




'''simple docstring'''






from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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

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



class 					A		:




				'''simple docstring'''





				def __init__(self ,  _UpperCAmelCase			)	->       Any:
							__UpperCamelCase      :			Union[str, Any]									=   f"https://www.instagram.com/{username}/"
							__UpperCamelCase      :			Optional[Any]									=   self.get_json()
				def        a_       (self			)	->       dict:
							__UpperCamelCase      :			Optional[int]									=   requests.get(self.url ,  headers=__lowerCAmelCase			).text
							__UpperCamelCase      :			str									=   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			)	->       str:
							return f"{self.__class__.__name__}(\'{self.username}\')"
				def __str__(self			)	->       str:
							return f"{self.fullname} ({self.username}) is {self.biography}"
				@property
				def        a_       (self			)	->       str:
							return self.user_data["username"]
				@property
				def        a_       (self			)	->       str:
							return self.user_data["full_name"]
				@property
				def        a_       (self			)	->       str:
							return self.user_data["biography"]
				@property
				def        a_       (self			)	->       str:
							return self.user_data["business_email"]
				@property
				def        a_       (self			)	->       str:
							return self.user_data["external_url"]
				@property
				def        a_       (self			)	->       int:
							return self.user_data["edge_followed_by"]["count"]
				@property
				def        a_       (self			)	->       int:
							return self.user_data["edge_follow"]["count"]
				@property
				def        a_       (self			)	->       int:
							return self.user_data["edge_owner_to_timeline_media"]["count"]
				@property
				def        a_       (self			)	->       str:
							return self.user_data["profile_pic_url_hd"]
				@property
				def        a_       (self			)	->       bool:
							return self.user_data["is_verified"]





				@property
				def        a_       (self			)	->       bool:
							return self.user_data["is_private"]







def 			__lowerCAmelCase					(    snake_case__ = "github"						):
			import os

			if os.environ.get("CI"						):
						return  # test failing on GitHub Actions
			__UpperCamelCase      :			Optional[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 > 150
			assert instagram_user.number_of_followers > 120_000
			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      	=					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 json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class   A			(unittest.TestCase    ):
			'''simple docstring'''




			def    a_       (			self   :    Union[str, Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   tempfile.mkdtemp()

					# fmt: off
					A__        =   ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
					# fmt: on
					A__        =   dict(zip(__lowerCAmelCase							,	range(len(__lowerCAmelCase   )   )   )   )
					A__        =   ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
					A__        =   {"""unk_token""": """<unk>"""}

					A__        =   os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES["""vocab_file"""]   )
					A__        =   os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES["""merges_file"""]   )
					with open(self.vocab_file							,	"""w"""							,	encoding="""utf-8"""   ) as fp:
							fp.write(json.dumps(__lowerCAmelCase   ) + """\n"""   )
					with open(self.merges_file							,	"""w"""							,	encoding="""utf-8"""   ) as fp:
							fp.write("""\n""".join(__lowerCAmelCase   )   )

					A__        =   {
					    """do_resize""": True,
					    """size""": 20,
					    """do_center_crop""": True,
					    """crop_size""": 18,
					    """do_normalize""": True,
					    """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
					    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
					}
					A__        =   os.path.join(self.tmpdirname							,	__lowerCAmelCase   )
					with open(self.image_processor_file							,	"""w"""							,	encoding="""utf-8"""   ) as fp:
							json.dump(__lowerCAmelCase							,	__lowerCAmelCase   )






			def    a_       (			self   :    Tuple							,	**__lowerCAmelCase   :    Dict   )	->	str:







					"""simple docstring"""






					return CLIPTokenizer.from_pretrained(self.tmpdirname							,	**__lowerCAmelCase   )






			def    a_       (			self   :    Union[str, Any]							,	**__lowerCAmelCase   :    Dict   )	->	List[str]:







					"""simple docstring"""






					return CLIPTokenizerFast.from_pretrained(self.tmpdirname							,	**__lowerCAmelCase   )






			def    a_       (			self   :    List[str]							,	**__lowerCAmelCase   :    Optional[Any]   )	->	Dict:







					"""simple docstring"""






					return CLIPImageProcessor.from_pretrained(self.tmpdirname							,	**__lowerCAmelCase   )






			def    a_       (			self   :    str   )	->	Dict:







					"""simple docstring"""






					shutil.rmtree(self.tmpdirname   )






			def    a_       (			self   :    str   )	->	Any:







					"""simple docstring"""






					A__        =   [np.random.randint(2_55							,	size=(3, 30, 4_00)							,	dtype=np.uinta   )]

					A__        =   [Image.fromarray(np.moveaxis(__lowerCAmelCase							,	0							,	-1   )   ) for x in image_inputs]

					return image_inputs






			def    a_       (			self   :    Optional[int]   )	->	Tuple:







