

"""simple docstring"""




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

				if is_vision_available():
								from transformers import MaskaFormerImageProcessor

if is_vision_available():
				from PIL import Image
class 			_UpperCAmelCase					:



	def __init__(   self			:    Union[str, Any]       ,  _lowercase			:    List[Any]       ,  _lowercase			:    Dict=2       ,  _lowercase			:    Dict=True       ,  _lowercase			:    Optional[Any]=False       ,  _lowercase			:    int=10       ,  _lowercase			:    List[Any]=3       ,  _lowercase			:    Optional[Any]=32 * 8       ,  _lowercase			:    Dict=32 * 8       ,  _lowercase			:    List[str]=4       ,  _lowercase			:    Optional[Any]=64       ,  ):
		__UpperCAmelCase							=      parent
		__UpperCAmelCase							=      batch_size
		__UpperCAmelCase							=      is_training
		__UpperCAmelCase							=      use_auxiliary_loss
		__UpperCAmelCase							=      num_queries
		__UpperCAmelCase							=      num_channels
		__UpperCAmelCase							=      min_size
		__UpperCAmelCase							=      max_size
		__UpperCAmelCase							=      num_labels
		__UpperCAmelCase							=      hidden_dim
		__UpperCAmelCase							=      hidden_dim



	def     a   (   self			:    Optional[Any]		):
		__UpperCAmelCase							=      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
		    SCREAMING_SNAKE_CASE__		)

		__UpperCAmelCase							=      torch.ones([self.batch_size, self.min_size, self.max_size]       ,  device=SCREAMING_SNAKE_CASE__		)

		__UpperCAmelCase							=      (
		    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]       ,  device=SCREAMING_SNAKE_CASE__		) > 0.5
		).float()
		__UpperCAmelCase							=      (torch.rand((self.batch_size, self.num_labels)       ,  device=SCREAMING_SNAKE_CASE__		) > 0.5).long()

		__UpperCAmelCase							=      self.get_config()
		return config, pixel_values, pixel_mask, mask_labels, class_labels



	def     a   (   self			:    Any		):
		__UpperCAmelCase							=      MaskaFormerConfig(
		    hidden_size=self.hidden_dim       ,  )
		__UpperCAmelCase							=      self.num_queries
		__UpperCAmelCase							=      self.num_labels

		__UpperCAmelCase							=      [1, 1, 1, 1]
		__UpperCAmelCase							=      self.num_channels

		__UpperCAmelCase							=      64
		__UpperCAmelCase							=      1_28
		__UpperCAmelCase							=      self.hidden_dim
		__UpperCAmelCase							=      self.hidden_dim
		__UpperCAmelCase							=      self.hidden_dim
		return config



	def     a   (   self			:    Any		):
		__UpperCAmelCase							=      self.prepare_config_and_inputs()
		__UpperCAmelCase							=      {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
		return config, inputs_dict



	def     a   (   self			:    Optional[Any]       ,  _lowercase			:    Optional[int]       ,  _lowercase			:    int		):
		__UpperCAmelCase							=      output.encoder_hidden_states
		__UpperCAmelCase							=      output.pixel_decoder_hidden_states
		__UpperCAmelCase							=      output.transformer_decoder_hidden_states

		self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__		)       ,  len(config.backbone_config.depths		)		)
		self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__		)       ,  len(config.backbone_config.depths		)		)
		self.parent.assertTrue(len(SCREAMING_SNAKE_CASE__		)       ,  config.decoder_layers		)



	def     a   (   self			:    Optional[Any]       ,  _lowercase			:    Dict       ,  _lowercase			:    List[Any]       ,  _lowercase			:    int       ,  _lowercase			:    List[str]=False		):
		with torch.no_grad():
			__UpperCAmelCase							=      MaskaFormerModel(config=SCREAMING_SNAKE_CASE__		)
			model.to(SCREAMING_SNAKE_CASE__		)
			model.eval()

