
"""simple docstring"""




from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    TFLayoutLMvaForQuestionAnswering,
					    TFLayoutLMvaForSequenceClassification,
					    TFLayoutLMvaForTokenClassification,
					    TFLayoutLMvaModel,
					)

if is_vision_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor






class 					__magic_name__							:






				'''simple docstring'''





				def __init__( self	,				_a	,				_a=2	,				_a=3	,				_a=4	,				_a=2	,				_a=7	,				_a=True	,				_a=True	,				_a=True	,				_a=True	,				_a=99	,				_a=36	,				_a=2	,				_a=4	,				_a=37	,				_a="gelu"	,				_a=0.1	,				_a=0.1	,				_a=512	,				_a=16	,				_a=2	,				_a=0.02	,				_a=6	,				_a=6	,				_a=3	,				_a=4	,				_a=None	,				_a=1_000	,				):





							"""simple docstring"""
							lowerCamelCase       =      parent
							lowerCamelCase       =      batch_size
							lowerCamelCase       =      num_channels
							lowerCamelCase       =      image_size
							lowerCamelCase       =      patch_size
							lowerCamelCase       =      is_training
							lowerCamelCase       =      use_input_mask
							lowerCamelCase       =      use_token_type_ids
							lowerCamelCase       =      use_labels
							lowerCamelCase       =      vocab_size
							lowerCamelCase       =      hidden_size
							lowerCamelCase       =      num_hidden_layers
							lowerCamelCase       =      num_attention_heads
							lowerCamelCase       =      intermediate_size
							lowerCamelCase       =      hidden_act
							lowerCamelCase       =      hidden_dropout_prob
							lowerCamelCase       =      attention_probs_dropout_prob
							lowerCamelCase       =      max_position_embeddings
							lowerCamelCase       =      type_vocab_size
							lowerCamelCase       =      type_sequence_label_size
							lowerCamelCase       =      initializer_range
							lowerCamelCase       =      coordinate_size
							lowerCamelCase       =      shape_size
							lowerCamelCase       =      num_labels
							lowerCamelCase       =      num_choices
							lowerCamelCase       =      scope
							lowerCamelCase       =      range_bbox

							# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
							lowerCamelCase       =      text_seq_length
							lowerCamelCase       =      (image_size // patch_size) ** 2 + 1
							lowerCamelCase       =      self.text_seq_length + self.image_seq_length





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      ids_tensor([self.batch_size, self.text_seq_length]	,				self.vocab_size				)

							lowerCamelCase       =      ids_tensor([self.batch_size, self.text_seq_length, 4]	,				self.range_bbox				)
							lowerCamelCase       =      bbox.numpy()
							# Ensure that bbox is legal
							for i in range(bbox.shape[0]				):
										for j in range(bbox.shape[1]				):
													if bbox[i, j, 3] < bbox[i, j, 1]:
																lowerCamelCase       =      bbox[i, j, 3]
																lowerCamelCase       =      bbox[i, j, 1]
																lowerCamelCase       =      tmp_coordinate
													if bbox[i, j, 2] < bbox[i, j, 0]:
																lowerCamelCase       =      bbox[i, j, 2]
																lowerCamelCase       =      bbox[i, j, 0]
																lowerCamelCase       =      tmp_coordinate
							lowerCamelCase       =      tf.constant(_a				)

							lowerCamelCase       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

							lowerCamelCase       =      None
							if self.use_input_mask:
										lowerCamelCase       =      random_attention_mask([self.batch_size, self.text_seq_length]				)

							lowerCamelCase       =      None
							if self.use_token_type_ids:
										lowerCamelCase       =      ids_tensor([self.batch_size, self.text_seq_length]	,				self.type_vocab_size				)

							lowerCamelCase       =      None
							lowerCamelCase       =      None
							if self.use_labels:
										lowerCamelCase       =      ids_tensor([self.batch_size]	,				self.type_sequence_label_size				)
										lowerCamelCase       =      ids_tensor([self.batch_size, self.text_seq_length]	,				self.num_labels				)