					"""simple docstring"""






					A__        =   self.get_tokenizer()
					A__        =   self.get_rust_tokenizer()
					A__        =   self.get_image_processor()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )
					processor_slow.save_pretrained(self.tmpdirname   )
					A__        =   CLIPProcessor.from_pretrained(self.tmpdirname							,	use_fast=__lowerCAmelCase   )

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )
					processor_fast.save_pretrained(self.tmpdirname   )
					A__        =   CLIPProcessor.from_pretrained(self.tmpdirname   )

					self.assertEqual(processor_slow.tokenizer.get_vocab()							,	tokenizer_slow.get_vocab()   )
					self.assertEqual(processor_fast.tokenizer.get_vocab()							,	tokenizer_fast.get_vocab()   )
					self.assertEqual(tokenizer_slow.get_vocab()							,	tokenizer_fast.get_vocab()   )
					self.assertIsInstance(processor_slow.tokenizer							,	__lowerCAmelCase   )
					self.assertIsInstance(processor_fast.tokenizer							,	__lowerCAmelCase   )

					self.assertEqual(processor_slow.image_processor.to_json_string()							,	image_processor.to_json_string()   )
					self.assertEqual(processor_fast.image_processor.to_json_string()							,	image_processor.to_json_string()   )
					self.assertIsInstance(processor_slow.image_processor							,	__lowerCAmelCase   )
					self.assertIsInstance(processor_fast.image_processor							,	__lowerCAmelCase   )






			def    a_       (			self   :    Optional[Any]   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   CLIPProcessor(tokenizer=self.get_tokenizer()							,	image_processor=self.get_image_processor()   )
					processor.save_pretrained(self.tmpdirname   )

					A__        =   self.get_tokenizer(bos_token="""(BOS)"""							,	eos_token="""(EOS)"""   )
					A__        =   self.get_image_processor(do_normalize=__lowerCAmelCase							,	padding_value=1.0   )

					A__        =   CLIPProcessor.from_pretrained(
					    self.tmpdirname							,	bos_token="""(BOS)"""							,	eos_token="""(EOS)"""							,	do_normalize=__lowerCAmelCase							,	padding_value=1.0   )

					self.assertEqual(processor.tokenizer.get_vocab()							,	tokenizer_add_kwargs.get_vocab()   )
					self.assertIsInstance(processor.tokenizer							,	__lowerCAmelCase   )

					self.assertEqual(processor.image_processor.to_json_string()							,	image_processor_add_kwargs.to_json_string()   )
					self.assertIsInstance(processor.image_processor							,	__lowerCAmelCase   )






			def    a_       (			self   :    List[Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   self.prepare_image_inputs()

					A__        =   image_processor(__lowerCAmelCase							,	return_tensors="""np"""   )
					A__        =   processor(images=__lowerCAmelCase							,	return_tensors="""np"""   )

					for key in input_image_proc.keys():
							self.assertAlmostEqual(input_image_proc[key].sum()							,	input_processor[key].sum()							,	delta=1e-2   )






			def    a_       (			self   :    Optional[Any]   )	->	Any:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   """lower newer"""

					A__        =   processor(text=__lowerCAmelCase   )

					A__        =   tokenizer(__lowerCAmelCase   )

					for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]							,	encoded_processor[key]   )






			def    a_       (			self   :    Union[str, Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   """lower newer"""
					A__        =   self.prepare_image_inputs()

					A__        =   processor(text=__lowerCAmelCase							,	images=__lowerCAmelCase   )

					self.assertListEqual(list(inputs.keys()   )							,	["""input_ids""", """attention_mask""", """pixel_values"""]   )

					# test if it raises when no input is passed
					with pytest.raises(__lowerCAmelCase   ):
							processor()






			def    a_       (			self   :    Tuple   )	->	str:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A__        =   processor.batch_decode(__lowerCAmelCase   )
					A__        =   tokenizer.batch_decode(__lowerCAmelCase   )

					self.assertListEqual(__lowerCAmelCase							,	__lowerCAmelCase   )







			def    a_       (			self   :    Optional[int]   )	->	str:







					"""simple docstring"""






					A__        =   self.get_image_processor()
					A__        =   self.get_tokenizer()

					A__        =   CLIPProcessor(tokenizer=__lowerCAmelCase							,	image_processor=__lowerCAmelCase   )

					A__        =   """lower newer"""
					A__        =   self.prepare_image_inputs()

					A__        =   processor(text=__lowerCAmelCase							,	images=__lowerCAmelCase   )

					self.assertListEqual(list(inputs.keys()   )							,	processor.model_input_names   )



