alphas_cumprod=a_      ,  )



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__						)      ->   List[Any]:
		a_  :   Dict			    =  state.alphas_cumprod

		a_  :   Dict			    =  alphas_cumprod[timesteps] ** 0.5
		a_  :   int			    =  sqrt_alpha_prod.flatten()
		a_  :   List[Any]			    =  broadcast_to_shape_from_left(snake_case__,			original_samples.shape						)

		a_  :   List[str]			    =  (1 - alphas_cumprod[timesteps]) ** 0.5
		a_  :   Any			    =  sqrt_one_minus_alpha_prod.flatten()
		a_  :   Optional[int]			    =  broadcast_to_shape_from_left(snake_case__,			original_samples.shape						)

		return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__						)      ->   Optional[int]:
		a_    ,					a_  :   int			    =  get_sqrt_alpha_prod(snake_case__,			snake_case__,			snake_case__,			snake_case__						)
		a_  :   str			    =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
		return noisy_samples



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__						)      ->   Optional[Any]:
		a_    ,					a_  :   str			    =  get_sqrt_alpha_prod(snake_case__,			snake_case__,			snake_case__,			snake_case__						)
		a_  :   str			    =  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
		return velocity




from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_lowerCAmelCase	:   List[str]    =   {"UserAgent": UserAgent().random}







def     UpperCAmelCase_		(					snake_case__					)	->     dict:



				"""simple docstring"""





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


class 	__snake_case   :
		def __init__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  f'https://www.instagram.com/{username}/'
						lowerCAmelCase__		  =  self.get_json()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  requests.get(self.url    ,headers=a_						).text
						lowerCAmelCase__		  =  BeautifulSoup(a_    ,'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 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["username"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["full_name"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["biography"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["business_email"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["external_url"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["edge_followed_by"]["count"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["edge_follow"]["count"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["edge_owner_to_timeline_media"]["count"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["profile_pic_url_hd"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["is_verified"]



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["is_private"]







def     UpperCAmelCase_		(					snake_case__ = "github"					)	->     None:



				"""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 > 150
				assert instagram_user.number_of_followers > 120000
				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()
	_lowerCAmelCase	:   Optional[int]    =   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 = }""")