			__UpperCAmelCase							=      model(pixel_values=SCREAMING_SNAKE_CASE__       ,  pixel_mask=SCREAMING_SNAKE_CASE__		)
			__UpperCAmelCase							=      model(SCREAMING_SNAKE_CASE__       ,  output_hidden_states=SCREAMING_SNAKE_CASE__		)

		self.parent.assertEqual(
		    output.transformer_decoder_last_hidden_state.shape       ,  (self.batch_size, self.num_queries, self.hidden_dim)       ,  )
		# let's ensure the other two hidden state exists
		self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None		)
		self.parent.assertTrue(output.encoder_last_hidden_state is not None		)

		if output_hidden_states:
			self.check_output_hidden_state(SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__		)



	def     a   (   self			:    Dict       ,  _lowercase			:    str       ,  _lowercase			:    int       ,  _lowercase			:    Optional[Any]       ,  _lowercase			:    Optional[Any]       ,  _lowercase			:    Dict		):
		__UpperCAmelCase							=      MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE__		)
		model.to(SCREAMING_SNAKE_CASE__		)
		model.eval()

		def comm_check_on_output(_lowercase			:    List[Any]		):
			# let's still check that all the required stuff is there
			self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None		)
			self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None		)
			self.parent.assertTrue(result.encoder_last_hidden_state is not None		)
			# okay, now we need to check the logits shape
			# due to the encoder compression, masks have a //4 spatial size
			self.parent.assertEqual(
			    result.masks_queries_logits.shape       ,  (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)       ,  )
			# + 1 for null class
			self.parent.assertEqual(
			    result.class_queries_logits.shape       ,  (self.batch_size, self.num_queries, self.num_labels + 1)		)

		with torch.no_grad():
			__UpperCAmelCase							=      model(pixel_values=SCREAMING_SNAKE_CASE__       ,  pixel_mask=SCREAMING_SNAKE_CASE__		)
			__UpperCAmelCase							=      model(SCREAMING_SNAKE_CASE__		)

			comm_check_on_output(SCREAMING_SNAKE_CASE__		)

			__UpperCAmelCase							=      model(
			    pixel_values=SCREAMING_SNAKE_CASE__       ,  pixel_mask=SCREAMING_SNAKE_CASE__       ,  mask_labels=SCREAMING_SNAKE_CASE__       ,  class_labels=SCREAMING_SNAKE_CASE__		)

		comm_check_on_output(SCREAMING_SNAKE_CASE__		)

		self.parent.assertTrue(result.loss is not None		)
		self.parent.assertEqual(result.loss.shape       ,  torch.Size([1]		)		)






@require_torch
class 			_UpperCAmelCase					(				lowercase__						,  lowercase__						,  unittest.TestCase    ):
	a__						:      Optional[int]						       = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
	a__						:      List[Any]						       = {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}

	a__						:      Dict						       = False
	a__						:      Optional[int]						       = False
	a__						:      Tuple						       = False
	a__						:      Union[str, Any]						       = False



	def     a   (   self			:    str		):
		__UpperCAmelCase							=      MaskaFormerModelTester(self		)
		__UpperCAmelCase							=      ConfigTester(self       ,  config_class=SCREAMING_SNAKE_CASE__       ,  has_text_modality=SCREAMING_SNAKE_CASE__		)



	def     a   (   self			:    Dict		):
		self.config_tester.run_common_tests()



	def     a   (   self			:    Dict		):
		__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs_for_common()
		self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE__       ,  **SCREAMING_SNAKE_CASE__       ,  output_hidden_states=SCREAMING_SNAKE_CASE__		)



	def     a   (   self			:    Dict		):
		__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE__		)



	@unittest.skip(reason='''Mask2Former does not use inputs_embeds'''		)
	def     a   (   self			:    Any		):
		pass



	@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method'''		)
	def     a   (   self			:    Tuple		):
		pass



	@unittest.skip(reason='''Mask2Former is not a generative model'''		)
	def     a   (   self			:    Any		):
		pass



	@unittest.skip(reason='''Mask2Former does not use token embeddings'''		)
	def     a   (   self			:    Union[str, Any]		):
		pass



	@require_torch_multi_gpu
	@unittest.skip(
	    reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''		)
	def     a   (   self			:    int		):
		pass



	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
	def     a   (   self			:    Union[str, Any]		):
		pass



	def     a   (   self			:    List[str]		):
		__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__UpperCAmelCase							=      model_class(SCREAMING_SNAKE_CASE__		)
			__UpperCAmelCase							=      inspect.signature(model.forward		)
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			__UpperCAmelCase							=      [*signature.parameters.keys()]

