'''simple docstring'''

from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowerCAmelCase_ : Dict						  =      {"UserAgent": UserAgent().random}


def __a (	__lowerCamelCase			:						List[Any]				)	->							dict:
      '''simple docstring'''


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





class 							lowercase						:


   def __init__(   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	Optional[Any])      ->					Tuple:
         lowercase_     =      F'https://www.instagram.com/{username}/'
         lowercase_     =      self.get_json()


   def __UpperCAmelCase       (   self				:	Tuple)      ->					dict:
         lowercase_     =      requests.get(self.url   ,		headers=__lowerCAmelCase).text
         lowercase_     =      BeautifulSoup(__lowerCAmelCase   ,		"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				:	Dict)      ->					str:
         return F'{self.__class__.__name__}(\'{self.username}\')'


   def __str__(   self				:	List[Any])      ->					str:
         return F'{self.fullname} ({self.username}) is {self.biography}'


   @property
   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					str:
         return self.user_data["username"]


   @property
   def __UpperCAmelCase       (   self				:	Optional[int])      ->					str:
         return self.user_data["full_name"]


   @property
   def __UpperCAmelCase       (   self				:	str)      ->					str:
         return self.user_data["biography"]


   @property
   def __UpperCAmelCase       (   self				:	Tuple)      ->					str:
         return self.user_data["business_email"]


   @property
   def __UpperCAmelCase       (   self				:	str)      ->					str:
         return self.user_data["external_url"]


   @property
   def __UpperCAmelCase       (   self				:	Any)      ->					int:
         return self.user_data["edge_followed_by"]["count"]


   @property
   def __UpperCAmelCase       (   self				:	str)      ->					int:
         return self.user_data["edge_follow"]["count"]


   @property
   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					int:
         return self.user_data["edge_owner_to_timeline_media"]["count"]


   @property
   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					str:
         return self.user_data["profile_pic_url_hd"]


   @property
   def __UpperCAmelCase       (   self				:	Dict)      ->					bool:
         return self.user_data["is_verified"]


   @property
   def __UpperCAmelCase       (   self				:	Optional[int])      ->					bool:
         return self.user_data["is_private"]




def __a (	__lowerCamelCase			:						str = "github"				)	->							None:
      '''simple docstring'''


      import os

      if os.environ.get("CI"				):
            return  # test failing on GitHub Actions
      lowercase_     =      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 > 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_ : 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 = }''')







'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCAmelCase_ : str						  =      {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Union[str, Any]						  =      [
           "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Wav2Vec2ForAudioFrameClassification",
           "Wav2Vec2ForCTC",
           "Wav2Vec2ForMaskedLM",
           "Wav2Vec2ForPreTraining",
           "Wav2Vec2ForSequenceClassification",
           "Wav2Vec2ForXVector",
           "Wav2Vec2Model",
           "Wav2Vec2PreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : int						  =      [
           "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFWav2Vec2ForCTC",
           "TFWav2Vec2Model",
           "TFWav2Vec2PreTrainedModel",
           "TFWav2Vec2ForSequenceClassification",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Dict						  =      [
           "FlaxWav2Vec2ForCTC",
           "FlaxWav2Vec2ForPreTraining",
           "FlaxWav2Vec2Model",
           "FlaxWav2Vec2PreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
       from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
       from .processing_wavaveca import WavaVecaProcessor
       from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavaveca import (
                  WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavaVecaForAudioFrameClassification,
                  WavaVecaForCTC,
                  WavaVecaForMaskedLM,
                  WavaVecaForPreTraining,
                  WavaVecaForSequenceClassification,
                  WavaVecaForXVector,
                  WavaVecaModel,
                  WavaVecaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFWavaVecaForCTC,
                  TFWavaVecaForSequenceClassification,
                  TFWavaVecaModel,
                  TFWavaVecaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  FlaxWavaVecaForCTC,
                  FlaxWavaVecaForPreTraining,
                  FlaxWavaVecaModel,
                  FlaxWavaVecaPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_ : Optional[int]						  =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






