"""simple docstring"""





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import YolosImageProcessor


class 	UpperCAmelCase_      (       unittest.TestCase):


      def __init__(  self						,     a						,     a=7						,     a=3						,     a=3_0						,     a=4_0_0						,     a=True						,     a=None						,     a=True						,     a=[0.5, 0.5, 0.5]						,     a=[0.5, 0.5, 0.5]						,     a=True						,     a=1 / 2_5_5						,     a=True						,     )			->   Optional[Any]:
             # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
             lowercase__     :						List[Any]         =      size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
             lowercase__     :						Optional[int]         =      parent
             lowercase__     :						Optional[int]         =      batch_size
             lowercase__     :						Tuple         =      num_channels
             lowercase__     :						List[str]         =      min_resolution
             lowercase__     :						Optional[Any]         =      max_resolution
             lowercase__     :						Optional[int]         =      do_resize
             lowercase__     :						Optional[int]         =      size
             lowercase__     :						Optional[int]         =      do_normalize
             lowercase__     :						Optional[Any]         =      image_mean
             lowercase__     :						Optional[int]         =      image_std
             lowercase__     :						List[str]         =      do_rescale
             lowercase__     :						Tuple         =      rescale_factor
             lowercase__     :						Optional[Any]         =      do_pad


      def 						_UpperCAmelCase			(  self				)			->   int:
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_rescale": self.do_rescale,
                 "rescale_factor": self.rescale_factor,
                 "do_pad": self.do_pad,
             }


      def 						_UpperCAmelCase			(  self						,     a						,     a=False				)			->   List[str]:
             if not batched:
                    lowercase__     :						List[Any]         =      image_inputs[0]
                    if isinstance(__lowerCAmelCase						,     Image.Image				):
                           lowercase__       , lowercase__     :						List[str]         =      image.size
                    else:
                           lowercase__       , lowercase__     :						Optional[Any]         =      image.shape[1], image.shape[2]
                    if w < h:
                           lowercase__     :						Any         =      int(self.size['shortest_edge'] * h / w				)
                           lowercase__     :						str         =      self.size['shortest_edge']
                    elif w > h:
                           lowercase__     :						List[str]         =      self.size['shortest_edge']
                           lowercase__     :						Union[str, Any]         =      int(self.size['shortest_edge'] * w / h				)
                    else:
                           lowercase__     :						str         =      self.size['shortest_edge']
                           lowercase__     :						Optional[int]         =      self.size['shortest_edge']

             else:
                    lowercase__     :						str         =      []
                    for image in image_inputs:
                           lowercase__       , lowercase__     :						List[str]         =      self.get_expected_values([image]				)
                           expected_values.append((expected_height, expected_width)				)
                    lowercase__     :						List[Any]         =      max(__lowerCAmelCase						,     key=lambda a				: item[0]				)[0]
                    lowercase__     :						Dict         =      max(__lowerCAmelCase						,     key=lambda a				: item[1]				)[1]

             return expected_height, expected_width





@require_torch
@require_vision
class 	UpperCAmelCase_      (       lowerCAmelCase_					,				unittest.TestCase):
      lowerCamelCase__						:       Union[str, Any]					    =    YolosImageProcessor if is_vision_available() else None


      def 						_UpperCAmelCase			(  self				)			->   Tuple:
             lowercase__     :						List[Any]         =      YolosImageProcessingTester(self				)


      @property
      def 						_UpperCAmelCase			(  self				)			->   int:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 						_UpperCAmelCase			(  self				)			->   Optional[int]:
             lowercase__     :						Optional[int]         =      self.image_processing_class(**self.image_processor_dict				)
             self.assertTrue(hasattr(__lowerCAmelCase						,     'image_mean'				)				)
             self.assertTrue(hasattr(__lowerCAmelCase						,     'image_std'				)				)
             self.assertTrue(hasattr(__lowerCAmelCase						,     'do_normalize'				)				)
             self.assertTrue(hasattr(__lowerCAmelCase						,     'do_resize'				)				)
             self.assertTrue(hasattr(__lowerCAmelCase						,     'size'				)				)


