

from __future__ import annotations

SCREAMING_SNAKE_CASE_            = 'Muhammad Umer Farooq'
SCREAMING_SNAKE_CASE_            = 'MIT'
SCREAMING_SNAKE_CASE_            = '1.0.0'
SCREAMING_SNAKE_CASE_            = 'Muhammad Umer Farooq'
SCREAMING_SNAKE_CASE_            = 'contact@muhammadumerfarooq.me'
SCREAMING_SNAKE_CASE_            = 'Alpha'

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

import requests

class 			a					(		UpperCAmelCase			):






						"""simple docstring"""




						def __init__(    self  ,   A_		):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase    :						list[str]							=      []
										_UpperCAmelCase    :						Any							=      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    :						int							=      parse.urljoin(self.domain  ,   _SCREAMING_SNAKE_CASE		)
																										self.urls.append(_SCREAMING_SNAKE_CASE		)







def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				return ".".join(get_sub_domain_name(lowerCAmelCase	).split("."	)[-2:]	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					int	)				->    Optional[int]:
				return parse.urlparse(lowerCAmelCase	).netloc

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Dict = "https://github.com"	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      get_domain_name(lowerCAmelCase	)

				# Initialize the parser
				_UpperCAmelCase    :						Any							=      Parser(lowerCAmelCase	)

				try:
								# Open URL
								_UpperCAmelCase    :						List[str]							=      requests.get(lowerCAmelCase	)

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

								# Get links and loop through
								_UpperCAmelCase    :						int							=      set()
								for link in parser.urls:
												# open URL.
												# read = requests.get(link)
												try:
																_UpperCAmelCase    :						str							=      requests.get(lowerCAmelCase	)
																# Get the valid email.
																_UpperCAmelCase    :						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__":
					SCREAMING_SNAKE_CASE_            = emails_from_url('https://github.com')
					print(F'''{len(emails)} emails found:''')
					print('\n'.join(sorted(emails)))





import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = [
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_UpperCAmelCase    :						Optional[int]							=      k.replace(lowerCAmelCase					, lowerCAmelCase	)

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      [
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								_UpperCAmelCase    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								_UpperCAmelCase    :						Tuple							=      rename_state_dict_key(lowerCAmelCase	)
								if new_k not in valid_keys:
												failures.append([k, new_k]	)
								else:
												_UpperCAmelCase    :						Tuple							=      v
				if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
								rename_layernorm_keys(lowerCAmelCase	)
				m.model.load_state_dict(lowerCAmelCase					, strict=lowerCAmelCase	)
				m.half()
				m.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
					parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
					parser.add_argument(
					    '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


