

"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)







@flax.struct.dataclass
class 	__UpperCamelCase    :
  _UpperCAmelCase          =		42

  # setable values
  _UpperCAmelCase          =		42
  _UpperCAmelCase          =		42
  _UpperCAmelCase          =		None

  @classmethod
  def __lowerCamelCase		(		cls    ,_A    ,_A    ,_A      ):
    '''simple docstring'''

    return cls(common=__lowerCamelCase    ,init_noise_sigma=__lowerCamelCase    ,timesteps=__lowerCamelCase      )







@dataclass
class 	__UpperCamelCase    (    _A						):
  _UpperCAmelCase          =		42







class 	__UpperCamelCase    (    _A							,							_A						):
  _UpperCAmelCase          =		[e.name for e in FlaxKarrasDiffusionSchedulers]

  _UpperCAmelCase          =		42

  @property
  def __lowerCamelCase		(		self      ):
    '''simple docstring'''

    return True

  @register_to_config
  def __init__(		self    ,_A = 1000    ,_A = 0.0_0_0_1    ,_A = 0.0_2    ,_A = "linear"    ,_A = None    ,_A = "fixed_small"    ,_A = True    ,_A = "epsilon"    ,_A = jnp.floataa    ,):
    '''simple docstring'''

    _lowerCAmelCase       :   str  =    dtype

  def __lowerCamelCase		(		self    ,_A = None      ):
    '''simple docstring'''

    if common is None:
      _lowerCAmelCase       :   int  =    CommonSchedulerState.create(self      )

    # standard deviation of the initial noise distribution
    _lowerCAmelCase       :   Tuple  =    jnp.array(1.0    ,dtype=self.dtype      )

    _lowerCAmelCase       :   Union[str, Any]  =    jnp.arange(0    ,self.config.num_train_timesteps      ).round()[::-1]

    return DDPMSchedulerState.create(
        common=__lowerCamelCase    ,init_noise_sigma=__lowerCamelCase    ,timesteps=__lowerCamelCase    ,)

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A = None      ):
    '''simple docstring'''

    return sample

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A = ()      ):
    '''simple docstring'''

    _lowerCAmelCase       :   List[str]  =    self.config.num_train_timesteps // num_inference_steps
    # creates integer timesteps by multiplying by ratio
    # rounding to avoid issues when num_inference_step is power of 3
    _lowerCAmelCase       :   int  =    (jnp.arange(0    ,__lowerCamelCase      ) * step_ratio).round()[::-1]

    return state.replace(
        num_inference_steps=__lowerCamelCase    ,timesteps=__lowerCamelCase    ,)

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A=None    ,_A=None      ):
    '''simple docstring'''

    _lowerCAmelCase       :   str  =    state.common.alphas_cumprod[t]
    _lowerCAmelCase       :   Union[str, Any]  =    jnp.where(t > 0    ,state.common.alphas_cumprod[t - 1]    ,jnp.array(1.0    ,dtype=self.dtype      )      )

    # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
    # and sample from it to get previous sample
    # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
    _lowerCAmelCase       :   str  =    (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

    if variance_type is None:
      _lowerCAmelCase       :   Tuple  =    self.config.variance_type

    # hacks - were probably added for training stability
    if variance_type == "fixed_small":
      _lowerCAmelCase       :   str  =    jnp.clip(__lowerCamelCase    ,a_min=1E-20      )
    # for rl-diffuser https://arxiv.org/abs/2205.09991
    elif variance_type == "fixed_small_log":
      _lowerCAmelCase       :   str  =    jnp.log(jnp.clip(__lowerCamelCase    ,a_min=1E-20      )      )
    elif variance_type == "fixed_large":
      _lowerCAmelCase       :   Union[str, Any]  =    state.common.betas[t]
    elif variance_type == "fixed_large_log":
      # Glide max_log
      _lowerCAmelCase       :   str  =    jnp.log(state.common.betas[t]      )
    elif variance_type == "learned":
      return predicted_variance
    elif variance_type == "learned_range":
      _lowerCAmelCase       :   Optional[Any]  =    variance
      _lowerCAmelCase       :   str  =    state.common.betas[t]
      _lowerCAmelCase       :   Optional[int]  =    (predicted_variance + 1) / 2
      _lowerCAmelCase       :   int  =    frac * max_log + (1 - frac) * min_log

    return variance

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A = None    ,_A = True    ,):
    '''simple docstring'''