      def 						_UpperCAmelCase			(  self				)			->   Tuple:
             lowercase__     :						str         =      self.image_processing_class.from_dict(self.image_processor_dict				)
             self.assertEqual(image_processor.size						,     {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}				)
             self.assertEqual(image_processor.do_pad						,     __lowerCAmelCase				)

             lowercase__     :						str         =      self.image_processing_class.from_dict(
                 self.image_processor_dict						,     size=4_2						,     max_size=8_4						,     pad_and_return_pixel_mask=__lowerCAmelCase				)
             self.assertEqual(image_processor.size						,     {'shortest_edge': 4_2, 'longest_edge': 8_4}				)
             self.assertEqual(image_processor.do_pad						,     __lowerCAmelCase				)


      def 						_UpperCAmelCase			(  self				)			->   int:
             pass


      def 						_UpperCAmelCase			(  self				)			->   int:
             # Initialize image_processing
             lowercase__     :						int         =      self.image_processing_class(**self.image_processor_dict				)
             # create random PIL images
             lowercase__     :						Union[str, Any]         =      prepare_image_inputs(self.image_processor_tester						,     equal_resolution=__lowerCAmelCase				)
             for image in image_inputs:
                    self.assertIsInstance(__lowerCAmelCase						,     Image.Image				)

             # Test not batched input
             lowercase__     :						List[str]         =      image_processing(image_inputs[0]						,     return_tensors='pt'				).pixel_values

             lowercase__       , lowercase__     :						Optional[Any]         =      self.image_processor_tester.get_expected_values(__lowerCAmelCase				)

             self.assertEqual(
                 encoded_images.shape						,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,     )

             # Test batched
             lowercase__       , lowercase__     :						Optional[Any]         =      self.image_processor_tester.get_expected_values(__lowerCAmelCase						,     batched=__lowerCAmelCase				)

             lowercase__     :						Union[str, Any]         =      image_processing(__lowerCAmelCase						,     return_tensors='pt'				).pixel_values
             self.assertEqual(
                 encoded_images.shape						,     (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )						,     )


      def 						_UpperCAmelCase			(  self				)			->   Tuple:
             # Initialize image_processing
             lowercase__     :						Optional[int]         =      self.image_processing_class(**self.image_processor_dict				)
             # create random numpy tensors
             lowercase__     :						Dict         =      prepare_image_inputs(self.image_processor_tester						,     equal_resolution=__lowerCAmelCase						,     numpify=__lowerCAmelCase				)
             for image in image_inputs:
                    self.assertIsInstance(__lowerCAmelCase						,     np.ndarray				)

             # Test not batched input
             lowercase__     :						List[Any]         =      image_processing(image_inputs[0]						,     return_tensors='pt'				).pixel_values

             lowercase__       , lowercase__     :						List[str]         =      self.image_processor_tester.get_expected_values(__lowerCAmelCase				)

             self.assertEqual(
                 encoded_images.shape						,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,     )

             # Test batched
             lowercase__     :						Union[str, Any]         =      image_processing(__lowerCAmelCase						,     return_tensors='pt'				).pixel_values

             lowercase__       , lowercase__     :						Optional[Any]         =      self.image_processor_tester.get_expected_values(__lowerCAmelCase						,     batched=__lowerCAmelCase				)

             self.assertEqual(
                 encoded_images.shape						,     (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )						,     )


      def 						_UpperCAmelCase			(  self				)			->   Tuple:
             # Initialize image_processing
             lowercase__     :						int         =      self.image_processing_class(**self.image_processor_dict				)
             # create random PyTorch tensors
             lowercase__     :						Optional[Any]         =      prepare_image_inputs(self.image_processor_tester						,     equal_resolution=__lowerCAmelCase						,     torchify=__lowerCAmelCase				)
             for image in image_inputs:
                    self.assertIsInstance(__lowerCAmelCase						,     torch.Tensor				)

