






'''simple docstring'''
from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




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


class 							_UpperCAmelCase    :




    def __init__(      self	:							Union[str, Any]	,  a	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  f"""https://www.instagram.com/{username}/"""
       lowercase_						:     List[str]							     =  self.get_json()




    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     str							     =  requests.get(self.url	,  headers=a      ).text
       lowercase_						:     Union[str, 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	:							Tuple      ):






       '''simple docstring'''






       return f"""{self.__class__.__name__}('{self.username}')"""




    def __str__(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return f"""{self.fullname} ({self.username}) is {self.biography}"""




    @property
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       return self.user_data["username"]




    @property
    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       return self.user_data["full_name"]




    @property
    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       return self.user_data["biography"]




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       return self.user_data["business_email"]




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       return self.user_data["external_url"]




    @property
    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






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




    @property
    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






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




    @property
    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






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




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       return self.user_data["profile_pic_url_hd"]




    @property
    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






       return self.user_data["is_verified"]





    @property
    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       return self.user_data["is_private"]




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase = "github"				):


   """simple docstring"""




   import os

   if os.environ.get("CI"				):
      return  # test failing on GitHub Actions
   lowercase_						:     int							     =  InstagramUser(_UpperCamelCase				)
   assert instagram_user.user_data
   assert isinstance(instagram_user.user_data    ,		_UpperCamelCase				)
   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 > 12_0000
   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()
 UpperCamelCase__			=      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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__			=      {
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      ['FunnelTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'FunnelBaseModel',
     'FunnelForMaskedLM',
     'FunnelForMultipleChoice',
     'FunnelForPreTraining',
     'FunnelForQuestionAnswering',
     'FunnelForSequenceClassification',
     'FunnelForTokenClassification',
     'FunnelModel',
     'FunnelPreTrainedModel',
     'load_tf_weights_in_funnel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__			=      [
     'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFFunnelBaseModel',
     'TFFunnelForMaskedLM',
     'TFFunnelForMultipleChoice',
     'TFFunnelForPreTraining',
     'TFFunnelForQuestionAnswering',
     'TFFunnelForSequenceClassification',
     'TFFunnelForTokenClassification',
     'TFFunnelModel',
     'TFFunnelPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
 from .tokenization_funnel import FunnelTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_funnel_fast import FunnelTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_funnel import (
      FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
      FunnelBaseModel,
      FunnelForMaskedLM,
      FunnelForMultipleChoice,
      FunnelForPreTraining,
      FunnelForQuestionAnswering,
      FunnelForSequenceClassification,
      FunnelForTokenClassification,
      FunnelModel,
      FunnelPreTrainedModel,
      load_tf_weights_in_funnel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_funnel import (
      TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFunnelBaseModel,
      TFFunnelForMaskedLM,
      TFFunnelForMultipleChoice,
      TFFunnelForPreTraining,
      TFFunnelForQuestionAnswering,
      TFFunnelForSequenceClassification,
      TFFunnelForTokenClassification,
      TFFunnelModel,
      TFFunnelPreTrainedModel,
  )

else:
 import sys

 UpperCamelCase__			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




