





'''simple docstring'''




from __future__ import annotations

a__					:			List[str]          =     '''Muhammad Umer Farooq'''
a__					:			Optional[Any]          =     '''MIT'''
a__					:			Tuple          =     '''1.0.0'''
a__					:			str          =     '''Muhammad Umer Farooq'''
a__					:			Union[str, Any]          =     '''contact@muhammadumerfarooq.me'''
a__					:			Optional[int]          =     '''Alpha'''

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

import requests






class 				__snake_case     (					__magic_name__							):





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





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		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      ,       UpperCamelCase_		)
						self.urls.append(UpperCamelCase_		)





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




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->str:
	return parse.urlparse(UpperCAmelCase_							).netloc




def 						__lowerCamelCase			(	UpperCAmelCase_ = "https://github.com"							)   ->list[str]:
	snake_case__	       =   get_domain_name(UpperCAmelCase_							)

	# Initialize the parser
	snake_case__	       =   Parser(UpperCAmelCase_							)

	try:
		# Open URL
		snake_case__	       =   requests.get(UpperCAmelCase_							)

		# 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(UpperCAmelCase_							)
				# 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(UpperCAmelCase_							)
			except ValueError:
				pass
	except ValueError:
		raise SystemExit(1							)

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


if __name__ == "__main__":
			a__					:			Union[str, Any]          =     emails_from_url('''https://github.com''')
			print(f"""{len(emails)} emails found:""")
			print('''\n'''.join(sorted(emails)))










'''simple docstring'''




import unittest

import numpy as np

from transformers import RobertaConfig, 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.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)






class 				__snake_case     (					unittest.TestCase							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=13      ,       UpperCamelCase_=7      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=99      ,       UpperCamelCase_=32      ,       UpperCamelCase_=5      ,       UpperCamelCase_=4      ,       UpperCamelCase_=37      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=512      ,       UpperCamelCase_=16      ,       UpperCamelCase_=2      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=4      ,       )      ->		List[Any]:
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   seq_length
		snake_case__	       =   is_training
		snake_case__	       =   use_attention_mask
		snake_case__	       =   use_token_type_ids
		snake_case__	       =   use_labels
		snake_case__	       =   vocab_size
		snake_case__	       =   hidden_size
		snake_case__	       =   num_hidden_layers
		snake_case__	       =   num_attention_heads
		snake_case__	       =   intermediate_size
		snake_case__	       =   hidden_act
		snake_case__	       =   hidden_dropout_prob
		snake_case__	       =   attention_probs_dropout_prob
		snake_case__	       =   max_position_embeddings
		snake_case__	       =   type_vocab_size
		snake_case__	       =   type_sequence_label_size
		snake_case__	       =   initializer_range
		snake_case__	       =   num_choices





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size		)

		snake_case__	       =   None
		if self.use_attention_mask:
			snake_case__	       =   random_attention_mask([self.batch_size, self.seq_length]		)

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

		snake_case__	       =   RobertaConfig(
		    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=UpperCamelCase_      ,       initializer_range=self.initializer_range      ,       )

		return config, input_ids, token_type_ids, attention_mask





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs
		snake_case__	       =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
		return config, inputs_dict





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs

		snake_case__	       =   True
		snake_case__	       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       vocab_size=2		)

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






@require_flax
class 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  True

	__lowerCAmelCase       						=  (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   FlaxRobertaModelTester(self		)





	@slow
	def   _snake_case   (		self		)      ->		Tuple:
		for model_class_name in self.all_model_classes:
			snake_case__	       =   model_class_name.from_pretrained('roberta-base'      ,       from_pt=UpperCamelCase_		)
			snake_case__	       =   model(np.ones((1, 1)		)		)
			self.assertIsNotNone(UpperCamelCase_		)