             # Test not batched input
             lowercase__     :						Optional[Any]         =      image_processing(image_inputs[0]						,     return_tensors='pt'				).pixel_values

             lowercase__       , lowercase__     :						str         =      self.image_processor_tester.get_expected_values(__lowerCAmelCase				)

             self.assertEqual(
                 encoded_images.shape						,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,     )

             # Test batched
             lowercase__     :						List[str]         =      image_processing(__lowerCAmelCase						,     return_tensors='pt'				).pixel_values

             lowercase__       , lowercase__     :						Optional[Any]         =      self.image_processor_tester.get_expected_values(__lowerCAmelCase						,     batched=__lowerCAmelCase				)

             self.assertEqual(
                 encoded_images.shape						,     (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     expected_height,
                     expected_width,
                 )						,     )


      def 						_UpperCAmelCase			(  self				)			->   Union[str, Any]:
             # Initialize image_processings
             lowercase__     :						Union[str, Any]         =      self.image_processing_class(**self.image_processor_dict				)
             lowercase__     :						int         =      self.image_processing_class(do_resize=__lowerCAmelCase						,     do_normalize=__lowerCAmelCase						,     do_rescale=__lowerCAmelCase				)
             # create random PyTorch tensors
             lowercase__     :						List[Any]         =      prepare_image_inputs(self.image_processor_tester						,     equal_resolution=__lowerCAmelCase						,     torchify=__lowerCAmelCase				)
             for image in image_inputs:
                    self.assertIsInstance(__lowerCAmelCase						,     torch.Tensor				)

             # Test whether the method "pad" and calling the image processor return the same tensors
             lowercase__     :						Union[str, Any]         =      image_processing_a.pad(__lowerCAmelCase						,     return_tensors='pt'				)
             lowercase__     :						str         =      image_processing_a(__lowerCAmelCase						,     return_tensors='pt'				)

             self.assertTrue(
                 torch.allclose(encoded_images_with_method['pixel_values']						,     encoded_images['pixel_values']						,     atol=1e-4				)				)


      @slow
      def 						_UpperCAmelCase			(  self				)			->   str:
             # prepare image and target
             lowercase__     :						Any         =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
             with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						,     'r'				) as f:
                    lowercase__     :						int         =      json.loads(f.read()				)

             lowercase__     :						Optional[int]         =      {'image_id': 3_9_7_6_9, 'annotations': target}

             # encode them
             lowercase__     :						Optional[Any]         =      YolosImageProcessor.from_pretrained('hustvl/yolos-small'				)
             lowercase__     :						str         =      image_processing(images=__lowerCAmelCase						,     annotations=__lowerCAmelCase						,     return_tensors='pt'				)

             # verify pixel values
             lowercase__     :						Any         =      torch.Size([1, 3, 8_0_0, 1_0_6_6]				)
             self.assertEqual(encoding['pixel_values'].shape						,     __lowerCAmelCase				)

             lowercase__     :						Optional[int]         =      torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,     __lowerCAmelCase						,     atol=1e-4				)				)

             # verify area
             lowercase__     :						int         =      torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,     __lowerCAmelCase				)				)
             # verify boxes
             lowercase__     :						List[str]         =      torch.Size([6, 4]				)
             self.assertEqual(encoding['labels'][0]['boxes'].shape						,     __lowerCAmelCase				)
             lowercase__     :						Any         =      torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,     __lowerCAmelCase						,     atol=1e-3				)				)
             # verify image_id
             lowercase__     :						str         =      torch.tensor([3_9_7_6_9]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,     __lowerCAmelCase				)				)
             # verify is_crowd
             lowercase__     :						str         =      torch.tensor([0, 0, 0, 0, 0, 0]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,     __lowerCAmelCase				)				)
             # verify class_labels
             lowercase__     :						str         =      torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,     __lowerCAmelCase				)				)
             # verify orig_size
             lowercase__     :						Any         =      torch.tensor([4_8_0, 6_4_0]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,     __lowerCAmelCase				)				)
             # verify size
             lowercase__     :						Any         =      torch.tensor([8_0_0, 1_0_6_6]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,     __lowerCAmelCase				)				)


