





import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)



class       A__     ( nn.Module    ):

      '''simple docstring'''





      def __init__(				self     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      Optional[Any] 			=     nn.Linear(3 ,							4     )
       UpperCamelCase			:      Dict 			=     nn.BatchNormad(4     )
       UpperCamelCase			:      List[str] 			=     nn.Linear(4 ,							5     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       return self.lineara(self.batchnorm(self.lineara(A_     )     )     )



class       A__     ( unittest.TestCase    ):

      '''simple docstring'''





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     ModelForTest()
       with TemporaryDirectory() as tmp_dir:
        offload_state_dict(A_ ,							model.state_dict()     )
        UpperCamelCase			:      Optional[Any] 			=     os.path.join(A_ ,							"index.json"     )
        self.assertTrue(os.path.isfile(A_     )     )
        # TODO: add tests on what is inside the index

        for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
         UpperCamelCase			:      Optional[int] 			=     os.path.join(A_ ,							F"""{key}.dat"""     )
         self.assertTrue(os.path.isfile(A_     )     )
         # TODO: add tests on the fact weights are properly loaded





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     [torch.floataa, torch.floataa, torch.bfloataa]

       for dtype in dtypes:
        UpperCamelCase			:      Any 			=     torch.randn(2 ,							3 ,							dtype=A_     )
        with TemporaryDirectory() as tmp_dir:
         UpperCamelCase			:      str 			=     offload_weight(A_ ,							"weight" ,							A_ ,							{}     )
         UpperCamelCase			:      Optional[Any] 			=     os.path.join(A_ ,							"weight.dat"     )
         self.assertTrue(os.path.isfile(A_     )     )
         self.assertDictEqual(A_ ,							{"weight": {"shape": [2, 3], "dtype": str(A_     ).split("."     )[1]}}     )

         UpperCamelCase			:      List[Any] 			=     load_offloaded_weight(A_ ,							index["weight"]     )
         self.assertTrue(torch.equal(A_ ,							A_     )     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     ModelForTest()
       UpperCamelCase			:      Optional[int] 			=     model.state_dict()
       UpperCamelCase			:      List[Any] 			=     {k: v for k, v in state_dict.items() if "linear2" not in k}
       UpperCamelCase			:      Optional[int] 			=     {k: v for k, v in state_dict.items() if "linear2" in k}

       with TemporaryDirectory() as tmp_dir:
        offload_state_dict(A_ ,							A_     )
        UpperCamelCase			:      Optional[int] 			=     OffloadedWeightsLoader(state_dict=A_ ,							save_folder=A_     )

        # Every key is there with the right value
        self.assertEqual(sorted(A_     ) ,							sorted(state_dict.keys()     )     )
        for key, param in state_dict.items():
         self.assertTrue(torch.allclose(A_ ,							weight_map[key]     )     )

       UpperCamelCase			:      str 			=     {k: v for k, v in state_dict.items() if "weight" in k}
       UpperCamelCase			:      List[str] 			=     {k: v for k, v in state_dict.items() if "weight" not in k}

       with TemporaryDirectory() as tmp_dir:
        offload_state_dict(A_ ,							A_     )
        UpperCamelCase			:      Any 			=     OffloadedWeightsLoader(state_dict=A_ ,							save_folder=A_     )

        # Every key is there with the right value
        self.assertEqual(sorted(A_     ) ,							sorted(state_dict.keys()     )     )
        for key, param in state_dict.items():
         self.assertTrue(torch.allclose(A_ ,							weight_map[key]     )     )

       with TemporaryDirectory() as tmp_dir:
        offload_state_dict(A_ ,							A_     )
        # Duplicates are removed
        UpperCamelCase			:      Dict 			=     OffloadedWeightsLoader(state_dict=A_ ,							save_folder=A_     )

        # Every key is there with the right value
        self.assertEqual(sorted(A_     ) ,							sorted(state_dict.keys()     )     )
        for key, param in state_dict.items():
         self.assertTrue(torch.allclose(A_ ,							weight_map[key]     )     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     {"a.1": 0, "a.10": 1, "a.2": 2}
       UpperCamelCase			:      int 			=     extract_submodules_state_dict(A_ ,							["a.1", "a.2"]     )
       self.assertDictEqual(A_ ,							{"a.1": 0, "a.2": 2}     )

       UpperCamelCase			:      Tuple 			=     {"a.1.a": 0, "a.10.a": 1, "a.2.a": 2}
       UpperCamelCase			:      Dict 			=     extract_submodules_state_dict(A_ ,							["a.1", "a.2"]     )
       self.assertDictEqual(A_ ,							{"a.1.a": 0, "a.2.a": 2}     )







from __future__ import annotations

__lowerCamelCase :  Optional[int]										=  """Muhammad Umer Farooq"""
__lowerCamelCase :  Tuple										=  """MIT"""
__lowerCamelCase :  Optional[int]										=  """1.0.0"""
__lowerCamelCase :  int										=  """Muhammad Umer Farooq"""
__lowerCamelCase :  Optional[int]										=  """contact@muhammadumerfarooq.me"""
__lowerCamelCase :  Dict										=  """Alpha"""

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

import requests



class       A__     ( __snake_case    ):





      def __init__(				self ,							A_     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      list[str] 			=     []
       UpperCamelCase			:      str 			=     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			:      Any 			=     parse.urljoin(self.domain ,							A_     )
           self.urls.append(A_     )



def 	A_       (  _lowerCAmelCase  )		->				str:
 return ".".join(get_sub_domain_name(_lowerCAmelCase  ).split("."  )[-2:]  )



def 	A_       (  _lowerCAmelCase  )		->				str:
 return parse.urlparse(_lowerCAmelCase  ).netloc



def 	A_       (  _lowerCAmelCase = "https://github.com"  )		->				list[str]:
 UpperCamelCase			:      int 			=     get_domain_name(_lowerCAmelCase  )

 # Initialize the parser
 UpperCamelCase			:      str 			=     Parser(_lowerCAmelCase  )

 try:
  # Open URL
  UpperCamelCase			:      int 			=     requests.get(_lowerCAmelCase  )

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

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