



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE  					=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE  					=    '▁'

SCREAMING_SNAKE_CASE  					=    {'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE  					=    {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

SCREAMING_SNAKE_CASE  					=    {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}



class 						lowerCamelCase   (    __snake_case	):







					'''simple docstring'''







					lowerCAmelCase_					:     List[Any]					       =	VOCAB_FILES_NAMES
					lowerCAmelCase_					:     Optional[int]					       =	PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_					:     Optional[int]					       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_					:     Optional[int]					       =	['input_ids', 'attention_mask']



					def __init__(      self     ,						lowerCAmelCase     ,						lowerCAmelCase="<s>"     ,						lowerCAmelCase="</s>"     ,						lowerCAmelCase="</s>"     ,						lowerCAmelCase="<s>"     ,						lowerCAmelCase="<unk>"     ,						lowerCAmelCase="<pad>"     ,						lowerCAmelCase="<mask>"     ,						lowerCAmelCase = None     ,						**lowerCAmelCase     ,						):
							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_						       =					AddedToken(__lowerCamelCase     ,						lstrip=__lowerCamelCase     ,						rstrip=__lowerCamelCase  ) if isinstance(__lowerCamelCase     ,						__lowerCamelCase  ) else mask_token

							UpperCAmelCase_						       =					{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__lowerCamelCase     ,						eos_token=__lowerCamelCase     ,						unk_token=__lowerCamelCase     ,						sep_token=__lowerCamelCase     ,						cls_token=__lowerCamelCase     ,						pad_token=__lowerCamelCase     ,						mask_token=__lowerCamelCase     ,						sp_model_kwargs=self.sp_model_kwargs     ,						**__lowerCamelCase     ,						)

							UpperCAmelCase_						       =					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(str(__lowerCamelCase  )  )
							UpperCAmelCase_						       =					vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							UpperCAmelCase_						       =					{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							UpperCAmelCase_						       =					1

							UpperCAmelCase_						       =					len(self.sp_model  ) + self.fairseq_offset
							UpperCAmelCase_						       =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}



					def __getstate__(      self  ):
							UpperCAmelCase_						       =					self.__dict__.copy()
							UpperCAmelCase_						       =					None
							UpperCAmelCase_						       =					self.sp_model.serialized_model_proto()
							return state



					def __setstate__(      self     ,						lowerCAmelCase  ):
							UpperCAmelCase_						       =					d

							# for backward compatibility
							if not hasattr(self     ,						"sp_model_kwargs"  ):
									UpperCAmelCase_						       =					{}

							UpperCAmelCase_						       =					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase = None  ):

							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCAmelCase_						       =					[self.cls_token_id]
							UpperCAmelCase_						       =					[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase = None     ,						lowerCAmelCase = False  ):

							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=__lowerCamelCase     ,						token_ids_a=__lowerCamelCase     ,						already_has_special_tokens=__lowerCamelCase  )

							if token_ids_a is None:
									return [1] + ([0] * len(__lowerCamelCase  )) + [1]
							return [1] + ([0] * len(__lowerCamelCase  )) + [1, 1] + ([0] * len(__lowerCamelCase  )) + [1]



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase = None  ):
							UpperCAmelCase_						       =					[self.sep_token_id]
							UpperCAmelCase_						       =					[self.cls_token_id]

							if token_ids_a is None:
									return len(cls + token_ids_a + sep  ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]



					@property
					def  A__		(      self  ):
							return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token



					def  A__		(      self  ):
							UpperCAmelCase_						       =					{self.convert_ids_to_tokens(__lowerCamelCase  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab



					def  A__		(      self     ,						lowerCAmelCase  ):
							return self.sp_model.encode(__lowerCamelCase     ,						out_type=__lowerCamelCase  )



					def  A__		(      self     ,						lowerCAmelCase  ):
							if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
							UpperCAmelCase_						       =					self.sp_model.PieceToId(__lowerCamelCase  )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



					def  A__		(      self     ,						lowerCAmelCase  ):
							if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset  )



					def  A__		(      self     ,						lowerCAmelCase  ):
							UpperCAmelCase_						       =					"".join(__lowerCamelCase  ).replace(__lowerCamelCase     ,						" "  ).strip()
							return out_string



					def  A__		(      self     ,						lowerCAmelCase     ,						lowerCAmelCase = None  ):
							if not os.path.isdir(__lowerCamelCase  ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
									return
							UpperCAmelCase_						       =					os.path.join(
							    __lowerCamelCase     ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

							if os.path.abspath(self.vocab_file  ) != os.path.abspath(__lowerCamelCase  ) and os.path.isfile(self.vocab_file  ):
									copyfile(self.vocab_file     ,						__lowerCamelCase  )
							elif not os.path.isfile(self.vocab_file  ):
									with open(__lowerCamelCase     ,						"wb"  ) as fi:
											UpperCAmelCase_						       =					self.sp_model.serialized_model_proto()
											fi.write(__lowerCamelCase  )

							return (out_vocab_file,)




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 = }')