


from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_snake_case  :	Any			      =       {"UserAgent": UserAgent().random}







def        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    Tuple											=     script.contents[0]
							__snake_case	:    List[str]											=     json.loads(data[data.find("{\"config\""			) : -1]			)
							return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]

class        a	:




							"""simple docstring"""


							def __init__(							self :						Any					,			lowerCamelCase :						str       )			->					Tuple:
														__snake_case	:    Union[str, Any]											=     F'https://www.instagram.com/{username}/'
														__snake_case	:    Dict											=     self.get_json()

							def 					__snake_case      (							self :						List[Any]       )			->					dict:
														__snake_case	:    Optional[Any]											=     requests.get(self.url					,			headers=lowerCamelCase       ).text
														__snake_case	:    Tuple											=     BeautifulSoup(lowerCamelCase					,			"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 :						Tuple       )			->					str:
														return F'{self.__class__.__name__}(\'{self.username}\')'

							def __str__(							self :						List[str]       )			->					str:
														return F'{self.fullname} ({self.username}) is {self.biography}'

							@property
							def 					__snake_case      (							self :						Optional[int]       )			->					str:
														return self.user_data["username"]

							@property
							def 					__snake_case      (							self :						Optional[Any]       )			->					str:
														return self.user_data["full_name"]

							@property
							def 					__snake_case      (							self :						Union[str, Any]       )			->					str:
														return self.user_data["biography"]

							@property
							def 					__snake_case      (							self :						Union[str, Any]       )			->					str:
														return self.user_data["business_email"]

							@property
							def 					__snake_case      (							self :						List[Any]       )			->					str:
														return self.user_data["external_url"]

							@property
							def 					__snake_case      (							self :						Optional[Any]       )			->					int:
														return self.user_data["edge_followed_by"]["count"]

							@property
							def 					__snake_case      (							self :						Dict       )			->					int:
														return self.user_data["edge_follow"]["count"]

							@property
							def 					__snake_case      (							self :						List[Any]       )			->					int:
														return self.user_data["edge_owner_to_timeline_media"]["count"]

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

							@property
							def 					__snake_case      (							self :						Tuple       )			->					bool:
														return self.user_data["is_verified"]






							@property
							def 					__snake_case      (							self :						Union[str, Any]       )			->					bool:
														return self.user_data["is_private"]







def        lowerCAmelCase_  (      __lowerCamelCase = "github"			):
							import os

							if os.environ.get("CI"			):
														return  # test failing on GitHub Actions
							__snake_case	:    List[str]											=     InstagramUser(__lowerCamelCase			)
							assert instagram_user.user_data
							assert isinstance(instagram_user.user_data							,			__lowerCamelCase			)
							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()
			_snake_case  :	Dict			      =       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 = }''')





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_snake_case  :	Dict			      =       {"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class        a	(unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							Union[str, Any]  						=						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							__UpperCAmelCase					:							Optional[int]  						=						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														__UpperCAmelCase					:							Optional[int]  						=						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														__UpperCAmelCase					:							Any  						=						{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}

							@require_torch
							def 					__snake_case      (							self :						str       )			->					str:
														__snake_case	:    Dict											=     pipeline(
														    task="text-classification"					,			model="hf-internal-testing/tiny-random-distilbert"					,			framework="pt"       )

														__snake_case	:    Optional[Any]											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

														__snake_case	:    Any											=     text_classifier("This is great !"					,			top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]       )

														__snake_case	:    int											=     text_classifier(["This is great !", "This is bad"]					,			top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														    ]					,			)

														__snake_case	:    List[str]											=     text_classifier("This is great !"					,			top_k=1       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

														# Legacy behavior
														__snake_case	:    Dict											=     text_classifier("This is great !"					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

														__snake_case	:    Any											=     text_classifier("This is great !"					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]       )

														__snake_case	:    Tuple											=     text_classifier(["This is great !", "Something else"]					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														        [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
														    ]					,			)

														__snake_case	:    Tuple											=     text_classifier(["This is great !", "Something else"]					,			return_all_scores=lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[
														        {"label": "LABEL_0", "score": 0.5_04},
														        {"label": "LABEL_0", "score": 0.5_04},
														    ]					,			)

