






from __future__ import annotations

UpperCAmelCase__				   =		"Muhammad Umer Farooq"
UpperCAmelCase__				   =		"MIT"
UpperCAmelCase__				   =		"1.0.0"
UpperCAmelCase__				   =		"Muhammad Umer Farooq"
UpperCAmelCase__				   =		"contact@muhammadumerfarooq.me"
UpperCAmelCase__				   =		"Alpha"

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

import requests






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def __init__(      self      :						Union[str, Any] ,					__UpperCAmelCase      :						str  )    ->None:




       """simple docstring"""


       super().__init__()
       a		  =      []
       a		  =      domain







      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						list[tuple[str, str | None]]  )    ->None:




       """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:
           a		  =      parse.urljoin(self.domain ,					__UpperCAmelCase  )
           self.urls.append(__UpperCAmelCase  )
def 						_a    (      a :str )							->			str:
 return ".".join(get_sub_domain_name(a ).split('''.''' )[-2:] )
def 						_a    (      a :str )							->			str:
 return parse.urlparse(a ).netloc
def 						_a    (      a :str = "https://github.com" )							->			list[str]:
 a		  =      get_domain_name(a )

 # Initialize the parser
 a		  =      Parser(a )

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

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

  # Get links and loop through
  a		  =      set()
  for link in parser.urls:
   # open URL.
   # read = requests.get(link)
   try:
    a		  =      requests.get(a )
    # Get the valid email.
    a		  =      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__":
     UpperCAmelCase__				   =		emails_from_url("https://github.com")
     print(f"""{len(emails)} emails found:""")
     print("\n".join(sorted(emails)))









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case  :    Union[str, Any]						    =  {
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Union[str, Any]						    =  ['LayoutLMv3TokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    List[str]						    =  [
         'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
         'LayoutLMv3ForQuestionAnswering',
         'LayoutLMv3ForSequenceClassification',
         'LayoutLMv3ForTokenClassification',
         'LayoutLMv3Model',
         'LayoutLMv3PreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[Any]						    =  [
         'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFLayoutLMv3ForQuestionAnswering',
         'TFLayoutLMv3ForSequenceClassification',
         'TFLayoutLMv3ForTokenClassification',
         'TFLayoutLMv3Model',
         'TFLayoutLMv3PreTrainedModel',
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Tuple						    =  ['LayoutLMv3FeatureExtractor']
     _snake_case  :    str						    =  ['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
     from .configuration_layoutlmva import (
         LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
         LayoutLMvaConfig,
         LayoutLMvaOnnxConfig,
     )
     from .processing_layoutlmva import LayoutLMvaProcessor
     from .tokenization_layoutlmva import LayoutLMvaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_layoutlmva import (
              LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
              LayoutLMvaForQuestionAnswering,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaModel,
              LayoutLMvaPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_layoutlmva import (
              TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFLayoutLMvaForQuestionAnswering,
              TFLayoutLMvaForSequenceClassification,
              TFLayoutLMvaForTokenClassification,
              TFLayoutLMvaModel,
              TFLayoutLMvaPreTrainedModel,
          )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
          from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
     import sys

     _snake_case  :    Any						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

