


'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase__					=   logging.get_logger(__name__)


if is_vision_available():
						import PIL






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Dict				      =   ["pixel_values"]




			def __init__(						self     :   Tuple		,     lowerCamelCase__     :   bool = True		,     lowerCamelCase__     :   Dict[str, int] = None		,     lowerCamelCase__     :   PILImageResampling = PILImageResampling.BICUBIC		,     lowerCamelCase__     :   bool = True		,     lowerCamelCase__     :   Dict[str, int] = None		,     lowerCamelCase__     :   bool = True		,     lowerCamelCase__     :   Union[int, float] = 1 / 2_55		,     lowerCamelCase__     :   bool = True		,     lowerCamelCase__     :   Optional[Union[float, List[float]]] = None		,     lowerCamelCase__     :   Optional[Union[float, List[float]]] = None		,     lowerCamelCase__     :   bool = True		,     **lowerCamelCase__     :   int		,     )      ->None:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__       )
					_UpperCAmelCase						:				int	    =					size if size is not None else {"shortest_edge": 2_24}
					_UpperCAmelCase						:				List[Any]	    =					get_size_dict(lowerCamelCase__		,     default_to_square=lowerCamelCase__       )
					_UpperCAmelCase						:				Optional[int]	    =					crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
					_UpperCAmelCase						:				Dict	    =					get_size_dict(lowerCamelCase__		,     default_to_square=lowerCamelCase__		,     param_name="crop_size"       )

					_UpperCAmelCase						:				Optional[int]	    =					do_resize
					_UpperCAmelCase						:				List[Any]	    =					size
					_UpperCAmelCase						:				Dict	    =					resample
					_UpperCAmelCase						:				Union[str, Any]	    =					do_center_crop
					_UpperCAmelCase						:				Optional[int]	    =					crop_size
					_UpperCAmelCase						:				Any	    =					do_rescale
					_UpperCAmelCase						:				List[Any]	    =					rescale_factor
					_UpperCAmelCase						:				Union[str, Any]	    =					do_normalize
					_UpperCAmelCase						:				Any	    =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					_UpperCAmelCase						:				Any	    =					image_std if image_std is not None else OPENAI_CLIP_STD
					_UpperCAmelCase						:				Tuple	    =					do_convert_rgb




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   Dict[str, int]		,     lowerCamelCase__     :   PILImageResampling = PILImageResampling.BICUBIC		,     lowerCamelCase__     :   Optional[Union[str, ChannelDimension]] = None		,     **lowerCamelCase__     :   List[str]		,     )      ->np.ndarray:



					'''simple docstring'''







					_UpperCAmelCase						:				Tuple	    =					get_size_dict(lowerCamelCase__		,     default_to_square=lowerCamelCase__       )
					if "shortest_edge" not in size:
							raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""       )
					_UpperCAmelCase						:				List[str]	    =					get_resize_output_image_size(lowerCamelCase__		,     size=size["shortest_edge"]		,     default_to_square=lowerCamelCase__       )
					return resize(lowerCamelCase__		,     size=lowerCamelCase__		,     resample=lowerCamelCase__		,     data_format=lowerCamelCase__		,     **lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   Dict[str, int]		,     lowerCamelCase__     :   Optional[Union[str, ChannelDimension]] = None		,     **lowerCamelCase__     :   Any		,     )      ->np.ndarray:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					get_size_dict(lowerCamelCase__       )
					if "height" not in size or "width" not in size:
							raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""       )
					return center_crop(lowerCamelCase__		,     size=(size["height"], size["width"])		,     data_format=lowerCamelCase__		,     **lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   Union[int, float]		,     lowerCamelCase__     :   Optional[Union[str, ChannelDimension]] = None		,     **lowerCamelCase__     :   List[Any]		,     )      ->List[str]:



					'''simple docstring'''







					return rescale(lowerCamelCase__		,     scale=lowerCamelCase__		,     data_format=lowerCamelCase__		,     **lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Optional[int]		,     lowerCamelCase__     :   np.ndarray		,     lowerCamelCase__     :   Union[float, List[float]]		,     lowerCamelCase__     :   Union[float, List[float]]		,     lowerCamelCase__     :   Optional[Union[str, ChannelDimension]] = None		,     **lowerCamelCase__     :   Union[str, Any]		,     )      ->np.ndarray:



					'''simple docstring'''







					return normalize(lowerCamelCase__		,     mean=lowerCamelCase__		,     std=lowerCamelCase__		,     data_format=lowerCamelCase__		,     **lowerCamelCase__       )