							lowerCamelCase       =      LayoutLMvaConfig(
							    vocab_size=self.vocab_size	,				hidden_size=self.hidden_size	,				num_hidden_layers=self.num_hidden_layers	,				num_attention_heads=self.num_attention_heads	,				intermediate_size=self.intermediate_size	,				hidden_act=self.hidden_act	,				hidden_dropout_prob=self.hidden_dropout_prob	,				attention_probs_dropout_prob=self.attention_probs_dropout_prob	,				max_position_embeddings=self.max_position_embeddings	,				type_vocab_size=self.type_vocab_size	,				initializer_range=self.initializer_range	,				coordinate_size=self.coordinate_size	,				shape_size=self.shape_size	,				input_size=self.image_size	,				patch_size=self.patch_size	,				)

							return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      TFLayoutLMvaModel(config=_a				)

							# text + image
							lowerCamelCase       =      model(_a	,				pixel_values=_a	,				training=_a				)
							lowerCamelCase       =      model(
							    _a	,				bbox=_a	,				pixel_values=_a	,				attention_mask=_a	,				token_type_ids=_a	,				training=_a	,				)
							lowerCamelCase       =      model(_a	,				bbox=_a	,				pixel_values=_a	,				training=_a				)

							self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)				)

							# text only
							lowerCamelCase       =      model(_a	,				training=_a				)
							self.parent.assertEqual(
							    result.last_hidden_state.shape	,				(self.batch_size, self.text_seq_length, self.hidden_size)				)

							# image only
							lowerCamelCase       =      model({"""pixel_values""": pixel_values}	,				training=_a				)
							self.parent.assertEqual(
							    result.last_hidden_state.shape	,				(self.batch_size, self.image_seq_length, self.hidden_size)				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.num_labels
							lowerCamelCase       =      TFLayoutLMvaForSequenceClassification(config=_a				)
							lowerCamelCase       =      model(
							    _a	,				bbox=_a	,				pixel_values=_a	,				attention_mask=_a	,				token_type_ids=_a	,				labels=_a	,				training=_a	,				)
							self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.num_labels
							lowerCamelCase       =      TFLayoutLMvaForTokenClassification(config=_a				)
							lowerCamelCase       =      model(
							    _a	,				bbox=_a	,				pixel_values=_a	,				attention_mask=_a	,				token_type_ids=_a	,				labels=_a	,				training=_a	,				)
							self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.text_seq_length, self.num_labels)				)





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      2
							lowerCamelCase       =      TFLayoutLMvaForQuestionAnswering(config=_a				)
							lowerCamelCase       =      model(
							    _a	,				bbox=_a	,				pixel_values=_a	,				attention_mask=_a	,				token_type_ids=_a	,				start_positions=_a	,				end_positions=_a	,				training=_a	,				)
							self.parent.assertEqual(result.start_logits.shape	,				(self.batch_size, self.seq_length)				)
							self.parent.assertEqual(result.end_logits.shape	,				(self.batch_size, self.seq_length)				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.prepare_config_and_inputs()
							((lowerCamelCase)   ,     (lowerCamelCase)   ,     (lowerCamelCase)   ,     (lowerCamelCase)   ,     (lowerCamelCase)   ,     (lowerCamelCase)   ,     (lowerCamelCase)   ,     (lowerCamelCase))       =      config_and_inputs
							lowerCamelCase       =      {
							    """input_ids""": input_ids,
							    """bbox""": bbox,
							    """pixel_values""": pixel_values,
							    """token_type_ids""": token_type_ids,
							    """attention_mask""": input_mask,
							}
							return config, inputs_dict






@require_tf
class 					__magic_name__							( UpperCAmelCase__		,      UpperCAmelCase__		,      unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		(
				    (
				        TFLayoutLMvaModel,
				        TFLayoutLMvaForQuestionAnswering,
				        TFLayoutLMvaForSequenceClassification,
				        TFLayoutLMvaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				__UpperCamelCase    						=		(
				    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
				    if is_tf_available()
				    else {}
				)

				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		False
				__UpperCamelCase    						=		False





