



'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_tensorflow_text_available():
						from transformers.models.bert import TFBertTokenizer


UpperCamelCase__:   Union[str, Any]								=			["bert-base-uncased", "bert-base-cased"]
UpperCamelCase__:   Optional[Any]								=			"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():

						class 				SCREAMING_SNAKE_CASE(							tf.keras.Model			):







							"""simple docstring"""






							def __init__(						self			:     Union[str, Any]		,			__snake_case			:     Tuple		)			->		Dict:
														super().__init__()
														UpperCAmelCase :       Union[str, Any] 							=		tokenizer
														UpperCAmelCase :       str 							=		AutoConfig.from_pretrained(__snake_case		)
														UpperCAmelCase :       Any 							=		TFAutoModel.from_config(__snake_case		)







							def      A	(						self			:     List[Any]		,			__snake_case			:     Optional[Any]		)			->		Optional[Any]:
														UpperCAmelCase :       List[Any] 							=		self.tokenizer(__snake_case		)
														UpperCAmelCase :       Optional[int] 							=		self.bert(**__snake_case		)
														return out["pooler_output"]

@require_tf
@require_tensorflow_text
class 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     int		)			->		Union[str, Any]:
								super().setUp()

								UpperCAmelCase :       Dict 							=		[
								    BertTokenizer.from_pretrained(__snake_case		) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
								]  # repeat for when fast_bert_tokenizer=false
								UpperCAmelCase :       str 							=		[TFBertTokenizer.from_pretrained(__snake_case		) for checkpoint in TOKENIZER_CHECKPOINTS] + [
								    TFBertTokenizer.from_pretrained(__snake_case		,			use_fast_bert_tokenizer=__snake_case		)
								    for checkpoint in TOKENIZER_CHECKPOINTS
								]
								assert len(self.tokenizers		) == len(self.tf_tokenizers		)

								UpperCAmelCase :       str 							=		[
								    '''This is a straightforward English test sentence.''',
								    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
								    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
								    '''And some much more rare Chinese: 齉 堃 齉堃''',
								    '''Je vais aussi écrire en français pour tester les accents''',
								    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
								]
								UpperCAmelCase :       Optional[Any] 							=		list(zip(self.test_sentences		,			self.test_sentences[::-1]		)		)







	def      A	(						self			:     Dict		)			->		str:
								for tokenizer, tf_tokenizer in zip(self.tokenizers		,			self.tf_tokenizers		):
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						UpperCAmelCase :       List[str] 							=		tokenizer(__snake_case		,			return_tensors='''tf'''		,			padding='''longest'''		)
																						UpperCAmelCase :       Dict 							=		tf_tokenizer(__snake_case		)

																						for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape		)		)
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]		,			tf.intaa		) == tf_outputs[key]		)		)







	@slow
	def      A	(						self			:     Union[str, Any]		)			->		Union[str, Any]:
								for tf_tokenizer in self.tf_tokenizers:
															UpperCAmelCase :       Optional[int] 							=		tf_tokenizer(self.paired_sentences		)
															UpperCAmelCase :       Optional[Any] 							=		tf_tokenizer(
															    text=[sentence[0] for sentence in self.paired_sentences]		,			text_pair=[sentence[1] for sentence in self.paired_sentences]		,			)
															for key in merged_outputs.keys():
																						self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]		,			tf.intaa		) == separated_outputs[key]		)		)







	@slow
	def      A	(						self			:     str		)			->		Any:
								for tf_tokenizer in self.tf_tokenizers:
															UpperCAmelCase :       str 							=		tf.function(__snake_case		)
															for test_inputs in (self.test_sentences, self.paired_sentences):
																						UpperCAmelCase :       int 							=		tf.constant(__snake_case		)
																						UpperCAmelCase :       Tuple 							=		compiled_tokenizer(__snake_case		)
																						UpperCAmelCase :       List[Any] 							=		tf_tokenizer(__snake_case		)

																						for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]		)		)







	@slow
	def      A	(						self			:     Optional[int]		)			->		int:
								for tf_tokenizer in self.tf_tokenizers:
															UpperCAmelCase :       Tuple 							=		ModelToSave(tokenizer=__snake_case		)
															UpperCAmelCase :       Any 							=		tf.convert_to_tensor(self.test_sentences		)
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		)  # Build model with some sample inputs
															with TemporaryDirectory() as tempdir:
																						UpperCAmelCase :       Optional[Any] 							=		Path(__snake_case		) / '''saved.model'''
																						model.save(__snake_case		)
																						UpperCAmelCase :       Tuple 							=		tf.keras.models.load_model(__snake_case		)
															UpperCAmelCase :       List[Any] 							=		loaded_model(__snake_case		)
															# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
															self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output		)		)		,			1E-5		)













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)))


