'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor




class 			__A (					unittest.TestCase      ):



    def __init__(self       :       int  ,     __a       :       str  ,     __a       :       Dict=13  ,     __a       :       List[Any]=3  ,     __a       :       int=224  ,     __a       :       Optional[int]=30  ,     __a       :       List[str]=400  ,     __a       :       int=True  ,     __a       :       str=None  ,     __a       :       Dict=True  ,     __a       :       Dict=[0.5, 0.5, 0.5]  ,     __a       :       int=[0.5, 0.5, 0.5]  ,     ):
         UpperCAmelCase_			       =  size if size is not None else {"height": 18, "width": 18}
         UpperCAmelCase_			       =  parent
         UpperCAmelCase_			       =  batch_size
         UpperCAmelCase_			       =  num_channels
         UpperCAmelCase_			       =  image_size
         UpperCAmelCase_			       =  min_resolution
         UpperCAmelCase_			       =  max_resolution
         UpperCAmelCase_			       =  do_resize
         UpperCAmelCase_			       =  size
         UpperCAmelCase_			       =  do_normalize
         UpperCAmelCase_			       =  image_mean
         UpperCAmelCase_			       =  image_std






    def    _lowercase		(self       :       List[Any]					):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }




@require_torch
@require_vision
class 			__A (					UpperCamelCase__						,      unittest.TestCase      ):
    a__			:		List[str]			  =    ViTImageProcessor if is_vision_available() else None



    def    _lowercase		(self       :       Optional[int]					):
         UpperCAmelCase_			       =  EfficientFormerImageProcessorTester(self					)



    @property
    def    _lowercase		(self       :       List[Any]					):
         return self.image_proc_tester.prepare_image_processor_dict()



    def    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  self.image_processing_class(**self.image_processor_dict					)
         self.assertTrue(hasattr(__a  ,     "image_mean"					)					)
         self.assertTrue(hasattr(__a  ,     "image_std"					)					)
         self.assertTrue(hasattr(__a  ,     "do_normalize"					)					)
         self.assertTrue(hasattr(__a  ,     "do_resize"					)					)
         self.assertTrue(hasattr(__a  ,     "size"					)					)



    def    _lowercase		(self       :       int					):
         pass



    def    _lowercase		(self       :       int					):
         # Initialize image_processor
         UpperCAmelCase_			       =  self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         UpperCAmelCase_			       =  prepare_image_inputs(self.image_proc_tester  ,     equal_resolution=__a					)
         for image in image_inputs:
              self.assertIsInstance(__a  ,     Image.Image					)

         # Test not batched input
         UpperCAmelCase_			       =  image_processor(image_inputs[0]  ,     return_tensors="pt"					).pixel_values
         self.assertEqual(
             encoded_images.shape  ,     (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )  ,     )

         # Test batched
         UpperCAmelCase_			       =  image_processor(__a  ,     return_tensors="pt"					).pixel_values
         self.assertEqual(
             encoded_images.shape  ,     (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )  ,     )



    def    _lowercase		(self       :       int					):
         # Initialize image_processor
         UpperCAmelCase_			       =  self.image_processing_class(**self.image_processor_dict					)
         # create random numpy tensors
         UpperCAmelCase_			       =  prepare_image_inputs(self.image_proc_tester  ,     equal_resolution=__a  ,     numpify=__a					)
         for image in image_inputs:
              self.assertIsInstance(__a  ,     np.ndarray					)

         # Test not batched input
         UpperCAmelCase_			       =  image_processor(image_inputs[0]  ,     return_tensors="pt"					).pixel_values
         self.assertEqual(
             encoded_images.shape  ,     (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )  ,     )

         # Test batched
         UpperCAmelCase_			       =  image_processor(__a  ,     return_tensors="pt"					).pixel_values
         self.assertEqual(
             encoded_images.shape  ,     (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )  ,     )






    def    _lowercase		(self       :       Dict					):
         # Initialize image_processor
         UpperCAmelCase_			       =  self.image_processing_class(**self.image_processor_dict					)
         # create random PyTorch tensors
         UpperCAmelCase_			       =  prepare_image_inputs(self.image_proc_tester  ,     equal_resolution=__a  ,     torchify=__a					)
         for image in image_inputs:
              self.assertIsInstance(__a  ,     torch.Tensor					)

         # Test not batched input
         UpperCAmelCase_			       =  image_processor(image_inputs[0]  ,     return_tensors="pt"					).pixel_values
         self.assertEqual(
             encoded_images.shape  ,     (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )  ,     )

         # Test batched
         UpperCAmelCase_			       =  image_processor(__a  ,     return_tensors="pt"					).pixel_values
         self.assertEqual(
             encoded_images.shape  ,     (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size["height"],
                 self.image_proc_tester.size["width"],
             )  ,     )






from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase   =	{'''UserAgent''': UserAgent().random}



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]							)	->     dict:
							lowerCamelCase_					=       script.contents[0]
							lowerCamelCase_					=       json.loads(data[data.find("""{\"config\""""							) : -1]							)
							return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class    __A:

					def __init__(						self       :  int ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       F'''https://www.instagram.com/{username}/'''
												lowerCamelCase_					=       self.get_json()

					def  lowercase__   (						self       :  List[str] ):
												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       :  Dict ):
												return F'''{self.__class__.__name__}(\'{self.username}\')'''

					def __str__(						self       :  Optional[Any] ):
												return F'''{self.fullname} ({self.username}) is {self.biography}'''

					@property
					def  lowercase__   (						self       :  Dict ):
												return self.user_data["username"]

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

					@property
					def  lowercase__   (						self       :  Any ):
												return self.user_data["biography"]

					@property
					def  lowercase__   (						self       :  int ):
												return self.user_data["business_email"]

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["external_url"]

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

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

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

					@property
					def  lowercase__   (						self       :  List[str] ):
												return self.user_data["profile_pic_url_hd"]

					@property
					def  lowercase__   (						self       :  Optional[Any] ):
												return self.user_data["is_verified"]



					@property
					def  lowercase__   (						self       :  int ):
												return self.user_data["is_private"]



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str = "github"							)	->     None:
							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 > 1_5_0
							assert instagram_user.number_of_followers > 1_2_0_0_0_0
							assert instagram_user.number_of_followings > 1_5
							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()
		lowercase   =	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 = }""")