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							return True





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a=False				):





							"""simple docstring"""
							lowerCamelCase       =      copy.deepcopy(_a				)

							if model_class in get_values(_a				):
										lowerCamelCase       =      {
										    k: tf.tile(tf.expand_dims(_a	,				1				)	,				(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)				)
										    if isinstance(_a	,				tf.Tensor				) and v.ndim > 0
										    else v
										    for k, v in inputs_dict.items()
										}

							if return_labels:
										if model_class in get_values(_a				):
													lowerCamelCase       =      tf.ones(self.model_tester.batch_size	,				dtype=tf.intaa				)
										elif model_class in get_values(_a				):
													lowerCamelCase       =      tf.zeros(self.model_tester.batch_size	,				dtype=tf.intaa				)
													lowerCamelCase       =      tf.zeros(self.model_tester.batch_size	,				dtype=tf.intaa				)
										elif model_class in get_values(_a				):
													lowerCamelCase       =      tf.zeros(self.model_tester.batch_size	,				dtype=tf.intaa				)
										elif model_class in get_values(_a				):
													lowerCamelCase       =      tf.zeros(
													    (self.model_tester.batch_size, self.model_tester.text_seq_length)	,				dtype=tf.intaa				)

							return inputs_dict





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      TFLayoutLMvaModelTester(self				)
							lowerCamelCase       =      ConfigTester(self	,				config_class=_a	,				hidden_size=37				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							self.config_tester.run_common_tests()





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
										lowerCamelCase       =      model_class(_a				)
										if getattr(_a	,				"""hf_compute_loss"""	,				_a				):
													# The number of elements in the loss should be the same as the number of elements in the label
													lowerCamelCase       =      self._prepare_for_class(inputs_dict.copy()	,				_a	,				return_labels=_a				)
													lowerCamelCase       =      prepared_for_class[
													    sorted(prepared_for_class.keys() - inputs_dict.keys()	,				reverse=_a				)[0]
													]
													lowerCamelCase       =      added_label.shape.as_list()[:1]

													# Test that model correctly compute the loss with kwargs
													lowerCamelCase       =      self._prepare_for_class(inputs_dict.copy()	,				_a	,				return_labels=_a				)
													lowerCamelCase       =      prepared_for_class.pop("""input_ids"""				)

													lowerCamelCase       =      model(_a	,				**_a				)[0]
													self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)

													# Test that model correctly compute the loss when we mask some positions
													lowerCamelCase       =      self._prepare_for_class(inputs_dict.copy()	,				_a	,				return_labels=_a				)
													lowerCamelCase       =      prepared_for_class.pop("""input_ids"""				)
													if "labels" in prepared_for_class:
																lowerCamelCase       =      prepared_for_class["""labels"""].numpy()
																if len(labels.shape				) > 1 and labels.shape[1] != 1:
																			lowerCamelCase       =      -100
																			lowerCamelCase       =      tf.convert_to_tensor(_a				)
																			lowerCamelCase       =      model(_a	,				**_a				)[0]
																			self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)
																			self.assertTrue(not np.any(np.isnan(loss.numpy()				)				)				)

                # Test that model correctly compute the loss with a dict
													lowerCamelCase       =      self._prepare_for_class(inputs_dict.copy()	,				_a	,				return_labels=_a				)
													lowerCamelCase       =      model(_a				)[0]
													self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)

													# Test that model correctly compute the loss with a tuple
													lowerCamelCase       =      self._prepare_for_class(inputs_dict.copy()	,				_a	,				return_labels=_a				)

													# Get keys that were added with the _prepare_for_class function
													lowerCamelCase       =      prepared_for_class.keys() - inputs_dict.keys()
													lowerCamelCase       =      inspect.signature(model.call				).parameters
													lowerCamelCase       =      list(signature.keys()				)

