


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 GLPNImageProcessor







class lowerCAmelCase_		(				unittest.TestCase ):

	'''simple docstring'''

	def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase=7	,   _UpperCAmelCase=3	,   _UpperCAmelCase=18	,   _UpperCAmelCase=30	,   _UpperCAmelCase=4_00	,   _UpperCAmelCase=True	,   _UpperCAmelCase=32	,   _UpperCAmelCase=True	,   ):
							snake_case_ =   parent
							snake_case_ =   batch_size
							snake_case_ =   num_channels
							snake_case_ =   image_size
							snake_case_ =   min_resolution
							snake_case_ =   max_resolution
							snake_case_ =   do_resize
							snake_case_ =   size_divisor
							snake_case_ =   do_rescale





	def 		UpperCamelCase__  (			self      ):
							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}







@require_torch
@require_vision
class lowerCAmelCase_		(				__A      ,		unittest.TestCase ):

	'''simple docstring'''

	__snake_case				=						GLPNImageProcessor if is_vision_available() else None



	def 		UpperCamelCase__  (			self      ):
							snake_case_ =   GLPNImageProcessingTester(self      )



	@property
	def 		UpperCamelCase__  (			self      ):
							return self.image_processor_tester.prepare_image_processor_dict()



	def 		UpperCamelCase__  (			self      ):
							snake_case_ =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(_UpperCAmelCase	,   '''do_resize'''      )      )
							self.assertTrue(hasattr(_UpperCAmelCase	,   '''size_divisor'''      )      )
							self.assertTrue(hasattr(_UpperCAmelCase	,   '''resample'''      )      )
							self.assertTrue(hasattr(_UpperCAmelCase	,   '''do_rescale'''      )      )



	def 		UpperCamelCase__  (			self      ):
							pass



	def 		UpperCamelCase__  (			self      ):
							snake_case_ =   self.image_processing_class(**self.image_processor_dict      )
							# create random PIL images
							snake_case_ =   prepare_image_inputs(self.image_processor_tester	,   equal_resolution=_UpperCAmelCase      )
							for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase	,   Image.Image      )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_ =   image_processing(image_inputs[0]	,   return_tensors='''pt'''      ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )



	def 		UpperCamelCase__  (			self      ):
							snake_case_ =   self.image_processing_class(**self.image_processor_dict      )
							# create random numpy tensors
							snake_case_ =   prepare_image_inputs(self.image_processor_tester	,   equal_resolution=_UpperCAmelCase	,   numpify=_UpperCAmelCase      )
							for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase	,   np.ndarray      )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_ =   image_processing(image_inputs[0]	,   return_tensors='''pt'''      ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )





	def 		UpperCamelCase__  (			self      ):
							snake_case_ =   self.image_processing_class(**self.image_processor_dict      )
							# create random PyTorch tensors
							snake_case_ =   prepare_image_inputs(self.image_processor_tester	,   equal_resolution=_UpperCAmelCase	,   torchify=_UpperCAmelCase      )
							for image in image_inputs:
													self.assertIsInstance(_UpperCAmelCase	,   torch.Tensor      )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_ =   image_processing(image_inputs[0]	,   return_tensors='''pt'''      ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )



from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase    =					{"""UserAgent""": UserAgent().random}






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						dict:




      """simple docstring"""

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







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase      ):
       snake_case_ =   F'''https://www.instagram.com/{username}/'''
       snake_case_ =   self.get_json()



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   requests.get(self.url	,   headers=_UpperCAmelCase      ).text
       snake_case_ =   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      ):
       return F'''{self.__class__.__name__}(\'{self.username}\')'''



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



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



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["full_name"]



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



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



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["external_url"]



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["edge_followed_by"]["count"]



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



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["edge_owner_to_timeline_media"]["count"]



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["profile_pic_url_hd"]



 @property
 def 		UpperCamelCase__  (			self      ):
       return self.user_data["is_verified"]





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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = "github"      )->						None:




      """simple docstring"""

      import os

      if os.environ.get('''CI'''      ):
            return  # test failing on GitHub Actions
      snake_case_ =   InstagramUser(SCREAMING_SNAKE_CASE      )
      assert instagram_user.user_data
      assert isinstance(instagram_user.user_data      ,      SCREAMING_SNAKE_CASE      )
      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()
   UpperCAmelCase    =					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 = }''')