


from __future__ import annotations

A			:						Optional[Any]        =  'Muhammad Umer Farooq'
A			:						str        =  'MIT'
A			:						Any        =  '1.0.0'
A			:						List[Any]        =  'Muhammad Umer Farooq'
A			:						Optional[Any]        =  'contact@muhammadumerfarooq.me'
A			:						Optional[Any]        =  'Alpha'

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

import requests



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Dict ,   _UpperCAmelCase       :		str						)		->  None:




							"""simple docstring"""


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


					def     lowerCamelCase__	(self       :		Optional[Any] ,   _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:
															lowercase__	     =						parse.urljoin(self.domain ,   _UpperCAmelCase						)
															self.urls.append(_UpperCAmelCase						)






def 					UpperCamelCase   (      __magic_name__	:				str	)  ->    str:
		"""simple docstring"""




		return ".".join(get_sub_domain_name(__magic_name__	).split("""."""	)[-2:]	)






def 					UpperCamelCase   (      __magic_name__	:				str	)  ->    str:
		"""simple docstring"""




		return parse.urlparse(__magic_name__	).netloc






def 					UpperCamelCase   (      __magic_name__	:				str = "https://github.com"	)  ->    list[str]:
		"""simple docstring"""




		lowercase__	     =						get_domain_name(__magic_name__	)

		# Initialize the parser
		lowercase__	     =						Parser(__magic_name__	)

		try:
				# Open URL
				lowercase__	     =						requests.get(__magic_name__	)

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

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

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


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





from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
	import datasets



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					@slow
					@require_torch
					def     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,   """prajjwal1/bert-tiny"""						)
							lowercase__	     =						BertTokenizer.from_pretrained("""bert-base-uncased"""						)

							lowercase__	     =						bertabert.config.encoder.vocab_size
							lowercase__	     =						tokenizer.sep_token_id
							lowercase__	     =						tokenizer.cls_token_id
							lowercase__	     =						128

							lowercase__	     =						datasets.load_dataset("""cnn_dailymail""" ,   """3.0.0""" ,   split="""train[:1%]"""						)
							lowercase__	     =						datasets.load_dataset("""cnn_dailymail""" ,   """3.0.0""" ,   split="""validation[:1%]"""						)

							lowercase__	     =						train_dataset.select(range(32						)						)
							lowercase__	     =						val_dataset.select(range(16						)						)

							lowercase__	     =						4

							def _map_to_encoder_decoder_inputs(_UpperCAmelCase       :		Any						):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									lowercase__	     =						tokenizer(batch["""article"""] ,   padding="""max_length""" ,   truncation=_UpperCAmelCase ,   max_length=512						)
									lowercase__	     =						tokenizer(batch["""highlights"""] ,   padding="""max_length""" ,   truncation=_UpperCAmelCase ,   max_length=128						)
									lowercase__	     =						inputs.input_ids
									lowercase__	     =						inputs.attention_mask

									lowercase__	     =						outputs.input_ids
									lowercase__	     =						outputs.input_ids.copy()
									lowercase__	     =						[
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
									]
									lowercase__	     =						outputs.attention_mask

									assert all(len(_UpperCAmelCase						) == 512 for x in inputs.input_ids						)
									assert all(len(_UpperCAmelCase						) == 128 for x in outputs.input_ids						)

									return batch

							def _compute_metrics(_UpperCAmelCase       :		List[Any]						):
									lowercase__	     =						pred.label_ids
									lowercase__	     =						pred.predictions

									# all unnecessary tokens are removed
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)

									lowercase__	     =						sum([int(pred_str[i] == label_str[i]						) for i in range(len(_UpperCAmelCase						)						)]						) / len(_UpperCAmelCase						)

									return {"accuracy": accuracy}

							# map train dataset
							lowercase__	     =						train_dataset.map(
							    _map_to_encoder_decoder_inputs ,   batched=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   remove_columns=["""article""", """highlights"""] ,   )
							train_dataset.set_format(
							    type="""torch""" ,   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,   )

							# same for validation dataset
							lowercase__	     =						val_dataset.map(
							    _map_to_encoder_decoder_inputs ,   batched=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   remove_columns=["""article""", """highlights"""] ,   )
							val_dataset.set_format(
							    type="""torch""" ,   columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,   )

							lowercase__	     =						self.get_auto_remove_tmp_dir()

							lowercase__	     =						SeqaSeqTrainingArguments(
							    output_dir=_UpperCAmelCase ,   per_device_train_batch_size=_UpperCAmelCase ,   per_device_eval_batch_size=_UpperCAmelCase ,   predict_with_generate=_UpperCAmelCase ,   evaluation_strategy="""steps""" ,   do_train=_UpperCAmelCase ,   do_eval=_UpperCAmelCase ,   warmup_steps=0 ,   eval_steps=2 ,   logging_steps=2 ,   )

							# instantiate trainer
							lowercase__	     =						SeqaSeqTrainer(
							    model=_UpperCAmelCase ,   args=_UpperCAmelCase ,   compute_metrics=_compute_metrics ,   train_dataset=_UpperCAmelCase ,   eval_dataset=_UpperCAmelCase ,   tokenizer=_UpperCAmelCase ,   )

							# start training
							trainer.train()