    _lowerCAmelCase       :   Union[str, Any]  =    timestep

    if key is None:
      _lowerCAmelCase       :   Any  =    jax.random.PRNGKey(0      )

    if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
      _lowerCAmelCase       :   Any  =    jnp.split(__lowerCamelCase    ,sample.shape[1]    ,axis=1      )
    else:
      _lowerCAmelCase       :   Tuple  =    None

    # 1. compute alphas, betas
    _lowerCAmelCase       :   Dict  =    state.common.alphas_cumprod[t]
    _lowerCAmelCase       :   Tuple  =    jnp.where(t > 0    ,state.common.alphas_cumprod[t - 1]    ,jnp.array(1.0    ,dtype=self.dtype      )      )
    _lowerCAmelCase       :   Any  =    1 - alpha_prod_t
    _lowerCAmelCase       :   str  =    1 - alpha_prod_t_prev

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if self.config.prediction_type == "epsilon":
      _lowerCAmelCase       :   Optional[int]  =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif self.config.prediction_type == "sample":
      _lowerCAmelCase       :   Union[str, Any]  =    model_output
    elif self.config.prediction_type == "v_prediction":
      _lowerCAmelCase       :   Dict  =    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
    else:
      raise ValueError(
          F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
          ' for the FlaxDDPMScheduler.'      )

    # 3. Clip "predicted x_0"
    if self.config.clip_sample:
      _lowerCAmelCase       :   Dict  =    jnp.clip(__lowerCamelCase    ,-1    ,1      )

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    _lowerCAmelCase       :   Optional[int]  =    (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
    _lowerCAmelCase       :   Dict  =    state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    _lowerCAmelCase       :   List[str]  =    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    def random_variance():
      _lowerCAmelCase       :   str  =    jax.random.split(__lowerCamelCase    ,num=1      )
      _lowerCAmelCase       :   Any  =    jax.random.normal(__lowerCamelCase    ,shape=model_output.shape    ,dtype=self.dtype      )
      return (self._get_variance(__lowerCamelCase    ,__lowerCamelCase    ,predicted_variance=__lowerCamelCase      ) ** 0.5) * noise

    _lowerCAmelCase       :   List[Any]  =    jnp.where(t > 0    ,random_variance()    ,jnp.zeros(model_output.shape    ,dtype=self.dtype      )      )

    _lowerCAmelCase       :   List[Any]  =    pred_prev_sample + variance

    if not return_dict:
      return (pred_prev_sample, state)

    return FlaxDDPMSchedulerOutput(prev_sample=__lowerCamelCase    ,state=__lowerCamelCase      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,):
    '''simple docstring'''

    return add_noise_common(state.common    ,__lowerCamelCase    ,__lowerCamelCase    ,__lowerCamelCase      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,):
    '''simple docstring'''

    return get_velocity_common(state.common    ,__lowerCamelCase    ,__lowerCamelCase    ,__lowerCamelCase      )







  def __len__(		self      ):
    '''simple docstring'''

    return self.config.num_train_timesteps








"""simple docstring"""






from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



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







class 	__UpperCamelCase    :

  def __init__(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    F"""https://www.instagram.com/{username}/"""
      _lowerCAmelCase       :   str  =    self.get_json()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    requests.get(self.url    ,headers=_A      ).text
      _lowerCAmelCase       :   Optional[Any]  =    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 __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["username"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["full_name"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["biography"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["business_email"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["external_url"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["profile_pic_url_hd"]

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["is_verified"]







  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.user_data["is_private"]





def 		lowerCamelCase__		(					_lowerCamelCase = "github"   ):

    '''simple docstring'''



    import os

    if os.environ.get('CI'   ):
        return  # test failing on GitHub Actions
    _lowerCAmelCase       :   Tuple  =    InstagramUser(_lowerCamelCase   )
    assert instagram_user.user_data
    assert isinstance(instagram_user.user_data ,   _lowerCamelCase   )
    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						=		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 = }''')





