





import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    from transformers.models.bert.modeling_flax_bert import (
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
    )



class       A__     ( unittest.TestCase    ):

      '''simple docstring'''





      def __init__(				self ,							A_ ,							A_=13 ,							A_=7 ,							A_=True ,							A_=True ,							A_=True ,							A_=True ,							A_=99 ,							A_=32 ,							A_=5 ,							A_=4 ,							A_=37 ,							A_="gelu" ,							A_=0.1 ,							A_=0.1 ,							A_=512 ,							A_=16 ,							A_=2 ,							A_=0.02 ,							A_=4 ,							):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     parent
       UpperCamelCase			:      Optional[Any] 			=     batch_size
       UpperCamelCase			:      Union[str, Any] 			=     seq_length
       UpperCamelCase			:      Union[str, Any] 			=     is_training
       UpperCamelCase			:      int 			=     use_attention_mask
       UpperCamelCase			:      Any 			=     use_token_type_ids
       UpperCamelCase			:      Optional[Any] 			=     use_labels
       UpperCamelCase			:      Tuple 			=     vocab_size
       UpperCamelCase			:      Any 			=     hidden_size
       UpperCamelCase			:      List[str] 			=     num_hidden_layers
       UpperCamelCase			:      Dict 			=     num_attention_heads
       UpperCamelCase			:      Dict 			=     intermediate_size
       UpperCamelCase			:      Dict 			=     hidden_act
       UpperCamelCase			:      int 			=     hidden_dropout_prob
       UpperCamelCase			:      Optional[int] 			=     attention_probs_dropout_prob
       UpperCamelCase			:      Union[str, Any] 			=     max_position_embeddings
       UpperCamelCase			:      Union[str, Any] 			=     type_vocab_size
       UpperCamelCase			:      int 			=     type_sequence_label_size
       UpperCamelCase			:      Optional[int] 			=     initializer_range
       UpperCamelCase			:      Optional[int] 			=     num_choices





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




       UpperCamelCase			:      int 			=     ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size     )

       UpperCamelCase			:      Tuple 			=     None
       if self.use_attention_mask:
        UpperCamelCase			:      List[Any] 			=     random_attention_mask([self.batch_size, self.seq_length]     )

       UpperCamelCase			:      Any 			=     None
       if self.use_token_type_ids:
        UpperCamelCase			:      Tuple 			=     ids_tensor([self.batch_size, self.seq_length] ,							self.type_vocab_size     )

       UpperCamelCase			:      Dict 			=     BertConfig(
           vocab_size=self.vocab_size ,							hidden_size=self.hidden_size ,							num_hidden_layers=self.num_hidden_layers ,							num_attention_heads=self.num_attention_heads ,							intermediate_size=self.intermediate_size ,							hidden_act=self.hidden_act ,							hidden_dropout_prob=self.hidden_dropout_prob ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob ,							max_position_embeddings=self.max_position_embeddings ,							type_vocab_size=self.type_vocab_size ,							is_decoder=__SCREAMING_SNAKE_CASE ,							initializer_range=self.initializer_range ,							)

       return config, input_ids, token_type_ids, attention_mask





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




       UpperCamelCase			:      List[str] 			=     self.prepare_config_and_inputs()
       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Dict 			=     config_and_inputs
       UpperCamelCase			:      Union[str, Any] 			=     {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict





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




       UpperCamelCase			:      Tuple 			=     self.prepare_config_and_inputs()
       UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			,					UpperCamelCase			:      Union[str, Any] 			=     config_and_inputs

       UpperCamelCase			:      int 			=     True
       UpperCamelCase			:      str 			=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
       UpperCamelCase			:      int 			=     ids_tensor([self.batch_size, self.seq_length] ,							vocab_size=2     )

       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )



@require_flax
class       A__     ( __UpperCAmelCase ,     unittest.TestCase    ):

      '''simple docstring'''





      _UpperCAmelCase			:str							=     True

      _UpperCAmelCase			:Dict							=     (
          (
              FlaxBertModel,
              FlaxBertForPreTraining,
              FlaxBertForMaskedLM,
              FlaxBertForMultipleChoice,
              FlaxBertForQuestionAnswering,
              FlaxBertForNextSentencePrediction,
              FlaxBertForSequenceClassification,
              FlaxBertForTokenClassification,
              FlaxBertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )





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




       UpperCamelCase			:      Tuple 			=     FlaxBertModelTester(self     )





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




       UpperCamelCase			:      List[Any] 			=     FlaxBertModel.from_pretrained("bert-base-cased"     )
       UpperCamelCase			:      List[Any] 			=     model(np.ones((1, 1)     )     )
       self.assertIsNotNone(__SCREAMING_SNAKE_CASE     )







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)))
