"""simple docstring"""


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	List[Any]  				=				['image_processor', 'tokenizer']
    _snake_case  :	Tuple  				=				'BlipImageProcessor'
    _snake_case  :	Optional[int]  				=				'AutoTokenizer'


    def __init__(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Optional[Any]       ):
      super().__init__(__lowerCAmelCase ,					__lowerCAmelCase       )

      # add QFormer tokenizer
      _UpperCAmelCase    			=   qformer_tokenizer


    def __call__(       self						:	List[Any] ,					__lowerCAmelCase						:	ImageInput = None ,					__lowerCAmelCase						:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,					__lowerCAmelCase						:	bool = True ,					__lowerCAmelCase						:	Union[bool, str, PaddingStrategy] = False ,					__lowerCAmelCase						:	Union[bool, str, TruncationStrategy] = None ,					__lowerCAmelCase						:	Optional[int] = None ,					__lowerCAmelCase						:	int = 0 ,					__lowerCAmelCase						:	Optional[int] = None ,					__lowerCAmelCase						:	Optional[bool] = None ,					__lowerCAmelCase						:	bool = False ,					__lowerCAmelCase						:	bool = False ,					__lowerCAmelCase						:	bool = False ,					__lowerCAmelCase						:	bool = False ,					__lowerCAmelCase						:	bool = False ,					__lowerCAmelCase						:	bool = True ,					__lowerCAmelCase						:	Optional[Union[str, TensorType]] = None ,					**__lowerCAmelCase						:	Dict ,					):
      if images is None and text is None:
        raise ValueError("""You have to specify at least images or text."""       )

      _UpperCAmelCase    			=   BatchFeature()

      if text is not None:
        _UpperCAmelCase    			=   self.tokenizer(
            text=__lowerCAmelCase ,					add_special_tokens=__lowerCAmelCase ,					padding=__lowerCAmelCase ,					truncation=__lowerCAmelCase ,					max_length=__lowerCAmelCase ,					stride=__lowerCAmelCase ,					pad_to_multiple_of=__lowerCAmelCase ,					return_attention_mask=__lowerCAmelCase ,					return_overflowing_tokens=__lowerCAmelCase ,					return_special_tokens_mask=__lowerCAmelCase ,					return_offsets_mapping=__lowerCAmelCase ,					return_token_type_ids=__lowerCAmelCase ,					return_length=__lowerCAmelCase ,					verbose=__lowerCAmelCase ,					return_tensors=__lowerCAmelCase ,					**__lowerCAmelCase ,					)
        encoding.update(__lowerCAmelCase       )
        _UpperCAmelCase    			=   self.qformer_tokenizer(
            text=__lowerCAmelCase ,					add_special_tokens=__lowerCAmelCase ,					padding=__lowerCAmelCase ,					truncation=__lowerCAmelCase ,					max_length=__lowerCAmelCase ,					stride=__lowerCAmelCase ,					pad_to_multiple_of=__lowerCAmelCase ,					return_attention_mask=__lowerCAmelCase ,					return_overflowing_tokens=__lowerCAmelCase ,					return_special_tokens_mask=__lowerCAmelCase ,					return_offsets_mapping=__lowerCAmelCase ,					return_token_type_ids=__lowerCAmelCase ,					return_length=__lowerCAmelCase ,					verbose=__lowerCAmelCase ,					return_tensors=__lowerCAmelCase ,					**__lowerCAmelCase ,					)
        _UpperCAmelCase    			=   qformer_text_encoding.pop("""input_ids"""       )
        _UpperCAmelCase    			=   qformer_text_encoding.pop("""attention_mask"""       )

      if images is not None:
        _UpperCAmelCase    			=   self.image_processor(__lowerCAmelCase ,					return_tensors=__lowerCAmelCase       )
        encoding.update(__lowerCAmelCase       )

      return encoding


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					*__lowerCAmelCase						:	Optional[Any] ,					**__lowerCAmelCase						:	List[Any]       ):
      return self.tokenizer.batch_decode(*__lowerCAmelCase ,					**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	int ,					*__lowerCAmelCase						:	Dict ,					**__lowerCAmelCase						:	Optional[Any]       ):
      return self.tokenizer.decode(*__lowerCAmelCase ,					**__lowerCAmelCase       )


    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   self.tokenizer.model_input_names
      _UpperCAmelCase    			=   self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )


    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	Dict ,					**__lowerCAmelCase						:	Dict       ):
      if os.path.isfile(__lowerCAmelCase       ):
        raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''       )
      os.makedirs(__lowerCAmelCase ,					exist_ok=__lowerCAmelCase       )
      _UpperCAmelCase    			=   os.path.join(__lowerCAmelCase ,					"""qformer_tokenizer"""       )
      self.qformer_tokenizer.save_pretrained(__lowerCAmelCase       )
      return super().save_pretrained(__lowerCAmelCase ,					**__lowerCAmelCase       )


    @classmethod
    def   lowerCAmelCase_	(       cls						:	Any ,					__lowerCAmelCase						:	Any ,					**__lowerCAmelCase						:	Optional[Any]       ):
      _UpperCAmelCase    			=   AutoTokenizer.from_pretrained(__lowerCAmelCase ,					subfolder="""qformer_tokenizer"""       )
      _UpperCAmelCase    			=   cls._get_arguments_from_pretrained(__lowerCAmelCase ,					**__lowerCAmelCase       )
      args.append(__lowerCAmelCase       )
      return cls(*__lowerCAmelCase       )

"""simple docstring"""


from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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






def      __UpperCAmelCase       (      lowercase		):







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


class 							a					:


    def __init__(       self						:	Any ,					__lowerCAmelCase						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   f'''https://www.instagram.com/{username}/'''
      _UpperCAmelCase    			=   self.get_json()


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   requests.get(self.url ,					headers=__lowerCAmelCase       ).text
      _UpperCAmelCase    			=   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       ):
      return f'''{self.__class__.__name__}(\'{self.username}\')'''


    def __str__(       self						:	int       ):
      return f'''{self.fullname} ({self.username}) is {self.biography}'''


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


    @property
    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      return self.user_data["full_name"]


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


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


    @property
    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      return self.user_data["external_url"]


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


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


    @property
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      return self.user_data["edge_owner_to_timeline_media"]["count"]


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


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


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






def      __UpperCAmelCase       (      lowercase = "github"		):







  """simple docstring"""
  import os

  if os.environ.get("""CI"""		):
    return  # test failing on GitHub Actions
  _UpperCAmelCase    			=   InstagramUser(lowercase		)
  assert instagram_user.user_data
  assert isinstance(instagram_user.user_data ,lowercase		)
  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 > 1_50
  assert instagram_user.number_of_followers > 12_00_00
  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 = }''')
