

import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__A			:					Tuple						=			logging.get_logger(__name__)


__A			:					Optional[int]						=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all LED models at https://huggingface.co/models?filter=LED
__A			:					List[str]						=			{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

__A			:					int						=			{
    'allenai/led-base-16384': 1_6_3_8_4,
}





@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   (
        list(range(ord("!"       )			,  ord("~"       ) + 1       )       ) + list(range(ord("¡"       )			,  ord("¬"       ) + 1       )       ) + list(range(ord("®"       )			,  ord("ÿ"       ) + 1       )       )
    )
    SCREAMING_SNAKE_CASE										=   bs[:]
    SCREAMING_SNAKE_CASE										=   0
    for b in range(2**8       ):
        if b not in bs:
            bs.append(A__       )
            cs.append(2**8 + n       )
            n += 1
    SCREAMING_SNAKE_CASE										=   [chr(A__       ) for n in cs]
    return dict(zip(A__			,  A__       )       )





def 	__a     (      A__			:  str       ):
    SCREAMING_SNAKE_CASE										=   set()
    SCREAMING_SNAKE_CASE										=   word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)       )
        SCREAMING_SNAKE_CASE										=   char
    return pairs





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      VOCAB_FILES_NAMES
   lowerCamelCase__				=      PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__				=      ["input_ids", "attention_mask"]





   def __init__(       self					:			Tuple     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			List[Any]="replace"     , __lowerCamelCase					:			int="<s>"     , __lowerCamelCase					:			Any="</s>"     , __lowerCamelCase					:			Optional[Any]="</s>"     , __lowerCamelCase					:			Optional[int]="<s>"     , __lowerCamelCase					:			str="<unk>"     , __lowerCamelCase					:			List[str]="<pad>"     , __lowerCamelCase					:			List[Any]="<mask>"     , __lowerCamelCase					:			Tuple=False     , **__lowerCamelCase					:			Union[str, Any]     , ):
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else bos_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else eos_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else sep_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else cls_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else unk_token
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       SCREAMING_SNAKE_CASE										=   AddedToken(__lowerCamelCase     , lstrip=__lowerCamelCase     , rstrip=__lowerCamelCase			) if isinstance(__lowerCamelCase     , __lowerCamelCase			) else mask_token

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

       with open(__lowerCamelCase     , encoding="utf-8"			) as vocab_handle:
           SCREAMING_SNAKE_CASE										=   json.load(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   {v: k for k, v in self.encoder.items()}
       SCREAMING_SNAKE_CASE										=   errors  # how to handle errors in decoding
       SCREAMING_SNAKE_CASE										=   bytes_to_unicode()
       SCREAMING_SNAKE_CASE										=   {v: k for k, v in self.byte_encoder.items()}
       with open(__lowerCamelCase     , encoding="utf-8"			) as merges_handle:
           SCREAMING_SNAKE_CASE										=   merges_handle.read().split("\n"			)[1:-1]
       SCREAMING_SNAKE_CASE										=   [tuple(merge.split()			) for merge in bpe_merges]
       SCREAMING_SNAKE_CASE										=   dict(zip(__lowerCamelCase     , range(len(__lowerCamelCase			)			)			)			)
       SCREAMING_SNAKE_CASE										=   {}
       SCREAMING_SNAKE_CASE										=   add_prefix_space

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       SCREAMING_SNAKE_CASE										=   re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"			)





   @property
   # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
   def      _snake_case		(       self					:			int			):
       return len(self.encoder			)





   def      _snake_case		(       self					:			int			):
       return dict(self.encoder     , **self.added_tokens_encoder			)





   def      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			List[str]			):
       if token in self.cache:
           return self.cache[token]
       SCREAMING_SNAKE_CASE										=   tuple(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   get_pairs(__lowerCamelCase			)

       if not pairs:
           return token

       while True:
           SCREAMING_SNAKE_CASE										=   min(__lowerCamelCase     , key=lambda __lowerCamelCase			: self.bpe_ranks.get(__lowerCamelCase     , float("inf"			)			)			)
           if bigram not in self.bpe_ranks:
               break
           SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   bigram
           SCREAMING_SNAKE_CASE										=   []
           SCREAMING_SNAKE_CASE										=   0
           while i < len(__lowerCamelCase			):
               try:
                   SCREAMING_SNAKE_CASE										=   word.index(__lowerCamelCase     , __lowerCamelCase			)
               except ValueError:
                   new_word.extend(word[i:]			)
                   break
               else:
                   new_word.extend(word[i:j]			)
                   SCREAMING_SNAKE_CASE										=   j

               if word[i] == first and i < len(__lowerCamelCase			) - 1 and word[i + 1] == second:
                   new_word.append(first + second			)
                   i += 2
               else:
                   new_word.append(word[i]			)
                   i += 1
           SCREAMING_SNAKE_CASE										=   tuple(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   new_word
           if len(__lowerCamelCase			) == 1:
               break
           else:
               SCREAMING_SNAKE_CASE										=   get_pairs(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   " ".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   word
       return word