      @slow
      def 						_UpperCAmelCase			(  self				)			->   List[Any]:
             # prepare image, target and masks_path
             lowercase__     :						str         =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
             with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'						,     'r'				) as f:
                    lowercase__     :						Tuple         =      json.loads(f.read()				)

             lowercase__     :						Optional[int]         =      {'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

             lowercase__     :						Tuple         =      pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'				)

             # encode them
             lowercase__     :						Tuple         =      YolosImageProcessor(format='coco_panoptic'				)
             lowercase__     :						List[str]         =      image_processing(images=__lowerCAmelCase						,     annotations=__lowerCAmelCase						,     masks_path=__lowerCAmelCase						,     return_tensors='pt'				)

             # verify pixel values
             lowercase__     :						Any         =      torch.Size([1, 3, 8_0_0, 1_0_6_6]				)
             self.assertEqual(encoding['pixel_values'].shape						,     __lowerCAmelCase				)

             lowercase__     :						Optional[Any]         =      torch.tensor([0.2_796, 0.3_138, 0.3_481]				)
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,     __lowerCAmelCase						,     atol=1e-4				)				)

             # verify area
             lowercase__     :						Any         =      torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,     __lowerCAmelCase				)				)
             # verify boxes
             lowercase__     :						Union[str, Any]         =      torch.Size([6, 4]				)
             self.assertEqual(encoding['labels'][0]['boxes'].shape						,     __lowerCAmelCase				)
             lowercase__     :						str         =      torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,     __lowerCAmelCase						,     atol=1e-3				)				)
             # verify image_id
             lowercase__     :						Union[str, Any]         =      torch.tensor([3_9_7_6_9]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,     __lowerCAmelCase				)				)
             # verify is_crowd
             lowercase__     :						Optional[int]         =      torch.tensor([0, 0, 0, 0, 0, 0]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,     __lowerCAmelCase				)				)
             # verify class_labels
             lowercase__     :						Union[str, Any]         =      torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,     __lowerCAmelCase				)				)
             # verify masks
             lowercase__     :						List[Any]         =      8_2_2_8_7_3
             self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,     __lowerCAmelCase				)
             # verify orig_size
             lowercase__     :						Tuple         =      torch.tensor([4_8_0, 6_4_0]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,     __lowerCAmelCase				)				)
             # verify size
             lowercase__     :						Optional[int]         =      torch.tensor([8_0_0, 1_0_6_6]				)
             self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,     __lowerCAmelCase				)				)


"""simple docstring"""


from __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase__			=    {"""UserAgent""": UserAgent().random}






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   script.contents[0]
  _UpperCAmelCase    			=   json.loads(data[data.find("""{\"config\""""		) : -1]		)
  return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]


class 							a					:


    def __init__(       self						:	Any ,					__lowerCAmelCase						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   f'''https://www.instagram.com/{username}/'''
      _UpperCAmelCase    			=   self.get_json()


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   requests.get(self.url ,					headers=__lowerCAmelCase       ).text
      _UpperCAmelCase    			=   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						:	Dict       ):
      return f'''{self.__class__.__name__}(\'{self.username}\')'''


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


    @property
    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      return self.user_data["username"]


    @property
    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      return self.user_data["full_name"]


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


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


    @property
    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      return self.user_data["external_url"]


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


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


    @property
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      return self.user_data["edge_owner_to_timeline_media"]["count"]


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


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


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






def      __UpperCAmelCase       (      lowercase = "github"		):







  """simple docstring"""
  import os

  if os.environ.get("""CI"""		):
    return  # test failing on GitHub Actions
  _UpperCAmelCase    			=   InstagramUser(lowercase		)
  assert instagram_user.user_data
  assert isinstance(instagram_user.user_data ,lowercase		)
  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()
    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 = }''')
