





import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import VivitImageProcessor



class       A__     ( unittest.TestCase    ):

      '''simple docstring'''





      def __init__(				self ,							A_ ,							A_=7 ,							A_=3 ,							A_=10 ,							A_=18 ,							A_=30 ,							A_=400 ,							A_=True ,							A_=None ,							A_=True ,							A_=[0.5, 0.5, 0.5] ,							A_=[0.5, 0.5, 0.5] ,							A_=None ,							):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     size if size is not None else {"shortest_edge": 18}
       UpperCamelCase			:      Tuple 			=     crop_size if crop_size is not None else {"height": 18, "width": 18}

       UpperCamelCase			:      Optional[Any] 			=     parent
       UpperCamelCase			:      Optional[int] 			=     batch_size
       UpperCamelCase			:      List[Any] 			=     num_channels
       UpperCamelCase			:      Union[str, Any] 			=     num_frames
       UpperCamelCase			:      Any 			=     image_size
       UpperCamelCase			:      Tuple 			=     min_resolution
       UpperCamelCase			:      Optional[Any] 			=     max_resolution
       UpperCamelCase			:      Any 			=     do_resize
       UpperCamelCase			:      Tuple 			=     size
       UpperCamelCase			:      List[Any] 			=     do_normalize
       UpperCamelCase			:      Optional[int] 			=     image_mean
       UpperCamelCase			:      Any 			=     image_std
       UpperCamelCase			:      str 			=     crop_size





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }



@require_torch
@require_vision
class       A__     ( __snake_case ,     unittest.TestCase    ):

      '''simple docstring'''





      _UpperCAmelCase			:List[str]							=     VivitImageProcessor if is_vision_available() else None





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     VivitImageProcessingTester(self     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       self.assertTrue(hasattr(A_ ,							"image_mean"     )     )
       self.assertTrue(hasattr(A_ ,							"image_std"     )     )
       self.assertTrue(hasattr(A_ ,							"do_normalize"     )     )
       self.assertTrue(hasattr(A_ ,							"do_resize"     )     )
       self.assertTrue(hasattr(A_ ,							"do_center_crop"     )     )
       self.assertTrue(hasattr(A_ ,							"size"     )     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.image_processing_class.from_dict(self.image_processor_dict     )
       self.assertEqual(image_processor.size ,							{"shortest_edge": 18}     )
       self.assertEqual(image_processor.crop_size ,							{"height": 18, "width": 18}     )

       UpperCamelCase			:      Dict 			=     self.image_processing_class.from_dict(self.image_processor_dict ,							size=42 ,							crop_size=84     )
       self.assertEqual(image_processor.size ,							{"shortest_edge": 42}     )
       self.assertEqual(image_processor.crop_size ,							{"height": 84, "width": 84}     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PIL videos
       UpperCamelCase			:      Union[str, Any] 			=     prepare_video_inputs(self.image_processor_tester ,							equal_resolution=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							Image.Image     )

       # Test not batched input
       UpperCamelCase			:      Any 			=     image_processing(video_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      str 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random numpy tensors
       UpperCamelCase			:      str 			=     prepare_video_inputs(self.image_processor_tester ,							equal_resolution=A_ ,							numpify=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							np.ndarray     )

       # Test not batched input
       UpperCamelCase			:      Tuple 			=     image_processing(video_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      Any 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PyTorch tensors
       UpperCamelCase			:      Union[str, Any] 			=     prepare_video_inputs(self.image_processor_tester ,							equal_resolution=A_ ,							torchify=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							torch.Tensor     )

       # Test not batched input
       UpperCamelCase			:      Tuple 			=     image_processing(video_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      List[Any] 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)







from __future__ import annotations

__lowerCamelCase :  Optional[int]										=  """Muhammad Umer Farooq"""
__lowerCamelCase :  Tuple										=  """MIT"""
__lowerCamelCase :  Optional[int]										=  """1.0.0"""
__lowerCamelCase :  int										=  """Muhammad Umer Farooq"""
__lowerCamelCase :  Optional[int]										=  """contact@muhammadumerfarooq.me"""
__lowerCamelCase :  Dict										=  """Alpha"""

import re
from html.parser import HTMLParser
from urllib import parse

import requests



class       A__     ( __snake_case    ):





      def __init__(				self ,							A_     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      list[str] 			=     []
       UpperCamelCase			:      str 			=     domain





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       if tag == "a":
        # Check the list of defined attributes.
        for name, value in attrs:
         # If href is defined, and not empty nor # print it.
         if name == "href" and value != "#" and value != "":
          # If not already in urls.
          if value not in self.urls:
           UpperCamelCase			:      Any 			=     parse.urljoin(self.domain ,							A_     )
           self.urls.append(A_     )



def 	A_       (  _lowerCAmelCase  )		->				str:
 return ".".join(get_sub_domain_name(_lowerCAmelCase  ).split("."  )[-2:]  )



def 	A_       (  _lowerCAmelCase  )		->				str:
 return parse.urlparse(_lowerCAmelCase  ).netloc



def 	A_       (  _lowerCAmelCase = "https://github.com"  )		->				list[str]:
 UpperCamelCase			:      int 			=     get_domain_name(_lowerCAmelCase  )

 # Initialize the parser
 UpperCamelCase			:      str 			=     Parser(_lowerCAmelCase  )

 try:
  # Open URL
  UpperCamelCase			:      int 			=     requests.get(_lowerCAmelCase  )

  # pass the raw HTML to the parser to get links
  parser.feed(r.text  )

  # Get links and loop through
  UpperCamelCase			:      Optional[Any] 			=     set()
  for link in parser.urls:
   # open URL.
   # read = requests.get(link)
   try:
    UpperCamelCase			:      Optional[Any] 			=     requests.get(_lowerCAmelCase  )
    # Get the valid email.
    UpperCamelCase			:      Optional[int] 			=     re.findall("[a-zA-Z0-9]+@" + domain							,     read.text  )
    # If not in list then append it.
    for email in emails:
     valid_emails.add(_lowerCAmelCase  )
   except ValueError:
    pass
 except ValueError:
  raise SystemExit(1  )

 # Finally return a sorted list of email addresses with no duplicates.
 return sorted(_lowerCAmelCase  )


if __name__ == "__main__":
    __lowerCamelCase :  Tuple										=  emails_from_url("""https://github.com""")
    print(f"""{len(emails)} emails found:""")
    print("""\n""".join(sorted(emails)))
