

import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
    import torch





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "char"
   lowerCamelCase__				=      "bpe"
   lowerCamelCase__				=      "wp"




__A			:					Optional[Any]						=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      ["image_processor", "char_tokenizer"]
   lowerCamelCase__				=      "ViTImageProcessor"
   lowerCamelCase__				=      "MgpstrTokenizer"





   def __init__(       self					:			List[Any]     , __lowerCamelCase					:			Dict=None     , __lowerCamelCase					:			Union[str, Any]=None     , **__lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."     , __lowerCamelCase     , )
           SCREAMING_SNAKE_CASE										=   kwargs.pop("feature_extractor"			)

       SCREAMING_SNAKE_CASE										=   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."			)
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."			)

       SCREAMING_SNAKE_CASE										=   tokenizer
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained("gpt2"			)
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained("bert-base-uncased"			)

       super().__init__(__lowerCamelCase     , __lowerCamelCase			)





   def __call__(       self					:			int     , __lowerCamelCase					:			Optional[int]=None     , __lowerCamelCase					:			Tuple=None     , __lowerCamelCase					:			Any=None     , **__lowerCamelCase					:			str			):
       if images is None and text is None:
           raise ValueError("You need to specify either an `images` or `text` input to process."			)

       if images is not None:
           SCREAMING_SNAKE_CASE										=   self.image_processor(__lowerCamelCase     , return_tensors=__lowerCamelCase     , **__lowerCamelCase			)
       if text is not None:
           SCREAMING_SNAKE_CASE										=   self.char_tokenizer(__lowerCamelCase     , return_tensors=__lowerCamelCase     , **__lowerCamelCase			)

       if text is None:
           return inputs
       elif images is None:
           return encodings
       else:
           SCREAMING_SNAKE_CASE										=   encodings["input_ids"]
           return inputs





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   sequences
       SCREAMING_SNAKE_CASE										=   char_preds.size(0			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._decode_helper(__lowerCamelCase     , "char"			)
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._decode_helper(__lowerCamelCase     , "bpe"			)
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   self._decode_helper(__lowerCamelCase     , "wp"			)

       SCREAMING_SNAKE_CASE										=   []
       SCREAMING_SNAKE_CASE										=   []
       for i in range(__lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   [char_scores[i], bpe_scores[i], wp_scores[i]]
           SCREAMING_SNAKE_CASE										=   [char_strs[i], bpe_strs[i], wp_strs[i]]
           SCREAMING_SNAKE_CASE										=   scores.index(max(__lowerCamelCase			)			)
           final_strs.append(strs[max_score_index]			)
           final_scores.append(scores[max_score_index]			)

       SCREAMING_SNAKE_CASE										=   {}
       SCREAMING_SNAKE_CASE										=   final_strs
       SCREAMING_SNAKE_CASE										=   final_scores
       SCREAMING_SNAKE_CASE										=   char_strs
       SCREAMING_SNAKE_CASE										=   bpe_strs
       SCREAMING_SNAKE_CASE										=   wp_strs
       return out





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Tuple     , __lowerCamelCase					:			List[str]			):
       if format == DecodeType.CHARACTER:
           SCREAMING_SNAKE_CASE										=   self.char_decode
           SCREAMING_SNAKE_CASE										=   1
           SCREAMING_SNAKE_CASE										=   "[s]"
       elif format == DecodeType.BPE:
           SCREAMING_SNAKE_CASE										=   self.bpe_decode
           SCREAMING_SNAKE_CASE										=   2
           SCREAMING_SNAKE_CASE										=   "#"
       elif format == DecodeType.WORDPIECE:
           SCREAMING_SNAKE_CASE										=   self.wp_decode
           SCREAMING_SNAKE_CASE										=   102
           SCREAMING_SNAKE_CASE										=   "[SEP]"
       else:
           raise ValueError(f"Format {format} is not supported."			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   [], []
       SCREAMING_SNAKE_CASE										=   pred_logits.size(0			)
       SCREAMING_SNAKE_CASE										=   pred_logits.size(1			)
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   pred_logits.topk(1     , dim=-1     , largest=__lowerCamelCase     , sorted=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   preds_index.view(-1     , __lowerCamelCase			)[:, 1:]
       SCREAMING_SNAKE_CASE										=   decoder(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   torch.nn.functional.softmax(__lowerCamelCase     , dim=2			).max(dim=2			)
       SCREAMING_SNAKE_CASE										=   preds_max_prob[:, 1:]

       for index in range(__lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   preds_str[index].find(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   preds_str[index][:pred_eos]
           SCREAMING_SNAKE_CASE										=   preds_index[index].cpu().tolist()
           SCREAMING_SNAKE_CASE										=   pred_index.index(__lowerCamelCase			) if eos_token in pred_index else -1
           SCREAMING_SNAKE_CASE										=   preds_max_prob[index][: pred_eos_index + 1]
           SCREAMING_SNAKE_CASE										=   pred_max_prob.cumprod(dim=0			)[-1] if pred_max_prob.nelement() != 0 else 0.0
           dec_strs.append(__lowerCamelCase			)
           conf_scores.append(__lowerCamelCase			)

       return dec_strs, conf_scores





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   [seq.replace(" "     , ""			) for seq in self.char_tokenizer.batch_decode(__lowerCamelCase			)]
       return decode_strs





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			Optional[int]			):
       return self.bpe_tokenizer.batch_decode(__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   [seq.replace(" "     , ""			) for seq in self.wp_tokenizer.batch_decode(__lowerCamelCase			)]
       return decode_strs


from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

__A			:					List[Any]						=			{'UserAgent': UserAgent().random}





def 	__a     (      A__			:  List[Any]       ):
    SCREAMING_SNAKE_CASE										=   script.contents[0]
    SCREAMING_SNAKE_CASE										=   json.loads(data[data.find("{\"config\""       ) : -1]       )
    return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			List[str]     , __lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   f"https://www.instagram.com/{username}/"
       SCREAMING_SNAKE_CASE										=   self.get_json()





   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   requests.get(self.url     , headers=__lowerCamelCase			).text
       SCREAMING_SNAKE_CASE										=   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					:			Union[str, Any]			):
       return f"{self.__class__.__name__}('{self.username}')"





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





   @property
   def      _snake_case		(       self					:			Optional[int]			):
       return self.user_data["username"]





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





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





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





   @property
   def      _snake_case		(       self					:			Optional[Any]			):
       return self.user_data["external_url"]





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





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





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





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





   @property
   def      _snake_case		(       self					:			Optional[int]			):
       return self.user_data["is_verified"]





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





def 	__a     (      A__			:  str = "github"       ):
    import os

    if os.environ.get("CI"       ):
        return  # test failing on GitHub Actions
    SCREAMING_SNAKE_CASE										=   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 > 120000
    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			:					Dict						=			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 = }')