			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   ImageInput		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   Dict[str, int] = None		,     lowerCamelCase__     :   PILImageResampling = None		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   int = None		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   float = None		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   Optional[Union[float, List[float]]] = None		,     lowerCamelCase__     :   Optional[Union[float, List[float]]] = None		,     lowerCamelCase__     :   bool = None		,     lowerCamelCase__     :   Optional[Union[str, TensorType]] = None		,     lowerCamelCase__     :   Optional[ChannelDimension] = ChannelDimension.FIRST		,     **lowerCamelCase__     :   str		,     )      ->PIL.Image.Image:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					do_resize if do_resize is not None else self.do_resize
					_UpperCAmelCase						:				List[Any]	    =					size if size is not None else self.size
					_UpperCAmelCase						:				int	    =					get_size_dict(lowerCamelCase__		,     param_name="size"		,     default_to_square=lowerCamelCase__       )
					_UpperCAmelCase						:				Any	    =					resample if resample is not None else self.resample
					_UpperCAmelCase						:				Optional[int]	    =					do_center_crop if do_center_crop is not None else self.do_center_crop
					_UpperCAmelCase						:				List[Any]	    =					crop_size if crop_size is not None else self.crop_size
					_UpperCAmelCase						:				Tuple	    =					get_size_dict(lowerCamelCase__		,     param_name="crop_size"		,     default_to_square=lowerCamelCase__       )
					_UpperCAmelCase						:				str	    =					do_rescale if do_rescale is not None else self.do_rescale
					_UpperCAmelCase						:				List[str]	    =					rescale_factor if rescale_factor is not None else self.rescale_factor
					_UpperCAmelCase						:				Dict	    =					do_normalize if do_normalize is not None else self.do_normalize
					_UpperCAmelCase						:				str	    =					image_mean if image_mean is not None else self.image_mean
					_UpperCAmelCase						:				Tuple	    =					image_std if image_std is not None else self.image_std
					_UpperCAmelCase						:				Optional[int]	    =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					_UpperCAmelCase						:				str	    =					make_list_of_images(lowerCamelCase__       )

					if not valid_images(lowerCamelCase__       ):
							raise ValueError(
							    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
							    "torch.Tensor, tf.Tensor or jax.ndarray."       )

					if do_resize and size is None:
							raise ValueError("Size must be specified if do_resize is True."       )

					if do_center_crop and crop_size is None:
							raise ValueError("Crop size must be specified if do_center_crop is True."       )

					if do_rescale and rescale_factor is None:
							raise ValueError("Rescale factor must be specified if do_rescale is True."       )

					if do_normalize and (image_mean is None or image_std is None):
							raise ValueError("Image mean and std must be specified if do_normalize is True."       )

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
							_UpperCAmelCase						:				Optional[Any]	    =					[convert_to_rgb(lowerCamelCase__       ) for image in images]

					# All transformations expect numpy arrays.
					_UpperCAmelCase						:				Optional[int]	    =					[to_numpy_array(lowerCamelCase__       ) for image in images]

					if do_resize:
							_UpperCAmelCase						:				int	    =					[self.resize(image=lowerCamelCase__		,     size=lowerCamelCase__		,     resample=lowerCamelCase__       ) for image in images]

					if do_center_crop:
							_UpperCAmelCase						:				Dict	    =					[self.center_crop(image=lowerCamelCase__		,     size=lowerCamelCase__       ) for image in images]

					if do_rescale:
							_UpperCAmelCase						:				List[Any]	    =					[self.rescale(image=lowerCamelCase__		,     scale=lowerCamelCase__       ) for image in images]

					if do_normalize:
							_UpperCAmelCase						:				Optional[Any]	    =					[self.normalize(image=lowerCamelCase__		,     mean=lowerCamelCase__		,     std=lowerCamelCase__       ) for image in images]

					_UpperCAmelCase						:				Optional[int]	    =					[to_channel_dimension_format(lowerCamelCase__		,     lowerCamelCase__       ) for image in images]

					_UpperCAmelCase						:				Any	    =					{"pixel_values": images}
					return BatchFeature(data=lowerCamelCase__		,     tensor_type=lowerCamelCase__       )







'''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       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				List[str]	    =					script.contents[0]
		_UpperCAmelCase						:				Any	    =					json.loads(data[data.find("{\"config\""      ) : -1]      )
		return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]






class 		lowerCAmelCase__	:




			def __init__(						self     :   str		,     lowerCamelCase__     :   List[Any]       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					F"""https://www.instagram.com/{username}/"""
					_UpperCAmelCase						:				Dict	    =					self.get_json()




			def 							lowerCAmelCase__			(						self     :   Any       )      ->dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					requests.get(self.url		,     headers=lowerCamelCase__       ).text
					_UpperCAmelCase						:				Any	    =					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     :   Optional[Any]       )      ->str:



					'''simple docstring'''







					return F"""{self.__class__.__name__}('{self.username}')"""




			def __str__(						self     :   List[Any]       )      ->str:



					'''simple docstring'''







					return F"""{self.fullname} ({self.username}) is {self.biography}"""




			@property
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->str:



					'''simple docstring'''







					return self.user_data["username"]




			@property
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->str:



					'''simple docstring'''







					return self.user_data["full_name"]




			@property
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->str:



					'''simple docstring'''







					return self.user_data["biography"]




			@property
			def 							lowerCAmelCase__			(						self     :   int       )      ->str:



					'''simple docstring'''







					return self.user_data["business_email"]




			@property
			def 							lowerCAmelCase__			(						self     :   str       )      ->str:



					'''simple docstring'''







					return self.user_data["external_url"]




			@property
			def 							lowerCAmelCase__			(						self     :   Tuple       )      ->int:



					'''simple docstring'''







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




			@property
			def 							lowerCAmelCase__			(						self     :   str       )      ->int:



					'''simple docstring'''







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




			@property
			def 							lowerCAmelCase__			(						self     :   Any       )      ->int:



					'''simple docstring'''







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




			@property
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->str:



					'''simple docstring'''







					return self.user_data["profile_pic_url_hd"]




			@property
			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->bool:



					'''simple docstring'''







					return self.user_data["is_verified"]






			@property
			def 							lowerCAmelCase__			(						self     :   int       )      ->bool:



					'''simple docstring'''







					return self.user_data["is_private"]






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

		if os.environ.get("CI"      ):
				return  # test failing on GitHub Actions
		_UpperCAmelCase						:				Dict	    =					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 > 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 = }''')



