
from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowerCamelCase_						     =					{'''UserAgent''': UserAgent().random}






def 						__magic_name__			(   __a      :	Union[str, Any]						):



     '''simple docstring'''




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




class      __A:

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    F"https://www.instagram.com/{username}/"
       UpperCamelCase__				=    self.get_json()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    requests.get(self.url	,      headers=SCREAMING_SNAKE_CASE_						).text
       UpperCamelCase__				=    BeautifulSoup(SCREAMING_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						):
       return F"{self.__class__.__name__}('{self.username}')"







  def __str__(self						):
       return F"{self.fullname} ({self.username}) is {self.biography}"







  @property
  def 	UpperCAmelCase_ (self						):
       return self.user_data["username"]







  @property
  def 	UpperCAmelCase_ (self						):
       return self.user_data["full_name"]







  @property
  def 	UpperCAmelCase_ (self						):
       return self.user_data["biography"]







  @property
  def 	UpperCAmelCase_ (self						):
       return self.user_data["business_email"]







  @property
  def 	UpperCAmelCase_ (self						):
       return self.user_data["external_url"]







  @property
  def 	UpperCAmelCase_ (self						):
       return self.user_data["edge_followed_by"]["count"]







  @property
  def 	UpperCAmelCase_ (self						):
       return self.user_data["edge_follow"]["count"]







  @property
  def 	UpperCAmelCase_ (self						):
       return self.user_data["edge_owner_to_timeline_media"]["count"]







  @property
  def 	UpperCAmelCase_ (self						):
       return self.user_data["profile_pic_url_hd"]







  @property
  def 	UpperCAmelCase_ (self						):
       return self.user_data["is_verified"]







  @property
  def 	UpperCAmelCase_ (self						):
       return self.user_data["is_private"]






def 						__magic_name__			(   __a      :	str = "github"						):



     '''simple docstring'''




     import os

     if os.environ.get("""CI"""						):
          return  # test failing on GitHub Actions
     UpperCamelCase__				=    InstagramUser(__a						)
     assert instagram_user.user_data
     assert isinstance(instagram_user.user_data	,		__a						)
     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 > 120_000
     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 = }')







import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_						     =					logging.get_logger(__name__)






def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    R"""\w+[.]\d+"""
     UpperCamelCase__				=    re.findall(__a	,		__a						)
     for pat in pats:
          UpperCamelCase__				=    key.replace(__a	,		"""_""".join(pat.split("""."""						)						)						)
     return key






def 						__magic_name__			(   __a      :	str	,		__a      :	Dict	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
     if (
         any("""norm""" in str_ for str_ in pt_tuple_key						)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
          return renamed_pt_tuple_key, pt_tensor
     elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""scale""",)
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
          UpperCamelCase__				=    pt_tuple_key[:-1] + ("""embedding""",)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""kernel""",)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          UpperCamelCase__				=    pt_tensor.transpose(2	,		3	,		1	,		0						)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""kernel""",)
     if pt_tuple_key[-1] == "weight":
          UpperCamelCase__				=    pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""weight""",)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     UpperCamelCase__				=    pt_tuple_key[:-1] + ("""bias""",)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor






def 						__magic_name__			(   __a      :	List[Any]	,		__a      :	List[Any]	,		__a      :	Optional[int]=42						):



     '''simple docstring'''




     UpperCamelCase__				=    {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     UpperCamelCase__				=    flax_model.init_weights(PRNGKey(__a						)						)

     UpperCamelCase__				=    flatten_dict(__a						)
     UpperCamelCase__				=    {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          UpperCamelCase__				=    rename_key(__a						)
          UpperCamelCase__				=    tuple(renamed_pt_key.split("""."""						)						)

          # Correctly rename weight parameters
          UpperCamelCase__		,	UpperCamelCase__				=    rename_key_and_reshape_tensor(__a	,		__a	,		__a						)

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
                        f"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."						)

        # also add unexpected weight so that warning is thrown
          UpperCamelCase__				=    jnp.asarray(__a						)

     return unflatten_dict(__a						)





