





'''simple docstring'''







from __future__ import annotations

_lowercase       						=			'Muhammad Umer Farooq'
_lowercase       						=			'MIT'
_lowercase       						=			'1.0.0'
_lowercase       						=			'Muhammad Umer Farooq'
_lowercase       						=			'contact@muhammadumerfarooq.me'
_lowercase       						=			'Alpha'

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

import requests
class 			_lowercase						(       __a					):







     def __init__( self    ,   A__    ) ->	None:
      super().__init__()
      snake_case				=			[]
      snake_case				=			domain







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	None:
      # Only parse the 'anchor' tag.
      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:
          snake_case				=			parse.urljoin(self.domain    ,   A__    )
          self.urls.append(A__    )




def    __UpperCamelCase	(	a    : str     ) ->str:
 return ".".join(get_sub_domain_name(a     ).split('''.'''     )[-2:]     )

def    __UpperCamelCase	(	a    : str     ) ->str:
 return parse.urlparse(a     ).netloc

def    __UpperCamelCase	(	a    : str = "https://github.com"     ) ->list[str]:
 snake_case				=			get_domain_name(a     )

 # Initialize the parser
 snake_case				=			Parser(a     )

 try:
  # Open URL
  snake_case				=			requests.get(a     )

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

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

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


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








'''simple docstring'''







import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image
else:
   class 			_lowercase						:







        @staticmethod
        def     UpperCamelCase			( *A__    ,   **A__    ) ->	List[Any]:
         pass

def    __UpperCamelCase	(	a    : Image     ) ->str:
 snake_case				=			hashlib.mda(image.tobytes()     )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_lowercase						(       unittest.TestCase					):
     _UpperCAmelCase										=					MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			DepthEstimationPipeline(model=A__    ,   image_processor=A__    )
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
      self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}    ,   A__    )
      import datasets

      snake_case				=			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''    ,   '''image'''    ,   split='''test'''    )
      snake_case				=			depth_estimator(
          [
              Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              # RGBA
              dataset[0]['''file'''],
              # LA
              dataset[1]['''file'''],
              # L
              dataset[2]['''file'''],
          ]    )
      self.assertEqual(
          [
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
              {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
          ]    ,   A__    ,   )







     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF'''    )
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      pass







     @slow
     @require_torch
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			'''Intel/dpt-large'''
      snake_case				=			pipeline('''depth-estimation'''    ,   model=A__    )
      snake_case				=			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
      snake_case				=			hashimage(outputs['''depth''']    )

      # This seems flaky.
      # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )    ,   2_9.3_0_4    )
      self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )    ,   2.6_6_2    )







     @require_torch
     def     UpperCamelCase			( self    ) ->	Any:
      # This is highly irregular to have no small tests.
      self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

