



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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


UpperCAmelCase_									=							logging.get_logger(__name__)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Tuple				)->List[List[ImageInput]]:
      if isinstance(lowerCamelCase__     ,				(list, tuple)				) and isinstance(videos[0]     ,				(list, tuple)				) and is_valid_image(videos[0][0]				):
            return videos

      elif isinstance(lowerCamelCase__     ,				(list, tuple)				) and is_valid_image(videos[0]				):
            return [videos]

      elif is_valid_image(lowerCamelCase__				):
            return [[videos]]

      raise ValueError(f'''Could not make batched video from {videos}'''				)





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     ["""pixel_values"""]





   def __init__(	self       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = PILImageResampling.BILINEAR       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = 1 / 255       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):
         super().__init__(**_lowerCAmelCase			)
         _lowerCAmelCase 			=  size if size is not None else {'''shortest_edge''': 224}
         _lowerCAmelCase 			=  get_size_dict(_lowerCAmelCase       ,     default_to_square=_lowerCAmelCase			)
         _lowerCAmelCase 			=  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         _lowerCAmelCase 			=  get_size_dict(_lowerCAmelCase       ,     param_name='''crop_size'''			)

         _lowerCAmelCase 			=  do_resize
         _lowerCAmelCase 			=  size
         _lowerCAmelCase 			=  do_center_crop
         _lowerCAmelCase 			=  crop_size
         _lowerCAmelCase 			=  resample
         _lowerCAmelCase 			=  do_rescale
         _lowerCAmelCase 			=  rescale_factor
         _lowerCAmelCase 			=  do_normalize
         _lowerCAmelCase 			=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         _lowerCAmelCase 			=  image_std if image_std is not None else IMAGENET_STANDARD_STD





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = PILImageResampling.BILINEAR       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  get_size_dict(_lowerCAmelCase       ,     default_to_square=_lowerCAmelCase			)
         if "shortest_edge" in size:
               _lowerCAmelCase 			=  get_resize_output_image_size(_lowerCAmelCase       ,     size['''shortest_edge''']       ,     default_to_square=_lowerCAmelCase			)
         elif "height" in size and "width" in size:
               _lowerCAmelCase 			=  (size['''height'''], size['''width'''])
         else:
               raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''			)
         return resize(_lowerCAmelCase       ,     size=_lowerCAmelCase       ,     resample=_lowerCAmelCase       ,     data_format=_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  get_size_dict(_lowerCAmelCase			)
         if "height" not in size or "width" not in size:
               raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''			)
         return center_crop(_lowerCAmelCase       ,     size=(size['''height'''], size['''width'''])       ,     data_format=_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):
         return rescale(_lowerCAmelCase       ,     scale=_lowerCAmelCase       ,     data_format=_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):
         return normalize(_lowerCAmelCase       ,     mean=_lowerCAmelCase       ,     std=_lowerCAmelCase       ,     data_format=_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = ChannelDimension.FIRST       ,     ):

         if do_resize and size is None or resample is None:
               raise ValueError('''Size and resample 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.'''			)

         # All transformations expect numpy arrays.
         _lowerCAmelCase 			=  to_numpy_array(_lowerCAmelCase			)

         if do_resize:
               _lowerCAmelCase 			=  self.resize(image=_lowerCAmelCase       ,     size=_lowerCAmelCase       ,     resample=_lowerCAmelCase			)

         if do_center_crop:
               _lowerCAmelCase 			=  self.center_crop(_lowerCAmelCase       ,     size=_lowerCAmelCase			)

         if do_rescale:
               _lowerCAmelCase 			=  self.rescale(image=_lowerCAmelCase       ,     scale=_lowerCAmelCase			)

         if do_normalize:
               _lowerCAmelCase 			=  self.normalize(image=_lowerCAmelCase       ,     mean=_lowerCAmelCase       ,     std=_lowerCAmelCase			)

         _lowerCAmelCase 			=  to_channel_dimension_format(_lowerCAmelCase       ,     _lowerCAmelCase			)
         return image





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = ChannelDimension.FIRST       ,     **_lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  do_resize if do_resize is not None else self.do_resize
         _lowerCAmelCase 			=  resample if resample is not None else self.resample
         _lowerCAmelCase 			=  do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowerCAmelCase 			=  do_rescale if do_rescale is not None else self.do_rescale
         _lowerCAmelCase 			=  rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowerCAmelCase 			=  do_normalize if do_normalize is not None else self.do_normalize
         _lowerCAmelCase 			=  image_mean if image_mean is not None else self.image_mean
         _lowerCAmelCase 			=  image_std if image_std is not None else self.image_std

         _lowerCAmelCase 			=  size if size is not None else self.size
         _lowerCAmelCase 			=  get_size_dict(_lowerCAmelCase       ,     default_to_square=_lowerCAmelCase			)
         _lowerCAmelCase 			=  crop_size if crop_size is not None else self.crop_size
         _lowerCAmelCase 			=  get_size_dict(_lowerCAmelCase       ,     param_name='''crop_size'''			)

         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.'''			)

         _lowerCAmelCase 			=  make_batched(_lowerCAmelCase			)

         _lowerCAmelCase 			=  [
             [
                 self._preprocess_image(
                     image=_lowerCAmelCase       ,     do_resize=_lowerCAmelCase       ,     size=_lowerCAmelCase       ,     resample=_lowerCAmelCase       ,     do_center_crop=_lowerCAmelCase       ,     crop_size=_lowerCAmelCase       ,     do_rescale=_lowerCAmelCase       ,     rescale_factor=_lowerCAmelCase       ,     do_normalize=_lowerCAmelCase       ,     image_mean=_lowerCAmelCase       ,     image_std=_lowerCAmelCase       ,     data_format=_lowerCAmelCase       ,     )
                 for img in video
             ]
             for video in videos
         ]

         _lowerCAmelCase 			=  {'''pixel_values''': videos}
         return BatchFeature(data=_lowerCAmelCase       ,     tensor_type=_lowerCAmelCase			)




from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase_									=							{"UserAgent": UserAgent().random}
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Dict				)->dict:
      _lowerCAmelCase 			=  script.contents[0]
      _lowerCAmelCase 			=  json.loads(data[data.find('''{"config"'''				) : -1]				)
      return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]





class 		UpperCAmelCase						:





   def __init__(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  F'''https://www.instagram.com/{username}/'''
         _lowerCAmelCase 			=  self.get_json()





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  requests.get(self.url       ,     headers=_lowerCAmelCase			).text
         _lowerCAmelCase 			=  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			):
         return F'''{self.__class__.__name__}(\'{self.username}\')'''





   def __str__(	self			):
         return F'''{self.fullname} ({self.username}) is {self.biography}'''





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["username"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["full_name"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["biography"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["business_email"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["external_url"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["edge_followed_by"]["count"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["edge_follow"]["count"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["edge_owner_to_timeline_media"]["count"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["profile_pic_url_hd"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["is_verified"]





   @property
   def 	__lowerCAmelCase							(	self			):
         return self.user_data["is_private"]






def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str = "github"				)->None:
      import os

      if os.environ.get('''CI'''				):
            return  # test failing on GitHub Actions
      _lowerCAmelCase 			=  InstagramUser(_SCREAMING_SNAKE_CASE				)
      assert instagram_user.user_data
      assert isinstance(instagram_user.user_data     ,				_SCREAMING_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_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()
    UpperCAmelCase_									=							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 = }""")