													# Create a dictionary holding the location of the tensors in the tuple
													lowerCamelCase       =      {0: """input_ids"""}
													for label_key in label_keys:
																lowerCamelCase       =      signature_names.index(_a				)
																lowerCamelCase       =      label_key
													lowerCamelCase       =      sorted(tuple_index_mapping.items()				)
													# Initialize a list with their default values, update the values and convert to a tuple
													lowerCamelCase       =      []

													for name in signature_names:
																if name != "kwargs":
																			list_input.append(signature[name].default				)

													for index, value in sorted_tuple_index_mapping:
																lowerCamelCase       =      prepared_for_class[value]

													lowerCamelCase       =      tuple(_a				)

													# Send to model
													lowerCamelCase       =      model(tuple_input[:-1]				)[0]

													self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							(
							    (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,          
							)       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(_a	,				_a	,				_a	,				_a	,				_a	,				_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							(
							    (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,          
							)       =      self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
										lowerCamelCase       =      type
										self.model_tester.create_and_check_model(_a	,				_a	,				_a	,				_a	,				_a	,				_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							(
							    (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,          
							)       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(
							    _a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							(
							    (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,          
							)       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(
							    _a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							(
							    (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,     (
							    lowerCamelCase     
							)   ,          
							)       =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(
							    _a	,				_a	,				_a	,				_a	,				_a	,				_a	,				_a				)





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase       =      TFLayoutLMvaModel.from_pretrained(_a				)
										self.assertIsNotNone(_a				)




def 					a__				(       )		->      Optional[int]:
			lowerCamelCase       =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
			return image






@require_tf
class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				@cached_property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return LayoutLMvaImageProcessor(apply_ocr=_a				) if is_vision_available() else None





				@slow
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""				)

							lowerCamelCase       =      self.default_image_processor
							lowerCamelCase       =      prepare_img()
							lowerCamelCase       =      image_processor(images=_a	,				return_tensors="""tf"""				).pixel_values

							lowerCamelCase       =      tf.constant([[1, 2]]				)
							lowerCamelCase       =      tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]				)	,				axis=0				)

							# forward pass
							lowerCamelCase       =      model(input_ids=_a	,				bbox=_a	,				pixel_values=_a	,				training=_a				)

							# verify the logits
							lowerCamelCase       =      (1, 199, 768)
							self.assertEqual(outputs.last_hidden_state.shape	,				_a				)

							lowerCamelCase       =      tf.constant(
							    [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]				)

							self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]	,				_a	,				atol=1e-4				)				)









"""simple docstring"""




from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowerCAmelCase							:    List[Any]  							=							{"""UserAgent""": UserAgent().random}



def 					a__				(       snake_case__       )		->      dict:
			lowerCamelCase       =      script.contents[0]
			lowerCamelCase       =      json.loads(data[data.find("""{\"config\""""       ) : -1]       )
			return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]






class 					__magic_name__							:






				'''simple docstring'''





				def __init__( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      f'https://www.instagram.com/{username}/'
							lowerCamelCase       =      self.get_json()





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      requests.get(self.url	,				headers=_a				).text
							lowerCamelCase       =      BeautifulSoup(_a	,				"""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      _lowerCAmelCase       ( self				):





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





				@property
				def      _lowerCAmelCase       ( self				):





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





				@property
				def      _lowerCAmelCase       ( self				):





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





				@property
				def      _lowerCAmelCase       ( self				):





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





				@property
				def      _lowerCAmelCase       ( self				):





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





				@property
				def      _lowerCAmelCase       ( self				):





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





				@property
				def      _lowerCAmelCase       ( self				):





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





				@property
				def      _lowerCAmelCase       ( self				):





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





				@property
				def      _lowerCAmelCase       ( self				):





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





				@property
				def      _lowerCAmelCase       ( self				):





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





				@property
				def      _lowerCAmelCase       ( self				):





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




def 					a__				(       snake_case__ = "github"       )		->      None:
			import os

			if os.environ.get("""CI"""       ):
						return  # test failing on GitHub Actions
			lowerCamelCase       =      InstagramUser(snake_case__       )
			assert instagram_user.user_data
			assert isinstance(instagram_user.user_data ,				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 > 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()
					lowerCAmelCase							:    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 = }""")



