


import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    unittest.TestCase			):
      snake_case__				:     Any        =						BlenderbotSmallTokenizer
      snake_case__				:     str        =						False
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Any:
             super().setUp()

             a_					:		Tuple							=  ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
             a_					:		Any							=  dict(zip(SCREAMING_SNAKE_CASE__     ,							range(len(SCREAMING_SNAKE_CASE__     )     )     )     )

             a_					:		Optional[Any]							=  ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
             a_					:		str							=  {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

             a_					:		Optional[int]							=  os.path.join(self.tmpdirname     ,							VOCAB_FILES_NAMES['vocab_file']     )
             a_					:		Tuple							=  os.path.join(self.tmpdirname     ,							VOCAB_FILES_NAMES['merges_file']     )
             with open(self.vocab_file     ,							'w'     ,							encoding='utf-8'     ) as fp:
                    fp.write(json.dumps(SCREAMING_SNAKE_CASE__     ) + '\n'     )
             with open(self.merges_file     ,							'w'     ,							encoding='utf-8'     ) as fp:
                    fp.write('\n'.join(SCREAMING_SNAKE_CASE__     )     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							**SCREAMING_SNAKE_CASE__				:       Dict     )		->			Dict:
             kwargs.update(self.special_tokens_map     )
             return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Union[str, Any]:
             a_					:		Dict							=  'adapt act apte'
             a_					:		List[Any]							=  'adapt act apte'
             return input_text, output_text
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Optional[Any]:
             a_					:		Any							=  BlenderbotSmallTokenizer(self.vocab_file     ,							self.merges_file     ,							**self.special_tokens_map     )
             a_					:		Optional[int]							=  'adapt act apte'
             a_					:		Optional[int]							=  ['adapt', 'act', 'ap@@', 'te']
             a_					:		Optional[Any]							=  tokenizer.tokenize(SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

             a_					:		str							=  [0, 1, 2, 3, 4, 5]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			str:
             a_					:		Any							=  BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'     )
             assert tok('sam'     ).input_ids == [1_3_8_4]
             a_					:		Optional[Any]							=  'I am a small frog.'
             a_					:		Dict							=  tok([src_text]     ,							padding=SCREAMING_SNAKE_CASE__     ,							truncation=SCREAMING_SNAKE_CASE__     )['input_ids']
             a_					:		int							=  tok.batch_decode(SCREAMING_SNAKE_CASE__     ,							skip_special_tokens=SCREAMING_SNAKE_CASE__     ,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     )[0]
             assert src_text != decoded  # I wish it did!
             assert decoded == "i am a small frog ."

      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			str:
             a_					:		Dict							=  BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'     )
             a_					:		int							=  'I am a small frog .'
             a_					:		List[str]							=  '.'
             a_					:		Tuple							=  tok(SCREAMING_SNAKE_CASE__     )['input_ids']
             a_					:		Union[str, Any]							=  tok(SCREAMING_SNAKE_CASE__     )['input_ids']

             assert encoded[-1] == encoded_dot[0]






from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase_      : Any  					=       {'UserAgent': UserAgent().random}







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]				)	->		dict:

       """simple docstring"""

       a_					:		Tuple							=  script.contents[0]
       a_					:		int							=  json.loads(data[data.find('{"config"'				) : -1]				)
       return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Dict     )		->			Optional[Any]:
             a_					:		Tuple							=  F"""https://www.instagram.com/{username}/"""
             a_					:		Optional[Any]							=  self.get_json()
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			dict:
             a_					:		Any							=  requests.get(self.url     ,							headers=SCREAMING_SNAKE_CASE__     ).text
             a_					:		Dict							=  BeautifulSoup(SCREAMING_SNAKE_CASE__     ,							'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]     )		->			str:
             return F"""{self.__class__.__name__}('{self.username}')"""
      def __str__(       self				:       Optional[int]     )		->			str:
             return F"""{self.fullname} ({self.username}) is {self.biography}"""
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			str:
             return self.user_data["username"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             return self.user_data["full_name"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			str:
             return self.user_data["biography"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			str:
             return self.user_data["business_email"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			str:
             return self.user_data["external_url"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             return self.user_data["edge_followed_by"]["count"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			int:
             return self.user_data["edge_follow"]["count"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			int:
             return self.user_data["edge_owner_to_timeline_media"]["count"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			str:
             return self.user_data["profile_pic_url_hd"]
      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			bool:
             return self.user_data["is_verified"]

      @property
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			bool:
             return self.user_data["is_private"]







def        SCREAMING_SNAKE_CASE_						(			__A :			str = "github"				)	->		None:

       """simple docstring"""

       import os

       if os.environ.get('CI'				):
              return  # test failing on GitHub Actions
       a_					:		int							=  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 > 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_      : Union[str, Any]  					=       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 = }')