			__UpperCAmelCase							=      ['pixel_values']
			self.assertListEqual(arg_names[:1]       ,  SCREAMING_SNAKE_CASE__		)



	@slow
	def     a   (   self			:    int		):
		for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
			__UpperCAmelCase							=      MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__		)
			self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)



	def     a   (   self			:    List[Any]		):
		__UpperCAmelCase							=      (self.model_tester.min_size,) * 2
		__UpperCAmelCase							=      {
		    'pixel_values': torch.randn((2, 3, *size)       ,  device=SCREAMING_SNAKE_CASE__		),
		    'mask_labels': torch.randn((2, 10, *size)       ,  device=SCREAMING_SNAKE_CASE__		),
		    'class_labels': torch.zeros(2       ,  10       ,  device=SCREAMING_SNAKE_CASE__		).long(),
		}
		__UpperCAmelCase							=      self.model_tester.get_config()

		__UpperCAmelCase							=      MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)
		__UpperCAmelCase							=      model(**SCREAMING_SNAKE_CASE__		)
		self.assertTrue(outputs.loss is not None		)



	def     a   (   self			:    Optional[Any]		):
		__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs_for_common()
		self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE__       ,  **SCREAMING_SNAKE_CASE__       ,  output_hidden_states=SCREAMING_SNAKE_CASE__		)



	def     a   (   self			:    Any		):
		__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			__UpperCAmelCase							=      model_class(SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)
			__UpperCAmelCase							=      model(**SCREAMING_SNAKE_CASE__       ,  output_attentions=SCREAMING_SNAKE_CASE__		)
			self.assertTrue(outputs.attentions is not None		)



	def     a   (   self			:    Optional[int]		):
		if not self.model_tester.is_training:
			return

		__UpperCAmelCase							=      self.all_model_classes[1]
		__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs()

		__UpperCAmelCase							=      model_class(SCREAMING_SNAKE_CASE__		)
		model.to(SCREAMING_SNAKE_CASE__		)
		model.train()

		__UpperCAmelCase							=      model(SCREAMING_SNAKE_CASE__       ,  mask_labels=SCREAMING_SNAKE_CASE__       ,  class_labels=SCREAMING_SNAKE_CASE__		).loss
		loss.backward()



	def     a   (   self			:    List[str]		):
		__UpperCAmelCase							=      self.all_model_classes[1]
		__UpperCAmelCase							=      self.model_tester.prepare_config_and_inputs()
		__UpperCAmelCase							=      True
		__UpperCAmelCase							=      True

		__UpperCAmelCase							=      model_class(SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)
		model.train()

		__UpperCAmelCase							=      model(SCREAMING_SNAKE_CASE__       ,  mask_labels=SCREAMING_SNAKE_CASE__       ,  class_labels=SCREAMING_SNAKE_CASE__		)

		__UpperCAmelCase							=      outputs.encoder_hidden_states[0]
		encoder_hidden_states.retain_grad()

		__UpperCAmelCase							=      outputs.pixel_decoder_hidden_states[0]
		pixel_decoder_hidden_states.retain_grad()

		__UpperCAmelCase							=      outputs.transformer_decoder_hidden_states[0]
		transformer_decoder_hidden_states.retain_grad()

		__UpperCAmelCase							=      outputs.attentions[0]
		attentions.retain_grad()

		outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE__		)

		self.assertIsNotNone(encoder_hidden_states.grad		)
		self.assertIsNotNone(pixel_decoder_hidden_states.grad		)
		self.assertIsNotNone(transformer_decoder_hidden_states.grad		)
		self.assertIsNotNone(attentions.grad		)






_lowercase					:     Dict				 =		1e-4





def 	lowercase__		(    ):
	__UpperCAmelCase							=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
	return image




@require_vision
@slow
class 			_UpperCAmelCase					(				unittest.TestCase    ):



	@cached_property
	def     a   (   self			:    Tuple		):
		return "facebook/mask2former-swin-small-coco-instance"



	@cached_property
	def     a   (   self			:    Tuple		):
		return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints		) if is_vision_available() else None



	def     a   (   self			:    Any		):
		__UpperCAmelCase							=      MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(SCREAMING_SNAKE_CASE__		)
		__UpperCAmelCase							=      self.default_image_processor
		__UpperCAmelCase							=      prepare_img()
		__UpperCAmelCase							=      image_processor(SCREAMING_SNAKE_CASE__       ,  return_tensors='''pt'''		).to(SCREAMING_SNAKE_CASE__		)
		__UpperCAmelCase							=      inputs['pixel_values'].shape
		# check size is divisible by 32
		self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
		# check size
		self.assertEqual(SCREAMING_SNAKE_CASE__       ,  (1, 3, 3_84, 3_84)		)

		with torch.no_grad():
			__UpperCAmelCase							=      model(**SCREAMING_SNAKE_CASE__		)

