
from __future__ import annotations

lowercase  :					Tuple						    =							"""Muhammad Umer Farooq"""
lowercase  :					List[str]						    =							"""MIT"""
lowercase  :					Any						    =							"""1.0.0"""
lowercase  :					str						    =							"""Muhammad Umer Farooq"""
lowercase  :					List[str]						    =							"""contact@muhammadumerfarooq.me"""
lowercase  :					Union[str, Any]						    =							"""Alpha"""

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

import requests

class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,snake_case      ):



           '''simple docstring'''







           super().__init__()
           lowercase							:     list[str]       	=      []
           lowercase							:     Union[str, Any]       	=      domain
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''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:
                                       lowercase							:     Tuple       	=      parse.urljoin(self.domain		,snake_case      )
                                       self.urls.append(snake_case      )







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







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    str:
       return parse.urlparse(SCREAMING_SNAKE_CASE__				).netloc







def 						_snake_case(  SCREAMING_SNAKE_CASE__ = "https://github.com"				) ->    list[str]:
       lowercase							:     List[Any]       	=      get_domain_name(SCREAMING_SNAKE_CASE__				)

       # Initialize the parser
       lowercase							:     List[str]       	=      Parser(SCREAMING_SNAKE_CASE__				)

       try:
              # Open URL
              lowercase							:     Tuple       	=      requests.get(SCREAMING_SNAKE_CASE__				)

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

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

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


if __name__ == "__main__":
  lowercase  :					Optional[Any]						    =							emails_from_url("""https://github.com""")
  print(F'''{len(emails)} emails found:''')
  print("""\n""".join(sorted(emails)))


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_tf_available():
  import tensorflow as tf

  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  from ..tf_utils import stable_softmax

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowercase  :					Any						    =							logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           super().__init__(*snake_case		,**snake_case      )
           requires_backends(self		,"""vision"""      )
           self.check_model_type(
               TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
               if self.framework == """tf"""
               else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      {}
           if top_k is not None:
                  lowercase							:     int       	=      top_k
           return {}, {}, postprocess_params
    def __call__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      load_image(snake_case      )
           lowercase							:     List[Any]       	=      self.image_processor(images=snake_case		,return_tensors=self.framework      )
           return model_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.model(**snake_case      )
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=5      ):



           '''simple docstring'''







           if top_k > self.model.config.num_labels:
                  lowercase							:     Tuple       	=      self.model.config.num_labels

           if self.framework == "pt":
                  lowercase							:     str       	=      model_outputs.logits.softmax(-1      )[0]
                  lowercase	,			lowercase							:     Dict       	=      probs.topk(snake_case      )
           elif self.framework == "tf":
                  lowercase							:     Optional[int]       	=      stable_softmax(model_outputs.logits		,axis=-1      )[0]
                  lowercase							:     Union[str, Any]       	=      tf.math.top_k(snake_case		,k=snake_case      )
                  lowercase	,			lowercase							:     List[str]       	=      topk.values.numpy(), topk.indices.numpy()
           else:
                  raise ValueError(f"Unsupported framework: {self.framework}"      )

           lowercase							:     Tuple       	=      scores.tolist()
           lowercase							:     Dict       	=      ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case		,snake_case      )]