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   []
       for token in re.findall(self.pat     , __lowerCamelCase			):
           SCREAMING_SNAKE_CASE										=   "".join(
               self.byte_encoder[b] for b in token.encode("utf-8"			)			)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase			).split(" "			)			)
       return bpe_tokens





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			Union[str, Any]			):
       return self.encoder.get(__lowerCamelCase     , self.encoder.get(self.unk_token			)			)





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			int			):
       return self.decoder.get(__lowerCamelCase			)





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   "".join(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   bytearray([self.byte_decoder[c] for c in text]			).decode("utf-8"     , errors=self.errors			)
       return text





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Optional[str] = None			):
       if not os.path.isdir(__lowerCamelCase			):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
           return
       SCREAMING_SNAKE_CASE										=   os.path.join(
           __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
       SCREAMING_SNAKE_CASE										=   os.path.join(
           __lowerCamelCase     , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]			)

       with open(__lowerCamelCase     , "w"     , encoding="utf-8"			) as f:
           f.write(json.dumps(self.encoder     , indent=2     , sort_keys=__lowerCamelCase     , ensure_ascii=__lowerCamelCase			) + "\n"			)

       SCREAMING_SNAKE_CASE										=   0
       with open(__lowerCamelCase     , "w"     , encoding="utf-8"			) as writer:
           writer.write("#version: 0.2\n"			)
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     , key=lambda __lowerCamelCase			: kv[1]			):
               if index != token_index:
                   logger.warning(
                       f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                       " Please check that the tokenizer is not corrupted!"			)
                   SCREAMING_SNAKE_CASE										=   token_index
               writer.write(" ".join(__lowerCamelCase			) + "\n"			)
               index += 1

       return vocab_file, merge_file





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       SCREAMING_SNAKE_CASE										=   [self.cls_token_id]
       SCREAMING_SNAKE_CASE										=   [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None     , __lowerCamelCase					:			bool = 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      _snake_case		(       self					:			List[str]     , __lowerCamelCase					:			List[int]     , __lowerCamelCase					:			Optional[List[int]] = None			):
       SCREAMING_SNAKE_CASE										=   [self.sep_token_id]
       SCREAMING_SNAKE_CASE										=   [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]





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			int     , __lowerCamelCase					:			Tuple=False     , **__lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   kwargs.pop("add_prefix_space"     , self.add_prefix_space			)
       if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase			) > 0 and not text[0].isspace()):
           SCREAMING_SNAKE_CASE										=   " " + text
       return (text, kwargs)





   def      _snake_case		(       self					:			List[str]     , __lowerCamelCase					:			Union[Dict[str, EncodedInput], BatchEncoding]     , __lowerCamelCase					:			Optional[int] = None     , __lowerCamelCase					:			PaddingStrategy = PaddingStrategy.DO_NOT_PAD     , __lowerCamelCase					:			Optional[int] = None     , __lowerCamelCase					:			Optional[bool] = None     , ):
       SCREAMING_SNAKE_CASE										=   super()._pad(
           encoded_inputs=__lowerCamelCase     , max_length=__lowerCamelCase     , padding_strategy=__lowerCamelCase     , pad_to_multiple_of=__lowerCamelCase     , return_attention_mask=__lowerCamelCase     , )

       # Load from model defaults
       if return_attention_mask is None:
           SCREAMING_SNAKE_CASE										=   "attention_mask" in self.model_input_names

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
           SCREAMING_SNAKE_CASE										=   encoded_inputs[self.model_input_names[0]]
           # `global_attention_mask` need to have the same length as other (sequential) inputs.
           SCREAMING_SNAKE_CASE										=   len(encoded_inputs["global_attention_mask"]			) != len(__lowerCamelCase			)

           if needs_to_be_padded:
               SCREAMING_SNAKE_CASE										=   len(__lowerCamelCase			) - len(encoded_inputs["global_attention_mask"]			)

               if self.padding_side == "right":
                   # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                   SCREAMING_SNAKE_CASE										=   (
                       encoded_inputs["global_attention_mask"] + [-1] * difference
                   )
               elif self.padding_side == "left":
                   SCREAMING_SNAKE_CASE										=   [-1] * difference + encoded_inputs[
                       "global_attention_mask"
                   ]
               else:
                   raise ValueError("Invalid padding strategy:" + str(self.padding_side			)			)

       return encoded_inputs


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 = }')