





"""simple docstring"""





from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

__A											=		{"""UserAgent""": UserAgent().random}
def  __A   (_SCREAMING_SNAKE_CASE			)					->dict:





  """simple docstring"""


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






class 					_lowerCAmelCase   :
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							requests.get(self.url    , headers=__UpperCAmelCase      ).text
         lowerCAmelCase__			:Dict											=							BeautifulSoup(__UpperCAmelCase    , '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  __A   (_SCREAMING_SNAKE_CASE = "github"			)					->None:





  """simple docstring"""


  import os

  if os.environ.get('CI'			):
    return  # test failing on GitHub Actions
  lowerCAmelCase__			:Dict											=							InstagramUser(_SCREAMING_SNAKE_CASE			)
  assert instagram_user.user_data
  assert isinstance(instagram_user.user_data     ,   _SCREAMING_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 > 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()
  __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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__A											=		{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecAudioForAudioFrameClassification""",
      """Data2VecAudioForCTC""",
      """Data2VecAudioForSequenceClassification""",
      """Data2VecAudioForXVector""",
      """Data2VecAudioModel""",
      """Data2VecAudioPreTrainedModel""",
  ]
  __A											=		[
      """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecTextForCausalLM""",
      """Data2VecTextForMaskedLM""",
      """Data2VecTextForMultipleChoice""",
      """Data2VecTextForQuestionAnswering""",
      """Data2VecTextForSequenceClassification""",
      """Data2VecTextForTokenClassification""",
      """Data2VecTextModel""",
      """Data2VecTextPreTrainedModel""",
  ]
  __A											=		[
      """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Data2VecVisionForImageClassification""",
      """Data2VecVisionForMaskedImageModeling""",
      """Data2VecVisionForSemanticSegmentation""",
      """Data2VecVisionModel""",
      """Data2VecVisionPreTrainedModel""",
  ]

if is_tf_available():
  __A											=		[
      """TFData2VecVisionForImageClassification""",
      """TFData2VecVisionForSemanticSegmentation""",
      """TFData2VecVisionModel""",
      """TFData2VecVisionPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
  from .configuration_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DataaVecTextConfig,
      DataaVecTextOnnxConfig,
  )
  from .configuration_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
      DataaVecVisionConfig,
      DataaVecVisionOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dataavec_audio import (
        DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecAudioForAudioFrameClassification,
        DataaVecAudioForCTC,
        DataaVecAudioForSequenceClassification,
        DataaVecAudioForXVector,
        DataaVecAudioModel,
        DataaVecAudioPreTrainedModel,
    )
    from .modeling_dataavec_text import (
        DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecTextForCausalLM,
        DataaVecTextForMaskedLM,
        DataaVecTextForMultipleChoice,
        DataaVecTextForQuestionAnswering,
        DataaVecTextForSequenceClassification,
        DataaVecTextForTokenClassification,
        DataaVecTextModel,
        DataaVecTextPreTrainedModel,
    )
    from .modeling_dataavec_vision import (
        DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
        DataaVecVisionForImageClassification,
        DataaVecVisionForMaskedImageModeling,
        DataaVecVisionForSemanticSegmentation,
        DataaVecVisionModel,
        DataaVecVisionPreTrainedModel,
    )
  if is_tf_available():
    from .modeling_tf_dataavec_vision import (
        TFDataaVecVisionForImageClassification,
        TFDataaVecVisionForSemanticSegmentation,
        TFDataaVecVisionModel,
        TFDataaVecVisionPreTrainedModel,
    )

else:
  import sys

  __A											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
