






from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

__snake_case    :    Optional[int]         =     {"""UserAgent""": UserAgent().random}







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]		)  ->   dict:

						"""simple docstring"""



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


class     __SCREAMING_SNAKE_CASE					:






						def __init__(	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						F"https://www.instagram.com/{username}/"
												lowerCAmelCase__    						=						self.get_json()






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						requests.get(self.url     ,						headers=_UpperCamelCase   ).text
												lowerCAmelCase__    						=						BeautifulSoup(_UpperCamelCase     ,						'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   ):






												"""simple docstring"""
												return F"{self.__class__.__name__}('{self.username}')"






						def __str__(	self   ):






												"""simple docstring"""
												return F"{self.fullname} ({self.username}) is {self.biography}"






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.user_data["username"]






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.user_data["full_name"]






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.user_data["biography"]






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.user_data["business_email"]






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.user_data["external_url"]






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.user_data["edge_followed_by"]["count"]






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.user_data["edge_follow"]["count"]






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.user_data["edge_owner_to_timeline_media"]["count"]






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.user_data["profile_pic_url_hd"]






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.user_data["is_verified"]







						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.user_data["is_private"]







def 	_UpperCamelCase   (    UpperCamelCase_       :					str = "github"		)  ->   None:

						"""simple docstring"""



						import os

						if os.environ.get('CI'		):
												return  # test failing on GitHub Actions
						lowerCAmelCase__    						=						InstagramUser(UpperCamelCase_		)
						assert instagram_user.user_data
						assert isinstance(instagram_user.user_data    ,       UpperCamelCase_		)
						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 > 12_0000
						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()
							__snake_case    :    str         =     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 = }')













# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__snake_case    :    Optional[int]         =     """facebook/wmt19-en-de"""
__snake_case    :    Optional[Any]         =     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__snake_case    :    List[str]         =     FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
__snake_case    :    List[Any]         =     tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__snake_case    :    Any         =     tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__snake_case    :    Any         =     """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-de