							@require_torch
							def 					__snake_case      (							self :						Optional[int]       )			->					List[Any]:
														import torch

														__snake_case	:    Dict											=     pipeline(
														    task="text-classification"					,			model="hf-internal-testing/tiny-random-distilbert"					,			framework="pt"					,			device=torch.device("cpu"       )					,			)

														__snake_case	:    Optional[int]											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

							@require_tf
							def 					__snake_case      (							self :						Any       )			->					Tuple:
														__snake_case	:    List[Any]											=     pipeline(
														    task="text-classification"					,			model="hf-internal-testing/tiny-random-distilbert"					,			framework="tf"       )

														__snake_case	:    str											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "LABEL_0", "score": 0.5_04}]       )

							@slow
							@require_torch
							def 					__snake_case      (							self :						int       )			->					int:
														__snake_case	:    Dict											=     pipeline("text-classification"       )

														__snake_case	:    Dict											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 1.0}]       )
														__snake_case	:    int											=     text_classifier("This is bad !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "NEGATIVE", "score": 1.0}]       )
														__snake_case	:    str											=     text_classifier("Birds are a type of animal"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 0.9_88}]       )

							@slow
							@require_tf
							def 					__snake_case      (							self :						List[Any]       )			->					str:
														__snake_case	:    Optional[Any]											=     pipeline("text-classification"					,			framework="tf"       )

														__snake_case	:    Any											=     text_classifier("This is great !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 1.0}]       )
														__snake_case	:    Optional[int]											=     text_classifier("This is bad !"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "NEGATIVE", "score": 1.0}]       )
														__snake_case	:    Any											=     text_classifier("Birds are a type of animal"       )
														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": "POSITIVE", "score": 0.9_88}]       )

							def 					__snake_case      (							self :						str					,			lowerCamelCase :						Tuple					,			lowerCamelCase :						Any					,			lowerCamelCase :						Union[str, Any]       )			->					List[Any]:
														__snake_case	:    Union[str, Any]											=     TextClassificationPipeline(model=lowerCamelCase					,			tokenizer=lowerCamelCase       )
														return text_classifier, ["HuggingFace is in", "This is another test"]






							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Tuple       )			->					str:
														__snake_case	:    Tuple											=     text_classifier.model
														# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
														__snake_case	:    Any											=     "HuggingFace is in"
														__snake_case	:    Tuple											=     text_classifier(lowerCamelCase       )

														self.assertEqual(nested_simplify(lowerCamelCase       )					,			[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}]       )
														self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )

														__snake_case	:    Union[str, Any]											=     ["HuggingFace is in ", "Paris is in France"]
														__snake_case	:    Union[str, Any]											=     text_classifier(lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}, {"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}]					,			)
														self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )
														self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()       )

														# Forcing to get all results with `top_k=None`
														# This is NOT the legacy format
														__snake_case	:    str											=     text_classifier(lowerCamelCase					,			top_k=lowerCamelCase       )
														__snake_case	:    int											=     len(model.config.idalabel.values()       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}] * N, [{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}] * N]					,			)

														__snake_case	:    Optional[int]											=     {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
														__snake_case	:    Optional[Any]											=     text_classifier(lowerCamelCase       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}					,			)
														self.assertTrue(outputs["label"] in model.config.idalabel.values()       )

														# This might be used a text pair, but tokenizer + pipe interaction
														# makes it hard to understand that it's not using the pair properly
														# https://github.com/huggingface/transformers/issues/17305
														# We disabled this usage instead as it was outputting wrong outputs.
														__snake_case	:    Any											=     [["HuggingFace is in ", "Paris is in France"]]
														with self.assertRaises(lowerCamelCase       ):
																					text_classifier(lowerCamelCase       )

														# This used to be valid for doing text pairs
														# We're keeping it working because of backward compatibility
														__snake_case	:    List[Any]											=     text_classifier([[["HuggingFace is in ", "Paris is in France"]]]       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase       )					,			[{"label": ANY(lowerCamelCase       ), "score": ANY(lowerCamelCase       )}]					,			)
														self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()       )

