



"""simple docstring"""






from __future__ import annotations

_lowercase : Optional[int]      						=						"Muhammad Umer Farooq"
_lowercase : Any      						=						"MIT"
_lowercase : Union[str, Any]      						=						"1.0.0"
_lowercase : str      						=						"Muhammad Umer Farooq"
_lowercase : Optional[Any]      						=						"contact@muhammadumerfarooq.me"
_lowercase : int      						=						"Alpha"

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

import requests

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  str     )->					None:
								super().__init__()
								lowerCamelCase__		:		list[str]			=[]
								lowerCamelCase__		:		Dict			=domain




					def 		snake_case       (       self							:  int,  lowerCamelCase							:  str,  lowerCamelCase							:  list[tuple[str, str | None]]     )->					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:
																				lowerCamelCase__		:		Optional[Any]			=parse.urljoin(self.domain,  lowerCamelCase     )
																				self.urls.append(lowerCamelCase     )






def      snake_case__   (		__lowerCamelCase				:    str	):
			"""simple docstring"""
			return ".".join(get_sub_domain_name(__lowerCamelCase	).split('''.'''	)[-2:]	)






def      snake_case__   (		__lowerCamelCase				:    str	):
			"""simple docstring"""
			return parse.urlparse(__lowerCamelCase	).netloc






def      snake_case__   (		__lowerCamelCase				:    str = "https://github.com"	):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]			=get_domain_name(__lowerCamelCase	)

			# Initialize the parser
			lowerCamelCase__		:		Optional[int]			=Parser(__lowerCamelCase	)

			try:
						# Open URL
						lowerCamelCase__		:		Tuple			=requests.get(__lowerCamelCase	)

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

						# Get links and loop through
						lowerCamelCase__		:		List[str]			=set()
						for link in parser.urls:
									# open URL.
									# read = requests.get(link)
									try:
												lowerCamelCase__		:		Union[str, Any]			=requests.get(__lowerCamelCase	)
												# Get the valid email.
												lowerCamelCase__		:		List[str]			=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__":
		_lowercase : List[Any]      						=						emails_from_url("https://github.com")
		print(f'{len(emails)} emails found:')
		print("\n".join(sorted(emails)))









"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def      snake_case__   (		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			# Construct model
			if gpta_config_file == "":
						lowerCamelCase__		:		Dict			=GPTaConfig()
			else:
						lowerCamelCase__		:		Tuple			=GPTaConfig.from_json_file(__lowerCamelCase	)
			lowerCamelCase__		:		Optional[Any]			=GPTaModel(__lowerCamelCase	)

			# Load weights from numpy
			load_tf_weights_in_gpta(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			# Save pytorch-model
			lowerCamelCase__		:		List[str]			=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
			lowerCamelCase__		:		int			=pytorch_dump_folder_path + '''/''' + CONFIG_NAME
			print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
			torch.save(model.state_dict()							,		__lowerCamelCase	)
			print(f'''Save configuration file to {pytorch_config_dump_path}'''	)
			with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
		_lowercase : Optional[Any]      						=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--gpt2_config_file",
		    default="",
		    type=str,
		    help=(
		        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
		        "This specifies the model architecture."
		    ),
		)
		_lowercase : Any      						=						parser.parse_args()
		convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