		__UpperCAmelCase							=      torch.tensor(
		    [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]		).to(SCREAMING_SNAKE_CASE__		)
		self.assertTrue(
		    torch.allclose(
		        outputs.encoder_last_hidden_state[0, 0, :3, :3]       ,  SCREAMING_SNAKE_CASE__       ,  atol=SCREAMING_SNAKE_CASE__		)		)

		__UpperCAmelCase							=      torch.tensor(
		    [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]		).to(SCREAMING_SNAKE_CASE__		)
		self.assertTrue(
		    torch.allclose(
		        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]       ,  SCREAMING_SNAKE_CASE__       ,  atol=SCREAMING_SNAKE_CASE__		)		)

		__UpperCAmelCase							=      torch.tensor(
		    [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]		).to(SCREAMING_SNAKE_CASE__		)
		self.assertTrue(
		    torch.allclose(
		        outputs.transformer_decoder_last_hidden_state[0, :3, :3]       ,  SCREAMING_SNAKE_CASE__       ,  atol=SCREAMING_SNAKE_CASE__		)		)



	def     a   (   self			:    Any		):
		__UpperCAmelCase							=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(SCREAMING_SNAKE_CASE__		).eval()
		__UpperCAmelCase							=      self.default_image_processor
		__UpperCAmelCase							=      prepare_img()
		__UpperCAmelCase							=      image_processor(SCREAMING_SNAKE_CASE__       ,  return_tensors='''pt'''		).to(SCREAMING_SNAKE_CASE__		)
		__UpperCAmelCase							=      inputs['pixel_values'].shape
		# check size is divisible by 32
		self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
		# check size
		self.assertEqual(SCREAMING_SNAKE_CASE__       ,  (1, 3, 3_84, 3_84)		)

		with torch.no_grad():
			__UpperCAmelCase							=      model(**SCREAMING_SNAKE_CASE__		)
		# masks_queries_logits
		__UpperCAmelCase							=      outputs.masks_queries_logits
		self.assertEqual(
		    masks_queries_logits.shape       ,  (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		)
		__UpperCAmelCase							=      [
		    [-8.7_839, -9.0_056, -8.8_121],
		    [-7.4_104, -7.0_313, -6.5_401],
		    [-6.6_105, -6.3_427, -6.4_675],
		]
		__UpperCAmelCase							=      torch.tensor(SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)
		self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]       ,  SCREAMING_SNAKE_CASE__       ,  atol=SCREAMING_SNAKE_CASE__		)		)
		# class_queries_logits
		__UpperCAmelCase							=      outputs.class_queries_logits
		self.assertEqual(class_queries_logits.shape       ,  (1, model.config.num_queries, model.config.num_labels + 1)		)
		__UpperCAmelCase							=      torch.tensor(
		    [
		        [1.8_324, -8.0_835, -4.1_922],
		        [0.8_450, -9.0_050, -3.6_053],
		        [0.3_045, -7.7_293, -3.0_275],
		    ]		).to(SCREAMING_SNAKE_CASE__		)
		self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,  SCREAMING_SNAKE_CASE__       ,  atol=SCREAMING_SNAKE_CASE__		)		)



	def     a   (   self			:    Optional[Any]		):
		__UpperCAmelCase							=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(SCREAMING_SNAKE_CASE__		).eval()
		__UpperCAmelCase							=      self.default_image_processor

		__UpperCAmelCase							=      image_processor(
		    [np.zeros((3, 8_00, 13_33)		), np.zeros((3, 8_00, 13_33)		)]       ,  segmentation_maps=[np.zeros((3_84, 3_84)		).astype(np.floataa		), np.zeros((3_84, 3_84)		).astype(np.floataa		)]       ,  return_tensors='''pt'''       ,  )

		__UpperCAmelCase							=      inputs['pixel_values'].to(SCREAMING_SNAKE_CASE__		)
		__UpperCAmelCase							=      [el.to(SCREAMING_SNAKE_CASE__		) for el in inputs['mask_labels']]
		__UpperCAmelCase							=      [el.to(SCREAMING_SNAKE_CASE__		) for el in inputs['class_labels']]

		with torch.no_grad():
			__UpperCAmelCase							=      model(**SCREAMING_SNAKE_CASE__		)

		self.assertTrue(outputs.loss is not None		)




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 = }')


